Improving Malware Detection Response Time with Behavior-Based Statistical Analysis Techniques

Prelipcean, Dumitru Bogdan; Popescu, Adrian; Gavriluţ, Dragoş Teodor

doi:10.1109/synasc.2015.44

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…These APIs contained the behavioural patterns of the ransomware, which could be used to introspect the attack characteristics and extract the latent features by analysing the usage (calls) to certain API functions [20,23,67]. This approach is commonly used by many dynamic ransomware and malware detection studies [20,59,60,[68][69][70]. Figure 6 shows the general architecture of the crypto-ransomware dynamic analysis and detection process.…”

Section: A the Dataset And Experimental Environmentmentioning

confidence: 99%

Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection

Al‐rimy¹,

Maarof

Alazab

et al. 2021

Future Generation Computer Systems

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Section: A the Dataset And Experimental Environmentmentioning

confidence: 99%

Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection

Al‐rimy¹,

Maarof

Alazab

et al. 2021

Future Generation Computer Systems

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“…In recent years, hackers have been interested in monetary gain. The attacks that are most commonly used in such crimes rely on malicious codes [19]. Moreover, Internet fraud is also going beyond simply using personal information and is reaching a very sophisticated level by turning to automated mechanisms.…”

Section: Threat Analysis and Considerations For The Future Internetmentioning

confidence: 99%

HB-DIPM: Human Behavior Analysis-Based Malware Detection and Intrusion Prevention Model in the Future Internet

2016

J Inf Process Syst

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As interest in the Internet increases, related technologies are also quickly progressing. As smart devices become more widely used, interest is growing in words are missing here like "improving the" or "figuring out how to use the" future Internet to resolve the fundamental issues of transmission quality and security. The future Internet is being studied to improve the limits of existing Internet structures and to reflect new requirements. In particular, research on words are missing here like "finding new forms of" or "applying new forms of" or "studying various types of" or "finding ways to provide more" reliable communication to connect the Internet to various services is in demand. In this paper, we analyze the security threats caused by malicious activities in the future Internet and propose a human behavior analysis-based security service model for malware detection and intrusion prevention to provide more reliable communication. Our proposed service model provides high reliability services by responding to security threats by detecting various malware intrusions and protocol authentications based on human behavior.

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A cyber kill chain based taxonomy of banking Trojans for evolutionary computational intelligence

Kiwia

Dehghantanha

Choo

et al. 2018

Journal of Computational Science

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Malware such as banking Trojans are popular with financially-motivated cybercriminals. Detection of banking Trojans remains a challenging task, due to the constant evolution of techniques used to obfuscate and circumvent existing detection and security solutions. Having a malware taxonomy can facilitate the design of mitigation strategies such as those based on evolutionary computational intelligence. Specifically, in this paper, we propose a cyber kill chain based taxonomy of banking Trojans features. This threat intelligence based taxonomy providing a stage-by-stage operational understanding of a cyber-attack, can be highly beneficial to security practitioners and the design of evolutionary computational intelligence on Trojans detection and mitigation strategy. The proposed taxonomy is validated by using a real-world dataset of 127 banking Trojans

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Improving Malware Detection Response Time with Behavior-Based Statistical Analysis Techniques

Cited by 7 publications

References 11 publications

Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection

Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection

HB-DIPM: Human Behavior Analysis-Based Malware Detection and Intrusion Prevention Model in the Future Internet

A cyber kill chain based taxonomy of banking Trojans for evolutionary computational intelligence

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