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A Comprehensive Review on Malware Detection Approaches
IEEE Access, 2020 According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques.
Omer Aslan, Refik Samet
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Effective and Reliable Malware Group Classification for a Massive Malware Environment [PDF]
International Journal of Distributed Sensor Networks, 2016 Most of the cyber-attacks are caused by malware, and damage from them has escalated from cyber space to home appliances and infrastructure, thus affecting the daily living of the people. As such, anticipative analysis and countermeasures for malware have
Taejin Lee, Jin Kwak
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DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
Applied Sciences, 2023 Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher +4 more
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Federated Learning for Malware Detection in IoT Devices [PDF]
Comput. Networks, 2021 This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm.
Valerian Rey +4 more
semanticscholar +1 more source
Adversarial Malware Generation Method Based on Genetic Algorithm [PDF]
Jisuanji kexue, 2023 In recent years,with the development of Internet technology,malware has become an important method of network attack.To defend against malware attacks,deep learning techniques can be applied to malware detection.However,due to the limitations of deep ...
LI Kun, GUO Wei, ZHANG Fan, DU Jiayu, YANG Meiyue
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IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense [PDF]
IEEE Internet of Things Journal, 2023 Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from the ...
Rahul Yumlembam +3 more
semanticscholar +1 more source
Malware Detection Using Deep Learning and Correlation-Based Feature Selection
Symmetry, 2023 Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical.
E. Alomari +6 more
semanticscholar +1 more source
Attention-Based Cross-Modal CNN Using Non-Disassembled Files for Malware Classification
IEEE Access, 2023 The role of malware classification is crucial in addressing the explosive increase in malware variants. By classifying malware instances into malware families, malware analysts can apply appropriate techniques and tools to handle malware variants in each
Jeongwoo Kim +2 more
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Malware Detection on Local Network based on Honeypot and Yara
Sistemasi: Jurnal Sistem Informasi, 2023 The malware threats have never subsided, even the trend shows an increase and varies along with the development of hardware and software technology. End user may not realize if their machine is compromised by malware.
Nur Rohman Rosyid +4 more
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A Survey on Malware Detection with Graph Representation Learning [PDF]
ACM Computing Surveys, 2023 Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization ...
Tristan Bilot +3 more
semanticscholar +1 more source