Biblio
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2022. Lightweight Intrusion Detection System(L-IDS) for the Internet of Things. 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). :1—4.
Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.
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2020. Behavior-Based Detection of Cryptojacking Malware. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). :0543—0545.
With rise of cryptocurrency popularity and value, more and more cybercriminals seek to profit using that new technology. Most common ways to obtain illegitimate profit using cryptocurrencies are ransomware and cryptojacking also known as malicious mining. And while ransomware is well-known and well-studied threat which is obvious by design, cryptojacking is often neglected because it's less harmful and much harder to detect. This article considers question of cryptojacking detection. Brief history and definition of cryptojacking are described as well as reasons for designing custom detection technique. We also propose complex detection technique based on CPU load by an application, which can be applied to both browser-based and executable-type cryptojacking samples. Prototype detection program based on our technique was designed using decision tree algorithm. The program was tested in a controlled virtual machine environment and achieved 82% success rate against selected number of cryptojacking samples. Finally, we'll discuss generalization of proposed technique for future work.