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Filters: Author is Hossain, Md. Shohrab  [Clear All Filters]
2021-05-05
Hossain, Md. Turab, Hossain, Md. Shohrab, Narman, Husnu S..  2020.  Detection of Undesired Events on Real-World SCADA Power System through Process Monitoring. 2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :0779—0785.
A Supervisory Control and Data Acquisition (SCADA) system used in controlling or monitoring purpose in industrial process automation system is the process of collecting data from instruments and sensors located at remote sites and transmitting data at a central site. Most of the existing works on SCADA system focused on simulation-based study which cannot always mimic the real world situations. We propose a novel methodology that analyzes SCADA logs on offline basis and helps to detect process-related threats. This threat takes place when an attacker performs malicious actions after gaining user access. We conduct our experiments on a real-life SCADA system of a Power transmission utility. Our proposed methodology will automate the analysis of SCADA logs and systemically identify undesired events. Moreover, it will help to analyse process-related threats caused by user activity. Several test study suggest that our approach is powerful in detecting undesired events that might caused by possible malicious occurrence.
2020-09-11
Ashiq, Md. Ishtiaq, Bhowmick, Protick, Hossain, Md. Shohrab, Narman, Husnu S..  2019.  Domain Flux-based DGA Botnet Detection Using Feedforward Neural Network. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1—6.
Botnets have been a major area of concern in the field of cybersecurity. There have been a lot of research works for detection of botnets. However, everyday cybercriminals are coming up with new ideas to counter the well-known detection methods. One such popular method is domain flux-based botnets in which a large number of domain names are produced using domain generation algorithm. In this paper, we have proposed a robust way of detecting DGA-based botnets using few novel features covering both syntactic and semantic viewpoints. We have used Area under ROC curve as our performance metric since it provides comprehensive information about the performance of binary classifiers at various thresholds. Results show that our approach performs significantly better than the baseline approach. Our proposed method can help in detecting established DGA bots (equipped with extensive features) as well as prospective advanced DGA bots imitating real-world domain names.