Dai Seeks Solution to National Wireless Security Needs

Dr. Huaiyu Dai and Dr. Peng Ning have been awarded $479,993 by the National Science Foundation (NSF) for research on “Intelligent and Cross-Layer Attack and Defense in Spectrum Sharing” to address this need.

With the proliferation of wireless devices – particularly smart phones – the demand for wireless data, and thus for wireless bandwidth, keeps growing exponentially. Unfortunately, the public radio spectrum on which all wireless data travels is limited – so we need to find ways to more efficiently share it between our various wireless systems and devices.

While significant progress has been made in efficient spectrum access and sharing technologies, their successful deployment – required to fulfill the United State’s National Broadband Plan – now relies on the development of adequate security mechanisms. Such security mechanisms must be able to protect the welfare of all stakeholders in the spectrum sharing systems, in particular the federal government and military users. In addition, they should address the new challenges brought about by the advanced technologies and new access paradigms that have enabled innovative spectrum sharing, such as the dynamic spectrum access (DSA) paradigm and cognitive radio (CR) technology. 

Dr. Huaiyu Dai, associate professor of electrical and computer engineering, and Dr. Peng Ning, professor of computer science, have been awarded $479,993 by the National Science Foundation (NSF) for research on “Intelligent and Cross-Layer Attack and Defense in Spectrum Sharing” to address this need.

According to Dr. Dai, “this proposal constitutes a solid step towards filling this gap, seeking to obtain a deeper understanding of the emerging attacking strategy and behavior in this new arena, and develop a holistic view and solution to the security of spectrum access and sharing. In particular, assuming adequate intelligence and growing reasoning and learning capabilities for both the legitimate system and the adversary, the arms race between them will be explored through the stochastic game modeling and multi-agent reinforcement learning (MARL) methodologies. The expected outcome will be valuable to all players in wireless industry, as well as to all sectors of the national economy that benefit from wireless innovation.”