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Open Access

A state-of-the-art survey of malware detection approaches using data mining techniques

Human-centric Computing and Information Sciences20188:3

https://doi.org/10.1186/s13673-018-0125-x

Received: 20 July 2017

Accepted: 2 January 2018

Published: 12 January 2018

Abstract

Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

Keywords

Data miningMalware detectionClassificationBehavior-basedSignature-based

Introduction

In the recent years, the application of malware detection mechanisms utilize through data mining techniques through have increased using machine learning to recognize malicious files [1, 2]. Machine learning methods can take in hidden examples from a given preparing set which includes both malware and benign examples. These basic examples can separate malware from benevolent code [3, 4]. Malware is a standout most thoughtful intimidations for distributed systems and the Internet [5]. The battle between security analyzers and malware scholars is everlasting as innovation grows. Malware is a program that makes your framework accomplish something that an assailant needs it to do [6]. The most generally utilized malware detection develops a straightforward example coordinating way to deal with identify vindictive code. Typically malware designers don’t compose new code without any preparation, yet redesign the old code with new components or muddling strategies [7]. With a large number of malware cases seeming each day, proficiently preparing countless specimens which display comparable conduct, has turned out to be progressively essential [8].

Up to now, malware analysis [9, 10] have the high growing impact in the procedure of deciding the reason and the usefulness the conduct of a given suspicious application. Such a procedure is an important essential with a specific end goal to create effective and powerful identification furthermore characterization techniques; malware analysis is partitioned into two primary classifications that include dynamic and static methods [11, 12]. To the best of our knowledge, the most data mining methods have some benefits and weaknesses in malware detection subject [13]. In addition, having a new literature review can be influenced on the research studies and explore some technical details in malware detection using data mining techniques. Of course, some research [1317] had discussed the malware detection approaches. There are some defects in the surveyed research. Some papers are published in out of date and did not considered new articles in comparison and analysis. In addition, some surveys have not any systematic classification and article selection for their researches. For example, Muazzam Siddiqui et al. [18] presented a survey of malware detection using data mining techniques. Some defects of the survey are as follow: this survey used old research in literature analysis. In addition, they did not any systematic review for article selection in their research. This research did not specified an appropriate categorization for malware detection techniques. Just, they analyzed the scanning and data analysis methods in the proposed research.

To overcome some defects, this paper presents a systematic literature review on the new recent malware detection techniques using data mining approaches. This review classifies the malware detection approaches in two main fields: signature-based and behavior-based. The contributions of this paper are as follows:
  • Providing a summary of the current challenges related to malware detection approaches in data mining.

  • Presenting a systematic and categorized overview of the current approaches to machine learning mechanisms in the data mining topics.

  • Exploring a structure of the important methods that are significant in malware detection approach.

  • Discussing the important factors of classification malware approaches in the data mining to improve their problems in the futures.

The rest of this paper organized as follows. “Malware detection approaches”, overviews the malware detection mechanisms in data mining methods and classifies them with a technical taxonomy. “Review of the malware detection approaches” presents an analytical comparison of the proposed approaches for selected studies. In “Discussion”, a discussion about the malware detection issues is shown that have not been analyzed comprehensively up to now as an exploration of new challenges. Finally, “Conclusion” displays the conclusion.

Malware detection approaches

As a result of the developing malware in the innovation, the information of obscure malware protection is a fundamental subject in the malware recognition as per the machine learning strategies [19]. The machine learning strategies are divided into supervised and unsupervised classes. Malware detection approaches are divided into two main categories that include behavior-based and signature-based methods [20]. Also, there are two static and dynamic [21] malware analysis that generally performed in finding malicious applications [22].

In Fig. 1, we illustrate a malware detection taxonomy based on machine learning approaches. According to this figure, the API calls features, assembly features, and binary features are existing approaches for malware detection method. These features use machine learning methods for predicting and detecting malicious files.
Figure 1
Fig. 1

Taxonomy of malware detection approaches

Signature-based malware detection

Recently, signature-based detection is the most generally utilized procedure in antivirus programming highlighting exact correlation. Malware recognition has essentially centered on performing static investigations to review the code-structure mark of infections, instead of element behavioral methods [23]. The signature-based system finds interruptions utilizing a predefined list of known assaults. Despite the fact that this arrangement has the ability to identify malware in the versatile application, it requires steady overhauling of the predefined signature database. Moreover, it is less effective in identifying noxious exercises utilizing the signature-based technique because of the quickly changing nature of portable malware [24, 25]. Signature-based strategies depend in light of exceptional crude byte examples or standard articulations, known as marks, made to coordinate the noxious document. For example, static highlights of a record are utilized to decide if it is a malware. The main advantage of signature-based techniques is their thoroughness since they follow all conceivable execution ways of a given document.

In inside of the malware structure, existing malicious objects have characteristics that can be used to generate a unique digital signature. The anti-malware provider utilizes the meta-heuristic algorithms that can scan efficiently the malicious object to control its signature [26]. After identifying the malicious object, the detected signature is added to the existing database as the recognized malware. The database sources include huge number of the various signatures that classify malicious objects. In the signature-based malware detection, there are some various qualities including fast identification, easy to run, and broadly accessible [27].

Since the digital signature plans are gotten from known malware, these plans are likewise generally known. Subsequently they can be effectively evaded by programmers utilizing straightforward confusion procedures. Hence malware code can be modified and signature-based identification can be sidestepped. Since anti-malware providers are built on the premise of known malware, they can’t to distinguish obscure malware, or even variations of known malware. In this way, without exact digital signature, they can’t adequately distinguish polymorphic malware. Along these lines, signature-based recognition does not give zero-day insurance. Besides, since a signature-based indicator utilizes an isolate signature for each malware variation, the database of signatures develops at an exponential rate [28]. The signature-based malware detection has two main methods for applying malware detection approach in machine learning methods including assembly features and binary features. Figure 2 illustrates a standard signature-based malware detection framework using data mining approaches.
Figure 2
Fig. 2

The signature-based malware detection framework

Also, Table 1 shows the advantages and weaknesses of the signature-based malware detection approach.
Table 1

The advantages and weaknesses of the signature-based detection

Advantage

Weakness

Easy to run

Failing to detect the polymorphic malwares

Fast identification

Replicating information in the huge database

Broadly accessible

 

Finding comprehensive malware information

 

Behavior-based malware detection

This subsection illustrates the behavior-based approaches in malware detection. In addition, it reviews the selected behavior-based approaches in the data mining. Finally, the discussed behavior-based approaches compared and summarized in the last subsection. Behavior-based methodologies require execution of a given example in a sandboxed situation and run-time exercises are checked and logged. Dynamic investigation systems utilize both virtualization and imitating conditions to execute a malware and to remove its practices. The primary advantage of the behavior-based approach is that gives a superior comprehension of how malware is produced and implemented [8, 14].

In the behavior-based malware approach, the suspicious objects are assessed based on their activities that they cannot execute in system. Efforts to achieve activities that are clearly irregular or unofficial would specify the suspicious object is malicious, or at least apprehensive. A malicious behavior is known using a dynamic analysis that evaluates malicious intent by the object’s code and structure. In the behavior-based detection, the API calls and assembly features are two main methods for applying machine learning algorithms. Figure 3 depicts a standard behavior-based malware detection approach using data mining algorithms.
Figure 3
Fig. 3

The behavior-based malware detection framework

Table 2 shows the advantages and weaknesses of the behavior-based malware detection approach.
Table 2

The advantages and weaknesses of the behavior-based detection

Advantage

Weakness

Detecting unconceived types of malware attacks

Storage complexity for behavioral patterns

Data-flow dependency detector

Time complexity

Detecting the polymorphic malwares

 

After describing the existing malware detection approaches, next section presents the technical analysis of the current research studies in the malware detection with data mining algorithms.

Review of the malware detection approaches

In this section, the existing malware detection approaches are analyzed according to some evaluation factors such as the main idea, advantages and disadvantages, algorithm type and assessment type in data mining techniques. We analyze the selected studies according to existing approaches and discuss on them.

Review of the signature-based approaches

Wu et al. [29] have utilized an artificial immune-based smartphone malware detection model (SP-MDM) both static malware examination and element malware investigation as indicated by the component of the biologic resistant framework that can shield us from disease by creatures. In this model, the static marks and dynamic marks of malware are separated, and in view of the genuine esteemed vector encoding, the antigens are produced. The youthful identifier develops into a develop one on the off chance that it experiences self-resistance. Finder posterity with higher fondness is made after the streamlining of developing identifiers utilizing clonal determination calculation. Also, they collected twenty malware and twenty benign files as testing samples set.

Bat-Erdene et al. [30] presented a strategy for characterizing the packing algorithms of given unknown packed executable. To begin with, they measured the entropy estimations of a given executable and change over the entropy estimations of a specific area of memory into typical representations. Their presented strategy utilized symbolic aggregate approximation (SAX), which is known to be viable for huge information changes. Second, we order the conveyance of images utilizing managed learning order strategies, i.e., credulous Bayes and bolster vector machines for recognizing pressing calculations. The aftereffects of our examinations including a gathering of 324 pressed kindhearted projects and 326 stuffed malware programs with 19 pressing calculations illustrate that our strategy can distinguish pressing calculations of given executable with a high precision of 95.35%, a review of 95.83%, and an accuracy of 94.13%. We propose four likeness estimations for distinguishing pressing calculations based on SAX representations of the entropy values and an incremental total examination. Among these four measurements, the loyalty closeness estimation shows the best-matching result, i.e., a rate of precision running from 95.0 to 99.9%, which is from 2 to 13 higher than that of the other three measurements. Our review affirms that pressing calculations can be recognized through an entropy examination in view of a measure of the instability of the running procedures and without earlier information of the executable.

Cui et al. [31] illustrated a novel recognition framework in light of cloud environment and packet examination. The framework identifies the malicious mobile malware behavior through their bundles with the utilization of information mining strategies. This approach totally keeps away from the deformities of customary techniques. The framework is administration arranged and can be sent by portable administrators to send cautions to clients who have malware on their gadgets. To enhance framework execution, another bunching technique called withdrawal grouping was made. This technique utilizes earlier learning to lessen dataset measure. In addition, a multi-module location plan was acquainted with improve framework precision. The aftereffects of this plan are created by incorporating the location consequences of a few calculations, including Naive Bayes and Decision Tree.

Fan et al. [32] proposed a compelling arrangement mining calculation to find vindictive quintal examples, and afterward, All-Nearest-Neighbor (ANN) classifier is constructed for malicious position in the established samples. The created information mining structure made out of the proposed consecutive example mining technique and ANN classifier can well describe the malevolent examples from the gathered record test set to adequately distinguish recently concealed malware tests. A thorough exploratory review on a genuine information accumulation is performed to assess our recognition structure. The promising test comes about demonstrate that their structure beats other to exchange information mining based discovery techniques in distinguishing new vindictive executable.

Hellal and Ben Romdhane [33] displayed another diagram mining technique to recognize variations of malware utilizing static examination while covering the current defects. Also, they proposed a novel calculation, called minimal contrast frequent sub-graph miner method (MCFSM), for separating negligible discriminative and generally utilized malevolent behavioral designs which can distinguish definitely a whole group of vindictive projects, conversely to another arrangement of benevolent projects. The proposed technique demonstrates high recognition rates and low false positive rates and creates a predetermined number of behavioral malware marks.

Martín et al. [34] illustrated outsider calls to sidestep the impacts of these disguise methodologies since they can’t be obfuscated. We join bunching and multi-target advancement to produce a classifier in view of particular practices characterized by outsider call bunches. The analyzer guarantees that these gatherings are identified with noxious or favorable practices cleaning any non-discriminative example. This device, named MOCDroid,1 accomplishes a precision of 95.15% in test with 1.69% of false positives with genuine applications extricated from the wild, overcoming all business antivirus motors from VirusTotal.

Santos et al. [35] proposed another strategy to identify obscure malware families. This model depends on the recurrence of the presence of opcode groupings. Moreover, they depicted a system to mine the importance of each opcode and evaluate the recurrence of each opcode grouping. Furthermore, they provided experimental approval that this new strategy is fit for recognizing obscure malware.

Wang and Wang [24] presented a malware recognition framework to ensure a little order mistake by machine learning using the speculation capacity of support vector models (SVMs). This review built up a programmed malware location framework via preparing a SVM classifier in light of behavioral marks. Over approval, plan was utilized for taking care of grouping exactness issues by utilizing SVMs connected with 60 groups of genuine malware. The trial comes about uncover that the characterization blunder diminishes as the measuring of testing information is expanded. For various estimating (N) of malware tests, the expectation precision of malware discovery runs up to 98.7% with N = 100. The general recognition precision of the SVC is more than 85% for unspecific versatile malware.

Summary of the reviewed signature-based approaches

According to the discussed and reviewed signature-based detection approaches, the comparison of the proposed articles is demonstrated in Table 3 which shows the used case study in research, the main advantages, disadvantages and target environment for the existing studies. The main advantage of signature-based detection approaches is using pattern detection that decreases the system overhead and execution time for malware prediction. The main disadvantage of the signature-based detection approaches is omitting feature selection. The target environment is categorized into three main platforms including embedded systems, Windows-based and smartphones. The most research studies in the signature-based detection have used the Windows-based environment for representing the proposed malware detection approach.
Table 3

A side-by-side comparison of the reviewed signature-based articles

Method

Main idea

Advantages

Disadvantages

Target environment

PMD

Polymorphic Malware Detection (PMD) [25]

Low cost

High accuracy

Increasing total feature selection

Windows-based

SigPID

Significant permission identification android malware detection (SigPID) [19]

Low cost

High accuracy

Low scanning

Smartphone

OpCode

Graph malware detection [3]

Low complexity

Low cost

Low timely

High robustness

Embedded systems

Droid

Droid malware detection [11]

Fast feature selection

High complexity

Smartphone

APMD

API malware detection (APMD) [23]

Low monitoring overhead

High accuracy

High cost

Windows-based

SVDD

N-grams malware detection [20]

High detection accuracy

Did not analyzing feature selection

Windows-based

SMD

Smartphone malware detection (SMD) [29]

Combining static malware analysis and dynamic malware analysis

Presenting novel the clone and the mutation mechanism

Did not comparing with other classification approaches

Low accuracy

Smartphone

SAAM

Symbolic aggregate approximation for malwares (SAAM) [30]

Best packet classification

High accuracy

Presenting a data transformation method to reduce the space complexity

Did not examine the multiple packing algorithms.

Windows-based

SOMM

Service-Oriented mobile malware detection (SoMM) [31]

High detection accuracy

High scaling

High traffic

Did not analyzing behavior of malwares

Smartphone

SPM

Sequential pattern mining (SMP) [32]

High accuracy

Low overhead

Did not analyzing feature selection

Windows-based

FPM

Frequent pattern mining (FPM) [33]

Presenting automatic train approach

Not analysis discriminative frequent behavior patterns

High overhead

Windows-based

MOED

Multi-objective evolutionary detection (MOED) [34]

High speed detection

High accuracy

Low overhead

Using traditional detection engines

Smartphone

Opcode

Opcode sequences [35]

Prefect detection ratio of unknown malware

Did not analyze instance selection

Smartphone

MobA

Mobile android [24]

Good attribute selection

Low overhead

High complexity

Did not analysis countermeasures

Smartphone

SHMD

Signature and Heuristic-based malware detection [36]

Low overhead

Best binary feature selection

High time complexity

High cost

Smartphone

MKLDroid

A multi-view context-aware approach to Android malware detection [15]

High efficiency

Run time detection

High complexity

Did not analyzing feature selection

Smartphone

DBScan

Hybrid pattern based text mining approach [17]

Low overhead

High time

Low scalability

Windows-based

DroidNative

Android malware detector with control flow patterns [37]

Low time

High efficiency

Low scalability

High cost

Smartphone

BAM

Hybrid malware detection with binary associative memory [13]

High efficiency

High complexity

Windows-based

In addition, Table 4 depicts a side-by-side comparison of the signature-based detection factors in each article. These factors include case-study method, classification or clustering approach, data analysis method, and data set type and accuracy factor.
Table 4

A side-by-side comparison of the important factors in the signature-based detection of each article

Case study

Classification approach

Data analysis method

Used dataset

Total dataset

Accuracy

%

Polymorphic Malware Detection [25]

K-means

Dynamic

ClamAV, VirusTotal,

2876

99

Android malware detection [19]

SVM

Dynamic

Google play store

5494

94

Graph malware detection [3]

Graph-SVM

Dynamic

Windows DLL calls

6671

88

Droid malware detection [11]

SVM

Dynamic

Windows API library

7000

98

API malware detection [23]

Naive Bayes and Decision Tree—SVM

Dynamic

Google play store

7000

95

N-grams malware detection [20]

SVM

Dynamic

Google play store

658

97

Smartphone malware detection [29]

K-means—artificial immune system

Hybrid

Android malware database XVNA

1300

89.8

Symbolic aggregate approximation for malwares [30]

Naive Bayes and SVM

Dynamic

Offensive computing and VX heavens library

8100

95.83

Service-Oriented mobile malware detection [31]

Naive Bayes and Decision Tree

Hybrid

Key Laboratory of Network Security, Fujian Normal University

3000

97.3

Sequential pattern mining [32]

All-Nearest-Neighbor, KNN, SVM J48

Hybrid

VXHeaven website

3200

95.2

Frequent pattern mining [33]

Minimal contrast frequent subgraphs

Static

Several websites

2083

92

Multi-objective evolutionary detection [34]

Multi-objective evolutionary by GA

Static

Viruseshair and VirusTotal websites

9383

95.15

Opcode sequences [35]

K-nearest neighbors and SVM

Hybrid

VxHeavens website

2000

92.9

Mobile android [24]

SVM

Hybrid

Contagio Blogger and VirusTotal Web sites

2500

98.7

Signature and Heuristic-based Malware Detection [36]

SVM, J48, KNN, Decision tree and Random tree

Hybrid

M0DROID website

500

99.81

A multi-view [15] context-aware approach to Android malware detection

Multiple Kernel Learning, SVM

Static

Google Play, AndroidDrawer, FDroid

6056

98.05

Hybrid pattern based text mining approach [17]

ANN, malicious sequential pattern based malware detection

Hybrid

Viruseshair and VirusTotal websites

8000

98.89

Android malware detector with control flow patterns [37]

Droid, CFGO-IL

Static

Several websites

3158

93.57

Hybrid malware detection with binary associative memory [13]

MLP, SVM, Naïve Bayes, J48

Hybrid

VX Heaven website

52,183

98.6

Review of the selected behavior-based approaches

Altaher [38] proposed an evolving hybrid neuro-fuzzy classifier (EHNFC) for Android-based malware grouping utilizing consent based components. The proposed EHNFC not just has the capacity of distinguishing obscured malware utilizing fluffy tenets, yet can likewise advance its structure by adopting new malware recognition fluffy tenets to enhance its discovery exactness when utilized as a part of the location of more malware applications. To this end, a developing bunching technique for adjusting and advancing malware location fluffy tenets was changed to consolidate a versatile methodology for overhauling the radii and focuses of grouped authorization based components. This adjustment to the advancing bunching strategy improves group merging also, produces decides that are better custom-made to the input information, henceforth enhancing the characterization precision of the proposed EHNFC. The exploratory outcomes for the proposed EHNFC demonstrate that the proposition outflanks a few cutting-edge jumbled malware order approaches as far as a false negative rate (0.05) and false positive rate (0.05). The outcomes likewise show that the proposition identifies the Android malware superior to other neuro-fuzzy frameworks as far as precision (90%).

Mohaisen et al. [39] proposed, a computerized and conduct based malware examination and marking framework called AMAL that addresses shortcomings of the current frameworks. AMAL comprises of two sub-frameworks, AutoMal and MaLabel. AutoMal gives instruments to gather low granularity behavioral curios that portray malware utilization of the document framework, memory, organize, what’s more, registry, and does that by running malware tests in virtualized situations. On the other hand, MaLabel utilizes those ancient rarities to make delegate highlights, utilize them for building classifiers prepared by physically screened preparing tests, and utilize those classifiers to characterize malware tests into families comparable in conduct. AutoMal additionally empowers unsupervised learning, by executing various bunching calculations for tests gathering. An assessment of both AutoMal and MaLabel in view of medium-scale (4000 specimens) and expansive scale datasets (more than 115,000 samples) collected and broke down via AutoMal shows AMAL’s adequacy in precisely describing, ordering, and gathering malware tests. MaLabel accomplishes an exactness of 99.5% and review of 99.6% to confident relations demand, and more than 98% of accuracy and evaluation for unsupervised classification.

Yuan et al. [40] presented a deep learning method to connect the components from the static investigation with elements from the dynamic investigation of Android applications. In addition, they actualized an Android malware detection engine based on the deep-learning method (DroidDetector) that can consequently distinguish whether a file has a malicious behavior or not. With a large number of Android applications, they tested DroidDetector and play out an in-depth examination of the elements that deep learning basically adventures to portray malware completely. The outcomes appear that deep learning is appropriate for characterizing Android malware and particularly compelling with the accessibility of additional preparation information. DroidDetector can accomplish 96.76% detection accuracy, which traditional machine learning methods.

Boukhtouta et al. [41] presented the issue of fingerprinting perniciousness of activity with the end goal of recognition and arrangement. This research pointed first at fingerprinting perniciousness by utilizing two approaches: Deep Packet Inspection (DPI) and IP bundle headers arrangement. To this end, we consider malignant activity created from element malware examination as movement perniciousness ground truth. In light of this supposition, they exhibited how these two methodologies are utilized to recognize what’s more, attribute maliciousness to the various threat. In this work, we concentrate the positive and negative angles for Deep Packet Review and IP bundle headers order. They assessed every approach in view of its recognition and attribution precision and additionally their level of multifaceted nature. The results of both methodologies have demonstrated promising outcomes as far as discovery; they are great possibility to constitute a collaboration to expand or prove recognition frameworks as far as runtime speed and grouping exactness.

Ding et al. [42] proposed an affiliation mining strategy based on API calls to recognize malware. To expand the identification speed of the Objective-Oriented association (OOA) mining, distinctive methodologies are exhibited: to enhance the govern quality, criteria for API determination are proposed to expel APIs that can’t get to distinctly visit things; to discover affiliation decides that have solid segregation control, we characterize the manage utility to assess the affiliation runs; and to enhance the location exactness, a characterization strategy in view of numerous affiliation guidelines is embraced. The trials demonstrate that the proposed systems can essentially enhance the running velocity of OOA. In our investigations, the time cost for information mining is decreased by 32%, and the time cost for arrangement is decreased by 50%.

Eskandari et al. [43] presented a novel hybrid approach, HDM-Analyzer, is displayed which takes points of interest of dynamic and static investigation techniques for rising pace while protecting the precision at a sensible level. HDM-Analyzer can foresee the dominant part of basic leadership focuses on using the factual data which is assembled by element investigation; along these lines, they have no any performance overhead. The fundamental commitment of this paper is taking exactness preferred standpoint of the element investigation and consolidating it into static examination keeping in mind the end goal to enlarge the precision of static investigation. Truth be told, the execution overhead has been endured in learning stage; hence, it does not force on highlight extraction stage which is performed in examining operation. The exploratory outcomes illustrate that HDM-Analyzer accomplishes better general exactness and time many-sided quality than static and element investigation strategies.

Miao et al. [44] presented a bilayer conduct reflection strategy in light of the semantic examination of dynamic API sequences. Operations on touchy framework assets and complex practices are disconnected in an interpretable way at various semantic layers. At the lower layer, crude API calls are joined to extract low-layer practices by means of information reliance investigation. At the higher layer, low-layer practices are further joined to build more intricate high-layer practices with great interpretability. The separated low-layer furthermore, high-layer practices are at last inserted into a high dimensional vector space. Henceforth, the disconnected practices can be specifically utilized by numerous prominent machine learning calculations. In addition, to handle the issue that considerate projects are not satisfactorily examined or malware and amiable projects are seriously imbalanced, an enhanced one-class bolster vector machine (OC-SVM) named OC-SVM-Neg is proposed which makes utilization of the accessible negative examples. The trial comes about demonstrate that the proposed include extraction technique with OC-SVM-Neg beats double classifiers on the false caution rate and the speculation capacity.

Ming et al. [45] have presented a substitution attacks to cover comparable practices by harming behavior-based specifications. The key strategy for the attacks is to supplant a system call dependence graph to its semantically identical variations so that the comparable malware tests confidential unique family end up being characteristic. Accordingly, malware investigators need to put more endeavors into reconsidering the similar samples which may have been examined sometime recently. They distill general attacking strategies by mining more than 5200 malware tests’ behavior specifications and execute a compiler-level model to automate replacement attacks. By evaluating on the real malicious examples, the effectiveness of the proposed method to obstruct several behavior-based malware analysis tasks, such as clustering and malware comparison. Finally, they discussed likely countermeasures to support current malware protection.

Nikolopoulos and Polenakis [46] have proposed a graph-based model which using relations between gatherings of system-calls, distinguishes whether an unknown software sample is malicious or benign, and classifies a malevolent software to one of a set of an arrangement of known malware families. All the more correctly, clients used the System-call Dependency Graphs (or, for short, ScD-graphs), acquired by traces captured through dynamic taint investigation. The authors planed their model to be safe against strong changes applying our recognition and arrangement systems on a weighted coordinated graph, to be specific Group Relation Graph, or Gr-graph for short, coming about because of ScD-graph subsequent to gathering disjoint subsets of its vertices. For the discovery procedure, the authors proposed the Delta-comparability metric, and for the procedure of classification, they proposed the SaMe-similitude and NP-similarity measurements comprising the SaMe-NP closeness. At last, they evaluated their model for malware recognition and classification demonstrating its possibilities against malicious software measuring its identification rates and classification accuracy.

Sheen et al. [47] have considered Android-based malware for examination and an adaptable recognition component is planned to utilize multi-feature collaborative decision fusion (MCDF). The distinctive features of a malicious record like the consent-based features and the API call based features are considered keeping in mind the end goal to give a superior discovery via preparing a gathering of classifiers and combining their choices utilizing collective approach in view of likelihood hypothesis. The execution of the proposed model is evaluated on a gathering of Android-based malware including diverse malware families and the outcomes demonstrate that the presented approach give a superior execution than best in class troupe plans accessible.

Norouzi et al. [48] have proposed distinctive classification techniques with a specific end goal to recognize malware in light of the element and conduct of each malware. A dynamic investigation technique has been exhibited for recognizing the malware features. A recommended program has been introduced for changing over a malware behavior executive history XML document to an appropriate WEKA instrument input. To represent the execution proficiency and preparing information and test, the authors apply the proposed ways to deal with a genuine contextual investigation information set utilizing WEKA instrument. The evaluation results described that the availability of the proposed data mining approach. In addition, their proposed data mining methodology is more proficient for identifying malware and behavioral classification of malware can be helpful to recognize malware in a behavioral antivirus.

Galal et al. [49] proposed a behavior-based features model that defines malicious action exhibited by malware example. To remove the proposed model, the authors first perform dynamic examination on a generally late malware dataset inside a controlled virtual environment and capture traces of API calls conjured by malware examples. The traces are then generalized into high-level features refer to as actions. The proposed method is evaluated using some famous classification methods such as random forests, decision tree and SVM. The experimental results show that the classifiers attain high precision and satisfactory results in the detection of malware variants.

Summary of the reviewed behavior-based approaches

According to the discussed and reviewed behavior-based detection approaches, the comparison of the proposed articles has illustrated in Table 5. Table 5 presents the main idea, advantages, disadvantages and target environment of each technical study in behavior-based approaches. The main advantage of behavior-based detection approaches is detecting all of the suspicious files according to their calls’ behavior that increases the accuracy of malware prediction. The main disadvantage of the signature-based detection approaches is the runtime overhead. The target environment is categorized into three main platform including embedded systems, windows-based and smartphones. The most research studies in the behavior-based detection have used the smartphone environment for representing the proposed malware detection approach.
Table 5

A comparison of the reviewed behavior-based articles

Method

Main idea

Advantages

Disadvantages

Target environment

DeepAM

Deep learning malware detection [9]

Solving the encrypted

Problem in malware detection

Higher accuracy

High cost

High timely

Embedded systems

QDFG

Graph mining in malware detection [21]

Reducing response time

High complexity

High cost

Smartphone

DMDAM

Android malware detection [6]

Reducing concepts for increasing feature selection

High accuracy

High complexity

Run-time overhead

Smartphone

AMP

Android malware detection [22]

High accuracy

High cost

Smartphone

AMD

Android malware detection [38]

Higher accuracy than the other neuro-fuzzy approaches

Minimum false positive and false negative

Did not considering dynamic analysis of Android apps

Run-time overhead

Smartphone

AMAL

AMAL: automated malware analysis [39]

Providing high levels of precision, recall, and accuracy

Low cost

IP reputation

High overhead

Smartphone

AMCS

Android Malware Characterization and Detection [40]

Conducting static and dynamic analyses to extract features from each applications

Deploying online testing for Droid-detector

High cost

High overhead on API calls

Smartphone

DPIM

Deep Packet Inspection for malware [41]

High classification accuracy

Independence from packet payloads

Decoupling between detection and attribution

Datasets over fitting

High complexity

Windows-based

OOM

Objective Oriented malware [42]

Adapting multiple association rules

Improve the running speed of classification

High complexity

High cost

Not analyzing unmatched files

Windows-based

HAM

Hybrid analysis malware [43]

Low execution overhead

High accuracy time

High time consumption

Windows-based

BBA

Bilayer behavior abstraction [44]

Low overhead

Did not analyzing feature selection

Windows-based

Mspec

Malware specifications [45]

Good normalizing features

Low execution time

Did not analyzing the accuracy conditions

High complexity

Windows-based

SyCM

System-call malware [46]

High accuracy

High dependency analysis for calls

High time consumption

Smartphone

ABM

Android based malware [47]

Using multi-feature attributes

High scalability

High complexity

High execution time

Smartphone

DBM

Behavioral malware [48]

Extracting XML to feature files

High scalability

High complexity

Windows-based

MAPI

Malicious code based on API [49]

Adding additional heuristic occupations to show more actions

High accuracy rates

Not suitable for samples of external events

Existence analysis

Windows-based

CloudIntell

Feature extraction method in cloud [18]

Lowest energy consumption

High scalability

High complexity

High response time

Windows-based

SDMS

Security dependency network for malware detection [50]

Low response time

High accuracy

High energy

High complexity

Windows-based

DFAMD

Data flow android malware detection [51]

High efficiency

Low overhead

Low time

High complexity

High dependency

Smartphone

SCCMD

So-called compression-based malware detection [21]

High efficiency

Low complexity

High response time

Windows-based

DeepFlow

Deep-learning malware detection [52]

  

Smartphone

Also, Table 6 shows a technical comparison of the behavior-based detection factors in each article. These factors include case-study method, classification or clustering approach, data analysis method, used data set, total number of dataset and accuracy factor.
Table 6

A side-by-side comparison of the important factors in behavior-based detection of each article

Case study

Classification approach

Data analysis method

Used dataset

Total dataset

Accuracy

%

Deep learning malware detection [9]

DeepAM

Dynamic

Windows API calls in Comodo Cloud Security Center

2000

98

Graph mining in malware detection [21]

Graph search

Dynamic

Windows sandbox malware

6994

96

Android malware detection [6]

Random forest

Dynamic

Android applications

170

86

Android malware detection [22]

Multilayer perceptron

Dynamic

Several websites

734

97

Android malware detection [38]

Evolving neuro-fuzzy inference system

Dynamic

Google play and android

Malware genome Project

500

90

AMAL: automated malware analysis [39]

Decision trees

Dynamic

Random sample from internal user and external customers such as antivirus companies

2086

98

Android malware characterization and detection [40]

Deep belief networks

Hybrid

Google play and android

Malware genome project

1860

96.76

Deep Packet Inspection for malware [41]

BoostedJ48, J48, Naïve Bayesian and SVM

Dynamic

Wireless and Secure Networks Research Lab

4560

99

Objective Oriented malware [42]

Multiple association rules

Hybrid

Several websites

8000

97.2

Hybrid analysis malware [43]

Bayesian network, Naive Bayes, Lazy K-Stare

Hybrid

Selected randomly from malware repository of APA, the security research laboratory at Shiraz University

3000

95.27

Bilayer behavior abstraction [44]

SMV, Naïve Bayes, decision tree, logistic regression

Dynamic

Open-access malware database such as

VXHeaven website

17,000

94

Malware specifications [45]

System call dependency graph

Dynamic

VXHeavens website

5200

92

System-call malware [46]

SaMe-NP

Dynamic

Variety of commodity software types including editors, office suites, media players,

2667

95.9

Android based malware [47]

J48, SVM, IBk, NaiveBayes

Static

Google play and android

Malware services

2000

98.91

Behavioral Malware [48]

Regression, SVM, J48

Dynamic

Web data commons library in VirusSign and VXHeaven

7000

98.3

Malicious code based on API [49]

Decision tree, SVM and random forest

Dynamic

API hooking library in VirusSign

2000

96.89

Feature extraction method in cloud [18]

Decision tree, SVM, Boosting

Static

Random dataset of VirusTotal

15,000

99.69

Security dependency network for malware detection [50]

No read down and no write up

Dynamic

VXHeavens website

7257

93.92

Data flow android malware detection [51]

KNN, LR, BN

Static

VXHeavens website and Google play

2200

97.66

So-called compression-based malware detection [21]

k-NN, QDA, LDA, SVN, Decision Trees, and random forest

Dynamic

Cuckoo sandbox

7507

99.3

Deep-learning malware detection [52]

Naive Bayes, PART, Logistic Regression, SVM and MLP

Hybrid

Google play, virus share

11,000

95.05

Discussion

In this section, a statistical analysis of reviewed approaches of malware detection using data mining is presented. Figure 4 shows the statistical diagram for all of the classification methods in the selected malware detection approaches. In this report, the SVM method has most percentage for malware detection approach with 29%, j48 has 17%, NB has 10%, RF has 5%, ANN has 3% and the other methods have less than 2% usage in data mining results. We discover that the SMV method just has the best accuracy in the signature-based malware detection approaches using data mining.
Figure 4
Fig. 4

Classification methods in malware detection mechanism

Also, Fig. 5 shows the accuracy factor for each research. As shown, all of the accuracy factors higher than 80%. The maximum accuracy percentage is 99.2% for the DPIM approach [41] and the minimum accuracy percentage is 86% for the DMDAM approach [22].
Figure 5
Fig. 5

Accuracy factor for selected approaches in malware detection

Also, Fig. 6 shows the main case study diagram of each research in malware detection. As shown, the recent researches have considered android smartphones to analyze malware detection approaches with 40%. The symbolic code aggregation case studies in windows-based platform has 23%, the pattern mining has 11%, the system calls has 8% usage in malware detection.
Figure 6
Fig. 6

Case study analysis for each research in malware detection

In addition, Fig. 7 illustrates the total number of data set used for malware detection analysis in each research. In this figure, there are five research that use higher than 5000 real samples during the evaluation process. The BBA approach [44] has the maximum dataset with 17,000 samples and the AMD approach [38] has the minimum dataset with 500 samples.
Figure 7
Fig. 7

The total number of dataset used in each research

Also, Fig. 8 shows the data analysis methods percentage in terms of static, dynamic and hybrid analysis in selected research. The most data analysis methods have used dynamic analysis with 51%, the hybrid analysis has 29% and the static analysis has 20% usage. The 30% of the signature-based approaches have used the dynamic data analysis. The 65% of the behavior-based malware detection approaches have used the dynamic data analysis method.
Figure 8
Fig. 8

The data analysis methods in the selected articles

Open issues

Due to applying the survey on the malware detection approaches, the following research challenges as the open issues are presented that are not addressed by the research populations up to now.
  • Decryption/encryption detection: One of the important open issues in malware detection is information hiding malware techniques. Information hiding techniques are utilized to make information hard to take note. This practice ought not to be mistaken for encryption, in which the substance is disjointed, as it is rather clear. Such components are regularly utilized mutually to guarantee that a discussion stays indiscernible. Steganography is a standout amongst the most surely understood subfields of data stowing away and means to shroud mystery information in an appropriate transporter.

  • Meta-heuristic detection: The malware detection analyses using meta-heuristic algorithms can influence the speed up of the execution time and the total accuracy factor of the data mining process.

  • Real-time malware detection: Is based on hybrid analysis, secure multi-objective evolutionary malware detection, secure e-banking environments and secure healthcare systems are very challenging to recognize the malicious files and hidden attacks using data mining approaches.

Further studies are suggested to improve the accuracy of the related malware detection methods using evolutionary mechanisms.

In this survey, we performed a full description research to find more than 35 authors and different works. However, by considering the increasing development of studies on this topic, it is not possible to guarantee that all of the articles were recovered, particularly for 2010, because the research finished in July 2017.

Suggestion criteria

According to the existing discussion analysis, some technical suggestions are introduced to expand the malware detection approaches in the new platforms and architectures such as Internet of Things (IoT) applications, e-banking and social networks.

Some evolutionary methods can be improve the malware detection for predicting the polymorphism attacks in the electronic wallet applications. For example, a meta-heuristic algorithm finds the optimal signature detection for a polymorphism malware attacks in the electronic mobile payments.

Context-aware detection is a new idea for dynamic malware detection approaches in the IoT applications based on semantic signature that categorize API calls with respect to the most interactions between end user and application layer of the IoT. When the smart devices cannot interact between user devices and datacenters, the reliability and availability of the smart services have been decreased.

Providing a safe condition for the huge data collection such as big data against the malware attacks is the key challenge for the malware detection for navigating big data security. Therefore, to select the minimal sample space of the malware damage, the data collection and storing big data can be navigate using data mining and synthesis methods.

Conclusion

This paper presented a systematic literature survey of the malware detection approaches using data mining. The reviewed and papers were investigated and classified into two main categories; (1) signature-based and (2) behavior-based approaches. The malware detection approaches were compared and analyzed according to various essential factors such as classification approaches, data analysis methods, the number of the used dataset, accuracy factor and case study analysis. The advantage and disadvantage of each method were deliberated in the malware detection methods. Most of the selected articles in data mining are behavior-based techniques. In the malware analysis stage, the most case studies are proposed for the android smartphones. In addition, using meta-heuristic algorithms in malware detection analysis can speed up and improve the execution time and the overall accuracy of the data mining process. As the experimental results, we observed that the SVM method has most percentage for malware detection approach with 29%, j48 has 17%, Decision tree has 14%, NB has 10%, BF has 5% and the other methods have less than 3% usage in data mining results. We discover that the SVM method just has the best accuracy in the signature-based malware detection approaches using data mining. In addition, the maximum accuracy percentage is 99.2% for the DPIM approach and the minimum accuracy percentage is 86% for the DMDAM approach. Also, we observed that the recent researches have considered android smartphones to analyze malware detection approaches with 40%. The symbolic code aggregation case studies in windows-based platform has 23%, the pattern mining has 11%, the system calls has 8% usage in malware detection. Finally, we have seen that The 30% of the signature-based approaches have used the dynamic data analysis. The 65% of the behavior-based malware detection approaches have used the dynamic data analysis method. As an important open issue, some important topics such as secure multi-objective malware, e-banking environments, and healthcare systems malware attacks are challenging areas to recognize the malicious files and hidden attacks.

Footnotes
1

Multi-objective classifier detection.

 

Declarations

Authors’ contributions

AS as the corresponding author. RH as the co-author. Both authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Not applicable.

Consent for publication

Not applicable.

Ethics approval and consent to participate

We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. All authors have approved the manuscript and agree with its submission.

Funding

Not applicable.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
(2)
Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

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