Scanning tool for the detection of images embedded with malicious programs

Shoniwa, Robert T.R.; George, Geogen

doi:10.1109/eesco.2015.7253960

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…The concept of machine learning was introduced into the architecture of the research (Shoniwa and George, 2015). After the steganalysis has been performed to determine if the embedded files contain malicious software, machine learning was then applied to determine what exact form of malware was embedded in the image.…”

Section: Steganalysismentioning

confidence: 99%

A hybrid machine learning approach for analysis of stegomalware

Kadebu

Shoniwa²,

Zvarevashe

et al. 2023

IJIEOM

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PurposeGiven how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.Design/methodology/approachThis research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.FindingsThe tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results.Originality/valueCyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially expedient where the malware is stealthy, hiding IOC.

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Section: Steganalysismentioning

confidence: 99%