CS Other Presentations

Department of Computer Science - University of Cyprus

Besides Colloquiums, the Department of Computer Science at the University of Cyprus also holds Other Presentations (Research Seminars, PhD Defenses, Short Term Courses, Demonstrations, etc.). These presentations are given by scientists who aim to present preliminary results of their research work and/or other technical material. Other Presentations serve as a forum for educating Computer Science students and related announcements are disseminated to the Department of Computer Science (i.e., the csall list):

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Presentations Coordinator: Demetris Zeinalipour

PhD Defense: mIDS: a Lightweight Intrusion Detection System for Wireless Sensor Networks and the Internet of Things, Christiana Ioannou (University of Cyprus, Cyprus), Wednesday, January 25, 2017, 11:00-12:00 EET.

The Department of Computer Science at the University of Cyprus cordially invites you to the PhD Defense entitled:

mIDS: a Lightweight Intrusion Detection System for Wireless Sensor Networks and the Internet of Things

Abstract:
Intrusion Detection Systems (IDS) are found at the second line of security defense. They are engaged once the intruder has penetrated the first line of defense, the preventive layer. Most intrusion detection solutions for WSNs in the literature, are evaluated using simulation tools or mathematical models. We present mIDS a run-time, low-memory overhead IDS that can detect unknown attacks by imposing minimum computation power. We implemented a monitoring tool in Contiki O/S, called RMT, that monitors and collects data from multiple network layers, in real time. RMT gathers statistics from the various sensor node's layers that can be customized to decrease memory cost. RMT was evolved to an anomaly IDS, called mIDS, that detects attacks within the network. At run time, mIDS uses input data from RMT and the normal activity profile to detect abnormalities within the network. At predefined time intervals mIDS analyses sensor node activity using the probability equation extracted at the offline stage using Binary Logistic Regression (BLR). We developed BLR models for each attack implemented and evaluated real time in three different topologies. mIDS achieved 96% - 100% accuracy levels. Depending on which BLR model raised an alarm, we can identify and classify the following network-layer attacks: Selective Forward, Blackhole, and Sinkhole.

Short Bio:
Christiana Ioannou completed her undergraduate studies at the San Diego State University (SDSU) in 2003 in Computer Science. In 2004 she received her diploma in Management at MIM (Mediterranean Institute of Technology). In 2006 she received her MSc in Computer Science at the Florida Institute of Technology (FIT), in Melbourne, Florida. Her master thesis was on Network Security. The objective of her master thesis was to detect novel malicious system calls. In 2009 she joined Network Research Lab (NetRL) at the University of Cyprus as a PhD student under the guidance of Dr. Vasos Vassiliou. She was involved in EU FP7 GINSENG and SeaMobile projects. Christiana is a member of the Cyprus Scientific and Technical Chamber. Her research interests include Wireless Sensor Networks, Intrusion Detection, and Prevention Systems.

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