The University of Texas at Dallas

Erik Jonsson School of Engineering and Computer Science

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Computer Science Professor Awarded IEEE Big Data Security Senior Research Award

Computer Science Professor Awarded IEEE Big Data Security Senior Research Award

Dr. Latifur Khan, professor of computer science in the Erik Jonsson School of Engineering and Computer Science at The University of Texas at Dallas, recently received the Institute of Electrical and Electronics Engineers (IEEE) Big Data Security Senior Research Award in recognition for his outstanding, sustained research contributions in the field of big data security and privacy for over ten years.

IEEE is the world’s premiere technical professional organization for the advancement of technology. Khan received this award in May 2019 at the 5th IEEE International Conference on Big Data Security on Cloud Conference in Washington, D.C.

Dr. Latifur Khan

Throughout his career, Khan has researched big data security and privacy as well as big data analytics, stream mining, machine learning for cyber security, insider threat applications along with novel approaches for website finger printing. He is an internationally recognized authority in stream data mining fundamentals and applications in cybersecurity and scalable complex data analytics. Additionally, he pioneered the development of many novel algorithms, frameworks and performance-driven approaches in these areas.

Khan has developed several novel mathematical approaches to data collection. As Director of Security Analytics at the Cyber Security Research and Education Institute (CSI) at UT Dallas, led by internationally recognized cybersecurity expert Dr. Bhavani Thuraisingham, Khan has studied machine learning, data mining, data analytics in cybersecurity, real-time anomaly detection over evolving streams, vulnerability analysis of malware apps for smartphones, encrypted traffic analysis, and secure encrypted stream data processing using modern secure hardware extensions.

“Data streams are continuous flows of data,” Khan explains. “Examples of data streams include network traffic, sensor data, call center records, and so on. The sheer volume and speed of data pose a great challenge for the data mining community to mine them.”

Khan’s work on novel class detection over evolving data streams has revealed new areas of research in the field of stream mining and online learning. He was the first researcher to demonstrate that the novel class detection technique can be effectively utilized for finding brand new or emerging class patterns in streaming data when the data may also possess instances from multiple existing classes or characteristics of data may change. This work has influenced cybersecurity applications, including intrusion detection, insider threat detection, website fingerprinting and textual stream.

Khan said, “In particular to the problem of intrusion detection over a stream of network traffic, one can consider each type of attack as a class label. In this case, novel class occurs when a completely new kind of attack occurs in the traffic.”

In recognition of his contributions, Khan received an IBM Faculty Award, IEEE’s technical achievement award for this pioneering work, as well as a number of US patents. Additionally, Khan has been named a distinguished scientist by the Association of Computing Machinery (ACM) and a fellow of the Society of Information Reuse and Integration (SIRI). He received an IEEE Technical Achievement Award from the IEEE Systems Man and Cybernetics Society and the IEEE Transportation Society in 2012.

In his more than 18 years at UT Dallas, Khan has published three books and over 270 papers in 40 journals and in peer-reviewed conference proceedings. He has also given more than 20 keynote speeches at globally recognized conferences and workshops. He earned his PhD from the University of Southern California.


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