The use of transformers for vision tasks has challenged the traditional dominant role of convolutional neural networks (CNN) in computer vision (CV). For image classification tasks, Vision Transformer (ViT) effectively establishes spatial relationships between patches within images, directing attention to important areas for accurate predictions. However, similar to CNNs, ViTs are vulnerable to adversarial attacks, which mislead the image classifier into making incorrect decisions on images with carefully designed perturbations. Moreover, adversarial patch attacks, which introduce arbitrary perturbations within a small area (usually less than 3% of pixels), pose a more serious threat to ViTs. Even worse, traditional detection methods, originally designed for CNN models, are impractical or suffer significant performance degradation when applied to ViTs, and they generally overlook patch attacks.In this paper, we propose ViTGuard as a general detection method for defending ViT models against adversarial attacks, including typical attacks where perturbations spread over the entire input (Lpnorm attacks) and patch attacks. ViTGuard uses a Masked Autoencoder (MAE) model to recover randomly masked patches from the unmasked regions, providing a flexible image reconstruction strategy. Then, threshold-based detectors leverage distinctive ViT features, including attention maps and classification (CLS) token representations, to distinguish between normal and adversarial samples. The MAE model does not involve any adversarial samples during training, ensuring the effectiveness of our detectors against unseen attacks. ViTGuard is compared with seven existing detection methods under nine attacks across three datasets with different sizes. The evaluation results show the superiority of ViTGuard over existing detectors. Finally, considering the potential detection evasion, we further demonstrate ViTGuard’s robustness against adaptive attacks for evasion.

Cyber reconnaissance is the process of gathering information about a target network for the purpose of compromising systems within that network. Network-based deception has emerged as a promising approach to disrupt attackers' reconnaissance efforts. However, limited work has been done so far on measuring the effectiveness of network-based deception. Furthermore, given that Software-Defined Networking (SDN) facilitates cyber deception by allowing network traffic to be modified and injected on-the-fly, understanding the effectiveness of employing different cyber deception strategies is critical. In this paper, we present a model to study the reconnaissance surface of a network and model the process of gathering information by attackers as interactions with a cyber defensive system that may use deception. To capture the evolution of the attackers' knowledge during reconnaissance, we design a belief system that is updated by using a Bayesian inference method. For the proposed model, we present two metrics based on KL-divergence to quantify the effectiveness of network deception. We tested the model and the two metrics by conducting experiments with a simulated attacker in an SDN-based deception system. The results of the experiments match our expectations, providing support for the model and proposed metrics.

Advanced targeted cyber attacks often rely on reconnaissance missions to gather information about potential targets, their characteristics and location to identify vulnerabilities in a networked environment. Advanced network scanning techniques are often used for this purpose and are automatically executed by malware infected hosts. In this paper, we formally define network deception to defend reconnaissance and develop a reconnaissance deception system, which is based on software defined networking, to achieve deception by simulating virtual topologies. Our system thwarts network reconnaissance by delaying the scanning techniques of adversaries and invalidating their collected information, while limiting the performance impact on benign network traffic. By simulating the topological as well as physical characteristics of networks, we introduce a system which deceives malicious network discovery and reconnaissance techniques with virtual information, while limiting the information an attacker is able to harvest from the true underlying system. This approach shows a novel defense technique against adversarial reconnaissance missions which are required for targeted cyber attacks such as advanced persistent threats in highly connected environments. The defense steps of our system aim to invalidate an attackers information, delay the process of finding vulnerable hosts and identify the source of adversarial reconnaissance within a network.

Today, people have the opportunity to opt-in to usage-based automotive insurances for reduced premiums by allowing companies to monitor their driving behavior. Several companies claim to measure only speed data to preserve privacy. With our elastic pathing algorithm, we show that drivers can be tracked by merely collecting their speed data and knowing their home location, which insurance companies do, with an accuracy that constitutes privacy intrusion. To demonstrate the algorithm's real-world applicability, we evaluated its performance with datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. Our algorithm predicted destinations with error within 250 meters for 14% traces and within 500 meters for 24% traces in the New Jersey dataset (254 traces). For the Seattle dataset (691 traces), we similarly predicted destinations with error within 250 and 500 meters for 13% and 26% of the traces respectively. Our work shows that these insurance schemes enable a substantial breach of privacy.

This paper studies the security and memorability of free-form multitouch gestures for mobile authentication. Towards this end, we collected a dataset with a generate-test-retest paradigm where participants (N=63) generated free-form gestures, repeated them, and were later retested for memory. Half of the participants decided to generate one-finger gestures, and the other half generated multi-finger gestures. Although there has been recent work on template-based gestures, there are yet no metrics to analyze security of either template or free-form gestures. For example, entropy-based metrics used for text-based passwords are not suitable for capturing the security and memorability of free-form gestures. Hence, we modify a recently proposed metric for analyzing information capacity of continuous full-body movements for this purpose. Our metric computed estimated mutual information in repeated sets of gestures. Surprisingly, one-finger gestures had higher average mutual information. Gestures with many hard angles and turns had the highest mutual information. The best-remembered gestures included signatures and simple angular shapes. We also implemented a multitouch recognizer to evaluate the practicality of free-form gestures in a real authentication system and how they perform against shoulder surfing attacks. We discuss strategies for generating secure and memorable free-form gestures. We conclude that free-form gestures present a robust method for mobile authentication.

This is a video demonstration for a full paper available in MobiSys'14 proceedings http://dx.doi.org/10.1145/2594368.2594375. The video demonstrates several forms of authentication on a common tablet, and compares them to our method for gesture-based authentication. Our method measures the security and memorability of user generated free-form gestures by estimating the mutual information of repeated gestures. We show examples of such gestures with high and low mutual information content. We also show what information from each is visible to a shoulder surfing attacker, and describe how our system is resistant to such an attack.

This paper describes the signal-to-noise ratio (SNR) estimation based on the preamble in tri-sectored OFDM systems. Several SNR estimators from previous literature suffer degradation in several important fading channel models. This paper proposes an estimation method that takes advantage of the fact that the preamble sequence symbols are assigned to every third subcarrier for each BS sector. Typically the closer the MS is to the BS, the more corrupted the noise power estimate is due to the higher signal power term in the estimator. The proposed method lowers the noise estimate by exploiting the differences in the powers of the orthogonal preambles received from the three spatially separated BS sectors. The simulation results show that the performance of our estimator improves as the power received from the two non-serving sectors decreases relative to the received power of the serving sector.