Computer Engineering Commons^™

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.

Towards Secure Deep Neural Networks For Cyber-Physical Systems, Jiangnan Li

Doctoral Dissertations

In recent years, deep neural networks (DNNs) are increasingly investigated in the literature to be employed in cyber-physical systems (CPSs). DNNs own inherent advantages in complex pattern identifying and achieve state-of-the-art performances in many important CPS applications. However, DNN-based systems usually require large datasets for model training, which introduces new data management issues. Meanwhile, research in the computer vision domain demonstrated that the DNNs are highly vulnerable to adversarial examples. Therefore, the security risks of employing DNNs in CPSs applications are of concern.

In this dissertation, we study the security of employing DNNs in CPSs from both the data domain …

A User Study Of A Wearable System To Enhance Bystanders’ Facial Privacy, Alfredo J. Perez, Sherali Zeadally, Scott Griffith, Luis Y. Matos Garcia, Jaouad A. Mouloud

Information Science Faculty Publications

The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called …

A Deep-Learning Based Robust Framework Against Adversarial P.E. And Cryptojacking Malware, Faraz Amjad Naseem

FIU Electronic Theses and Dissertations

This graduate thesis introduces novel, deep-learning based frameworks that are resilient to adversarial P.E. and cryptojacking malware. We propose a method that uses a convolutional neural network (CNN) to classify image representations of malware, that provides robustness against numerous adversarial attacks. Our evaluation concludes that the image-based malware classifier is significantly more robust to adversarial attacks than a state-of-the-art ML-based malware classifier, and remarkably drops the evasion rate of adversarial samples to 0% in certain attacks. Further, we develop MINOS, a novel, lightweight cryptojacking detection system that accurately detects the presence of unwarranted mining activity in real-time. MINOS can detect …