The proposed thesis focuses on studying and selecting relevant and novel cyberattacks affecting IoT devices (such as Raspberry Pis) to infect them and monitor their behaviour from the host perspective. For that, we will start with the study and analysis of novel families of malware (botnet, ransomware, rootkit, etc.) affecting IoT devices. In parallel, we will also analyze the available dimensions (from the device perspective) able to monitor the device normal behaviour (system calls, hardware performance counters, resource usage, etc.). Finally, the device will be infected with different malware families. The device's normal and abnormal behaviours produced by each malware will be monitored to create a dataset.
 L. Taheri, A. F. Abdul Kadir, and A. H. Lashkari. Extensible Android malware detection and family classification using network-flows and API-calls. In 2019 International Carnahan Conference on Security Technology, pages 1–8, Oct. 2019
 W. Haider, J. Hu, J. Slay, B. P. Turnbull, and Y. Xie. Generating realistic intrusion detection system dataset based on fuzzy qualitative modelling. Journal of Network and Computer Applications, 87:185–192, 2017
 S. Shen, V. V. Beek, and A. Iosup. Statistical characterization of business-critical workloads hosted in cloud data centres. In 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pages 465–474, May 2015
 G. Creech. Developing a high-accuracy cross-platform Host-Based Intrusion Detection System capable of reliably detecting zero-day attacks. PhD thesis, University of New South Wales, Canberra, Australia, 2014
 G. Creech and J. Hu. A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns. IEEE Transactions on Computers, 63(4):807–819, 2013.
Supervisors: Dr Alberto Huertasback to the main page