作者: Rania El-Sayed , Amir El-Ghamry , Tarek Gaber , Aboul Ella Hassanien , None
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摘要: IoT devices are increasingly used every day. However, their limited resources cause them to be vulnerable to any malware, malicious software that causes harm to any device without the user’s knowledge. Malwares are called zero-day attacks, a serious threat to internet security since they exploit zero-day vulnerabilities with unknown nature, making them difficult to detect. To solve this problem, the structure of these malware need to be known and analyzed, therefore a small dataset of different types of malware including zero-day attack, is live-captured from network traffic to form 1000 PCAP files representing malware and normal behavior that is used as a source for traffic analysis and malware classification. Most traditional malware detection systems proposed in the literature use signature-based methods, so these systems cannot detect unknown malware types. This paper aims to introduce novel IoT image …