Skip to content

Dr. Latifur Khan Awarded the IEEE Big Data Security Senior Research Award

Last May, Dr. Latifur Khan received the IEEE Big Data Security Senior Research Award in recognition for his outstanding and sustained research contributions in the field of Big Data Security and Privacy for over ten years. Dr. Khan was given this award at the 5th IEEE International Conference on Big Data Security on Cloud Conference (BigDataSecurity 2019) in Washington DC.

Throughout his career, Dr. Khan has conducted sustainable, cutting-edge research in Big Data Security and Privacy as well as in Big Data Analytics, Stream Mining, Machine Learning for Cyber Security, Insider Threat Applications along with novel approaches for Website Finger Printing.

Dr. Khan is an internationally recognized authority in stream data mining fundamentals and applications in cybersecurity and scalable complex data analytics. He pioneered the development of many novel algorithms, frameworks and performance-driven approaches in these areas. Dr. Khan has developed several novel approaches, supported by mathematical rigors and demonstrates the effectiveness of his approaches over baselines with experimental results. More specifically, his research group has done significant research on 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. 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,” explains Dr. Khan.  His work on novel class detection over evolving data streams opened up new areas in the field of stream mining/online learning. Khan 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 where the data may also possess instances from multiple existing classes (characteristics of data may change). This work has had a significant impact in cybersecurity applications, including intrusion detection, insider threat detection, website fingerprinting, and textual stream. “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,” notes Dr. Khan.  Khan was the first to investigate this problem and proposed improved solutions. He received an IBM Faculty Award, IEEE’s technical achievement award for this pioneering work as well as a number of US patents.

In summary, although Dr. Khan is rather modest about his significant accomplishments, he has, in fact, carried out exemplary research in his 18+ years at UT Dallas after receiving his Ph.D. from the University of Southern California. Dr. Khan has published over 270 papers in 40 journals, in peer-reviewed conference proceedings, and three books. He has given more than twenty keynote speeches at various selective conferences and workshops around the world.  Dr. Khan serves as the Director of Security Analytics within the UT Dallas Cyber Security Research and Education Institute (CSI).

Below are just a few of his accomplishments

  • ACM Distinguished Scientist
  • Fellow of SIRI (Society of Information Reuse and Integration) award in Aug 2018.
  • IBM Faculty Award (Research), 2016: Fundamental Research on Data Analytics (Stream Mining)
  • IEEE Technical Achievement Award, IEEE Systems Man and Cybernetics Society and the IEEE Transportation Systems Society, 2012

Note that the IEEE is the world’s largest technical professional organization for the advancement of technology. The IEEE and its members inspire a global community to innovate for a better tomorrow through highly cited publications, conferences, technology standards, and professional and educational activities.


The UT Dallas Computer Science program is one of the largest Computer Science departments in the United States with over 3,315 bachelors-degree students, more than 1,110 master’s students, 165 Ph.D. students,  52 tenure-track faculty members, and 44 full-time senior lecturers, as of Fall 2019. With The University of Texas at Dallas’ unique history of starting as a graduate institution first, the CS Department is built on a legacy of valuing innovative research and providing advanced training for software engineers and computer scientists.