Biblio

Video analytics has many applications in traffic control, security monitoring, action/event analysis, etc. With the adoption of deep neural networks, the accuracy of video analytics in video streams has been greatly improved. However, deep neural networks for performing video analytics are compute-intensive. In order to reduce processing time, many systems switch to the lower frame rate or resolution. State-of-the-art switching approaches adjust configurations by profiling video clips on a large configuration space. Multiple configurations are tested periodically and the cheapest one with a desired accuracy is adopted. In this paper, we propose a method that adapts the configuration by analyzing past video analytics results instead of profiling candidate configurations. Our method adopts a lower/higher resolution or frame rate when objects move slow/fast. We train a model that automatically selects the best configuration. We evaluate our method with two real-world video analytics applications: traffic tracking and pose estimation. Compared to the periodic profiling method, our method achieves 3%-12% higher accuracy with the same resource cost and 8-17x faster with comparable accuracy.

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users' identity. Since structure-based de-anonymization attacks can reveal users' identity's even when the graph is simply anonymized by employing naïve ID removal, recently, k- anonymity is proposed to secure users' privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides k- anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have k- anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.

BGP is known to have many security vulnerabilities due to the very nature of its underlying assumptions of trust among independently operated networks. Most prior efforts have focused on attacks that can be addressed using traditional cryptographic techniques to ensure authentication or integrity, e.g., BGPSec and related works. Although augmenting BGP with authentication and integrity mechanisms is critical, they are, by design, far from sufficient to prevent attacks based on manipulating the complex BGP protocol itself. In this paper, we identify two serious attacks on two of the most fundamental goals of BGP—to ensure reachability and to enable ASes to pick routes available to them according to their routing policies—even in the presence of BGPSec-like mechanisms. Our key contributions are to 1 formalize a series of critical security properties, 2 experimentally validate using commodity router implementations that BGP fails to achieve those properties, 3 quantify the extent of these vulnerabilities in the Internet's AS topology, and 4 propose simple modifications to provably ensure that those properties are satisfied. Our experiments show that, using our attacks, a single malicious AS can cause thousands of other ASes to become disconnected from thousands of other ASes for arbitrarily long, while our suggested modifications almost completely eliminate such attacks.