Machine learning algorithms have been proven to be vulnerable to a special type of attack in which an active adversary manipulates the training data of the algorithm in order to reach some desired goal. Although this type of attack has been proven in previous work, it has not been examined in the context of a data stream, and no work has been done to study a targeted version of the attack. Furthermore, current literature does not provide any metrics that allow a system to detect these attack while they are happening. In this work, we examine the targeted version of this attack on a Support Vector Machine(SVM) that is learning from a data stream, and examine the impact that this attack has on current metrics that are used to evaluate a models performance. We then propose a new metric for detecting these attacks, and compare its performance against current metrics.

Small, low-cost, wireless cameras are becoming increasingly commonplace making surreptitious observation of people more difficult to detect. Previous work in detecting hidden cameras has only addressed limited environments in small spaces where the user has significant control of the environment. To address this problem in a less constrained scope of environments, we introduce the concept of similarity of simultaneous observation where the user utilizes a camera (Wi-Fi camera, camera on a mobile phone or laptop) to compare timing patterns of data transmitted by potentially hidden cameras and the timing patterns that are expected from the scene that the known camera is recording. To analyze the patterns, we applied several similarity measures and demonstrated an accuracy of over 87% and and F1 score of 0.88 using an efficient threshold-based classification. Furthermore, we used our data set to train a neural network and saw improved results with accuracy as high as 97% and an F1 score over 0.95 for both indoors and outdoors settings. From these results, we conclude that similarity of simultaneous observation is a feasible method for detecting hidden wireless cameras that are streaming video of a user. Our work removes significant limitations that have been put on previous detection methods.

In open environments, such as mobile peer-to-peer systems, participants may need to access resources from unknown users. A critical security concern in such systems is the access of faulty resources, thereby wasting the requester's time and energy and possibly causing damage to her system. A common approach to mitigating the problem involves reputation mechanisms; however, since reputation relies on cooperation, a reputation mechanism's effectiveness can be significantly diminished in hostile environments. Reputation systems also require substantial communication among peers leading to: i) vulnerability to errors caused by intermittent connectivity; ii) message delivery disruptions caused by malicious peers; and iii) energy sapping message overheads. In this paper, we present AREX, a low-cost, adaptive mechanism designed to provide security for peers in hostile and uncertain environments, which are common in mobile P2P systems. AREX features an adaptive exploration strategy that increases the system's utility for benign peers and decreases the systempsilas utility for malicious peers. AREX reduces vulnerabilities and energy costs by operating without communication between peers. Through simulation, we demonstrate AREX's ability to reduce energy costs, protect benign peers, and diminish malicious peers' motivation to attack in a variety of hostile environments.