A New Static Detection Method of Malicious Document Based on Wavelet Package Analysis

Gu, Binjie; Fang, Yong; Jia, Peng; Liu, Liang; Zhang, Lei; Wang, Meilin

doi:10.1109/iih-msp.2015.72

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…Some of the common malware analysis techniques are virus scan, analysis of memory/os artifacts, PE file scanning, and disassembly of code. Gu et al [19] used the study of wavelets to derive features from actual data. Such features can be used to determine whether malicious code has been inserted in the compound text.…”

Section: Malware Analysismentioning

confidence: 99%

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Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

Kumar¹,

Alenezi²,

Ansari³

et al. 2020

IJIES

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Nowadays, most of the cyber-attacks are initiated by extremely malicious programs known as Malware. Malwares are very vigorous and can penetrate the security of information and communication systems. While there are different techniques available for malware analysis, it becomes challenging to select the most effective approach. In this context, the decision-making process may be an efficient means of empirically assessing the impact of different methods for securing the web applications. In this research study, we have used a methodology that includes the integration of Fuzzy AHP and Fuzzy TOPSIS technique for evaluating the impact of different malware analysis techniques in web application perspective. This study uses different versions of a university’s web application for evaluating the impact of several existing malware analysis techniques. The findings of the study show that the Reverse Engineering approach is the most efficient technique for analyzing complex malware. The outcome of this study would definitely aid the future researchers and developers in selecting the appropriate techniques for scanning the web application code and enhancing the security.

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Section: Malware Analysismentioning

confidence: 99%

“…Next step is to normalize the fuzzy decision matrix with the assistance of Eq. (19). The normalized fuzzy decision matrix is represented by ̃ and is depicted as follows.…”

Section: Fuzzy Topsismentioning

confidence: 99%

Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

Kumar¹,

Alenezi²,

Ansari³

et al. 2020

IJIES

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show abstract

“…Several online automated tools exist for dynamic analysis of malware, e.g. Norman Sandbox [19], CWSandbox [20], Anubis [21] and TTAnalyzer [22], Ether [23] and ThreatExpert [24]. The analysis reports generated by these tools give in-depth understanding of the malware behavior and valuable insight into the actions performed by them.…”

Section: Dynamic Analysismentioning

confidence: 99%

“…Many studies use static analysis for malware detection using exact decompilation [16], similarity testing framework [17], based on register contents [18], using two-dimensional binary program features [19], subroutine based detection [20], statistics of assembly instructions [21], file relation graphs [22], de-anonymizing programmers via code stylometry [23], based upon a wavelet package technique [24], analysis and comparison of disassemblers for opcode [25].…”

mentioning

confidence: 99%

A Survey on Malware Detection and Analysis Tools

Talukder¹,

Talukder²

2020

IJNSA

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The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.

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“…Many studies use static analysis for malware detection using exact decompilation [16], similarity testing framework [17], based on register contents [18], using two dimensional binary program features [19], subroutine based detection [20], statistics of assembly instructions [21], file relation graphs [22], de-anonymizing programmers via code stylometry [23], based upon a wavelet package technique [24], analysis and comparison of disassemblers for opcode [25] The studies that use dynamic analysis perform synthesis the semantics of obfuscated code [7], multi-hypothesis testing [26], analyzing quantitative data flow graph metrics [27], using simplified data dependent api call graph [28], downloader graph analytics [29], access behavior [30], [31], APIs in initial behavior [32], log based crowdsourcing analysis [33] There have been many studies on the detection and analysis of malware using machine learning that study fine-grained features [34], deep learning [35], [36], dynamic features [37], static features [38], concept drift [39], predicting signatures [40], hybrid framework [41], malware metadata [42], reverse engineering of large datasets of binaries [43].…”

Section: Introductionmentioning

confidence: 99%

Tools and Techniques for Malware Detection and Analysis

Talukder¹

2020

Preprint

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One of the major and serious threats that the Internet faces today is the vast amounts of data and files which need to be evaluated for potential malicious intent. Malicious software, often referred to as a malware that are designed by attackers are polymorphic and metamorphic in nature which have the capability to change their code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executables. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malware into their known families using machine learning techniques. This survey paper provides an overview of techniques and tools for detecting and analyzing the malware.

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A New Static Detection Method of Malicious Document Based on Wavelet Package Analysis

Cited by 8 publications

References 2 publications

Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

A Survey on Malware Detection and Analysis Tools

Tools and Techniques for Malware Detection and Analysis

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