Search for: All records

First responders and other tactical teams rely on mo- bile tactical networks to coordinate and accomplish emergent time- critical tasks. The information exchanged through these networks is vulnerable to various strategic cyber network attacks. Detecting and mitigating them is a challenging problem due to the volatile and mobile nature of an ad hoc environment. This paper proposes MalCAD, a graph machine learning-based framework for detecting cyber attacks in mobile tactical software-defined networks. Mal- CAD operates based on observing connectivity features among various nodes obtained using graph theory, instead of collecting information at each node. The MalCAD framework is based on the XGBOOST classification algorithm and is evaluated for lost versus wasted connectivity and random versus targeted cyber attacks. Results show that, while the initial cyber attacks create a loss of 30%–60% throughput, MalCAD results in a gain of average throughput by 25%–50%, demonstrating successful attack mitigation. 

For the next generation of wireless technologies, Orthogonal Frequency Division Multiplexing (OFDM) remains a key signaling technique. Peak-to-Average Power Ratio (PAPR) reduction must be included with OFDM to reduce the detrimental high PAPR exhibited by OFDM. The cost of PAPR reduction techniques stems from adding multiple IFFT iterations, which are computationally expensive and increase latency. We propose a novel PAPR Estimation Technique called PESTNet which reduces the necessary IFFT operations for PAPR reduction techniques by using deep learning to estimate the PAPR before the IFFT is applied. This paper gives a brief background on PAPR in OFDM systems and describes the PESTNet algorithm and the training methodologies. A case study of the estimation model is provided where results demonstrate PESTNet is able to give an accurate estimate of PAPR and can compute large batches of resource grids up to 10 times faster than IFFT based techniques.