A deep learning-based IoT malware detection approach for electric vehicle charging stations. [PDF]
Sci RepXia L, Chen Y, Han L.
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Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]
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, 2013 Under most widely-used security mechanisms the programs users run possess more authority than is strictly necessary, with each process typically capable of utilising all of the user's privileges.
Payne, C., Schreuders, Z.C., McGill, T.
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AI-driven adaptive adversaries and the erosion of cryptographic trust in public key systems. [PDF]
J Anal Sci TechnolRadanliev P.
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Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]
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Detecting Metamorphic Malware based on Machine Learning
, 2019 With the prevalence of the Internet, the number of malware in the Windows platform is growing. According to the McAfee Labs\ue2 analysis report, the cases of malware using evasive techniques has also increased. Many kinds of evasive techniques, including
Dai, Chen-Han
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Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger. [PDF]
Sensors (Basel)Maghanaki M +3 more
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Self-Organizing Neural Grove for Malware Detection in IoT Edge Devices. [PDF]
Sensors (Basel)Inoue H, Komura T, Hashimoto I.
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Automated forensic extraction of encryption keys using behavioural analysis [PDF]
, 2012 Owen, Gareth
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Ransomware attacks and cybersecurity concerns in modern hospitals: vulnerabilities and impacts on trauma centers and patient care. [PDF]
Trauma Surg Acute Care OpenMartin MJ, Patel PP, Egodage T.
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