SBRT: API Signature Behaviour Based Representation Technique for Improving Metamorphic Malware Detection

Mohamed, Gamal Abdel Nassir; Ithnin, Norafida

doi:10.1007/978-3-319-59427-9_79

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Many research studies were conducted on attacker profiling from the viewpoint of malware creation. Mohaisen A, et al classified the malware group through dynamic analysis based on the API behavior that occurs when executing malware and estimated the same attacker [1,2,24,25,26], whereas Kinable, et al studied the method of malicious code classification through static analysis based on the call graph of malicious code [3,4,27]. Regarding attacker profiling from the viewpoint of botnet, Gu, G., M. Feily, et al conducted a study on analyzing the attack resources possessed by the same attacker by detecting botnets and analyzing the command and control channel [5,6].…”

Section: Related Workmentioning

confidence: 99%

Cyber-attack group analysis method based on association of cyber-attack information

Son¹,

Kim²,

Lee³

2020

KSII TIIS

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Cyber-attacks emerge in a more intelligent way, and various security technologies are applied to respond to such attacks. Still, more and more people agree that individual response to each intelligent infringement attack has a fundamental limit. Accordingly, the cyber threat intelligence analysis technology is drawing attention in analyzing the attacker group, interpreting the attack trend, and obtaining decision making information by collecting a large quantity of cyber-attack information and performing relation analysis. In this study, we proposed relation analysis factors and developed a system for establishing cyber threat intelligence, based on malicious code as a key means of cyber-attacks. As a result of collecting more than 36 million kinds of infringement information and conducting relation analysis, various implications that cannot be obtained by simple searches were derived. We expect actionable intelligence to be established in the true sense of the word if relation analysis logic is developed later.

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Section: Related Workmentioning

confidence: 99%

Cyber-attack group analysis method based on association of cyber-attack information

Son¹,

Kim²,

Lee³

2020

KSII TIIS

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“…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 [13][14][15][16][17] had discussed the malware detection approaches. There are some defects in the surveyed research.…”

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confidence: 99%

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

Souri

Hosseini

2018

Hum. Cent. Comput. Inf. Sci.

305

166

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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.

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A Comprehensive Survey on Identification of Malware Types and Malware Classification Using Machine Learning Techniques

Pachhala

Jothilakshmi

Battula³

2021

2021 2nd International Conference on Smart Electronics and Communication (ICOSEC)

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SBRT: API Signature Behaviour Based Representation Technique for Improving Metamorphic Malware Detection

Cited by 3 publications

References 27 publications

Cyber-attack group analysis method based on association of cyber-attack information

Cyber-attack group analysis method based on association of cyber-attack information

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

A Comprehensive Survey on Identification of Malware Types and Malware Classification Using Machine Learning Techniques

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