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Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu
Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu
Military Cyber Affairs
Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.
Artificial Intelligence In Retailing, Ibrahim Kircova, Munise Hayrun Saglam, Sirin Gizem Kose
Artificial Intelligence In Retailing, Ibrahim Kircova, Munise Hayrun Saglam, Sirin Gizem Kose
University of South Florida (USF) M3 Publishing
Advances in Artificial Intelligence and Machine Learning technologies have brought a completely new level of data processing that provides deeper business insights. Purchasing advice, dynamic pricing, personal content and advice have become widely used in the retail industry thanks to artificial intelligence. Almost real-time results can be achieved by expanding the scope of data obtained from existing customers and algorithms that mimic human-like behavior. In addition, interactions with machines are more widely accepted than before, allowing consumers to accept innovations faster and thus increase brand loyalty. On the other hand, the success of artificial intelligence, which will change the future …
Evaluating Machine Learning Classifiers For Defensive Cyber Operations, Michael D. Rich, Robert F. Mills, Thomas E. Dube, Steven K. Rogers
Evaluating Machine Learning Classifiers For Defensive Cyber Operations, Michael D. Rich, Robert F. Mills, Thomas E. Dube, Steven K. Rogers
Military Cyber Affairs
Today’s defensive cyber sensors are dominated by signature-based analytical methods that require continuous maintenance and lack the ability to detect unknown threats. Anomaly detection offers the ability to detect unknown threats, but despite over 15 years of active research, the operationalization of anomaly detection and machine learning for Defensive Cyber Operations (DCO) is lagging. This article provides an introduction to machine learning concepts with a focus on the unique challenges to using machine learning for DCO. Traditional machine learning evaluation methods are challenged in favor of a value-focused evaluation method that incorporates evaluator-specific weights for classifier and sensitivity threshold selection …