Malware Forensic Analytics Framework Using Big Data Platform

Chuprat, Suriayati; Ariffin, A.; Sahibuddin, Shamsul; Mahrin, Mohd Naz’ri; Senan, Firham M.; Ahmad, Noor Azurati; Narayana, G. V.; Magalingam, Pritheega; Anuar, Syahid; Talib, Mohd Zabri Adil

doi:10.1007/978-3-030-02683-7_19

Cited by 2 publications

(1 citation statement)

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“…The proposed framework in this paper delivers an approach that analyzes and predicts the future threat of malware attack in big data *Corresponding Author. www.ijacsa.thesai.org platform [14]. Authors of this study also provide some data in order to validate their results and framework workflow.…”

Section: Previous Research Initiativesmentioning

confidence: 95%

A Framework for Producing Effective and Efficient Secure Code through Malware Analysis

Pandey¹,

Tripathi²,

Alenezi³

et al. 2020

IJACSA

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Malware attacks are creating huge inconveniences for organizations and security experts. Due to insecure web applications, small businesses and personal systems are the most vulnerable targets of malware attacks. In the wake of this burgeoning cyber security breach, this article propositions a framework for a complete malware analysis process including dynamic analysis, static analysis, and reverse engineering process. Further, the article provides an approach of malicious code identification, mitigation, and management through a hybrid process of malware analysis, priority-based vulnerability mitigation process and various source code management approaches. The framework delivers a combined package of identification, mitigation and management that simplifies the process of malicious code handling. The proposed framework also gives a solution for reused codes in software industry. Successful implementation of the framework will make the code more robust in the face of unexpected behavior and deliver a revolutionary stage wise process for malicious code handling in software industry.

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Section: Previous Research Initiativesmentioning

confidence: 95%