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Showing 1 to 8 of 8 for “"Malware classification"”.
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Machine learning approaches for malware classification based on hybrid artefacts
Malware could be developed and transformed into various forms to deceive users and evade antivirus and security endpoint detection. Furthermore, if one machine in the network is compromised, it could be used for lateral movement--when malware spreads stealthily without sending an alarm to …
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Performance of Malware Classification on Machine Learning using Feature Selection
<p>The exponential growth of malware has created a significant threat in our daily lives, which heavily rely on computers running all kinds of software. Malware writers create malicious software by creating new variants, new innovations, new infections and more obfuscated malware by using …
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A Malware Classification Method Based on the Multi-Layer Feature Fusion of Malware Image Representations and Opcode Markov Images
As the threat of malware to information security becomes increasingly severe, the study of efficient malware classification methods has become more urgent. This paper proposes a multilayer malware classification method based on the fusion of image representation and opcode features. By integrating …
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An evaluation of the robustness of the natural-adversarial mutual information-based defense and malware classification against adversarial attacks for deep learning
… daily life. ML systems are being used to detect malware, control autonomous vehicles, classify images, assist with medical diagnosis, and block internet ads with high precision. Although the use of these ML systems has become widespread in our society, there is the potential for systems used in …
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Malware Image Classification using Machine Learning with Local Binary Pattern
<p>Malware classification is a critical part in the cybersecurity.</p> <p>Traditional methodologies for the malware classification</p> <p>typically use static analysis and dynamic analysis to identify malware.</p> <p>In this paper, a malware classification methodology based</p> <p>on its binary …
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Integrating Multiple Data Views for Improved Malware Analysis
Malicious software (malware) has become a prominent fixture in computing. There have been many methods developed over the years to combat the spread of malware, but these methods have inevitably been met with countermeasures. For instance, signature-based malware detection gave rise to polymorphic …
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Feature Selection and Improving Classification Performance for Malware Detection
… on machine learning algorithms for detecting malware. However, these methods require significant amount of extracted features for correct malware classification, making that feature extraction, training, and testing take significant time; even more, it has been unexplored which are the most …
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Improving the effectiveness and efficiency of dynamic malware analysis using machine learning
The malware threat landscape is constantly evolving, with upwards of one million new variants being released every day. Traditional approaches for detecting and classifying malware usually contain brittle handcrafted heuristics that quickly become outdated and can be exploited by nefarious actors. …