As novel technologies continue to reshape the digital era, cyberattacks are also increasingly becoming more commonplace and sophisticated. Distributed denial of service (DDoS) attacks are, perhaps, the most prevalent and exponentially-growing attack, targeting the varied and emerging computational network infrastructures across the globe. This necessitates the design of an efficient and early detection of large-scale sophisticated DDoS attacks. Software defined networks (SDN) point to a promising solution, as a network paradigm which decouples the centralized control intelligence from the forwarding logic. In this work, a deep convolutional neural network (CNN) ensemble framework for efficient DDoS attack detection in SDNs is proposed. The proposed framework is evaluated on a current state-of-the-art Flow-based dataset under established benchmarks. Improved accuracy is demonstrated against existing related detection approaches. INDEX TERMS Software defined network (SDN), anomaly detection, distributed denial of service (DDoS), deep learning, deep convolutional neural network (CNN).

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The use of biometrics in consumer electronics increases security while decreasing responsibility of the user. Most technologies in this realm, however, authenticate users based upon static characteristics such as voice patterns or fingerprints, rather than dynamic qualities such as brainwaves. In this work, we evaluate the efficacy of existing consumer-level EEG devices as authentication mechanisms for consumer electronics via a novel biometric. In doing so, we enumerate the requirements for implementation and large-scale adoption of more viable consumer solutions.

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