Trustworthy data delivery in mobile P2P network

Waluyo, Agustinus Borgy; Taniar, David; Rahayu, Wenny; Srinivasan, Bala

doi:10.1016/j.jcss.2016.09.007

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…Detection of malware in stages reduces the impact of sample analysis on system performance as less number of malware is needed to be analysed [2]. The need for secure, trustworthy and high-performance devices [3] in IoT devices and other fast systems automatically limits the use of dynamic analysis-based detection methods. Dynamic analysis requires more resources and more time to execute and observe the behaviour of the file.…”

Section: Introductionmentioning

confidence: 99%

Similarity hash based scoring of portable executable files for efficient malware detection in IoT

Namanya

Awan

Disso

et al. 2020

Future Generation Computer Systems

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The current rise in malicious attacks shows that existing security systems are bypassed by malicious files. Similarity hashing has been adopted for sample triaging in malware analysis and detection. File similarity is used to cluster malware into families such that their common signature can be designed. This paper explores four hash types currently used in malware analysis for portable executable (PE) files. Although each hashing technique produces interesting results, when applied independently, they have high false detection rates. This paper investigates into a central issue of how different hashing techniques can be combined to provide a quantitative malware score and to achieve better detection rates. We design and develop a novel approach for malware scoring based on the hashes results. The proposed approach is evaluated through a number of experiments. Evaluation clearly demonstrates a significant improvement (> 90%) in true detection rates of malware.

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Section: Introductionmentioning

confidence: 99%