Detection of injection attacks on in-vehicle network using data analytics

We investigate the possibility of detection of injection attacks using data analytics techniques in this thesis. The automotive industry is innovating the modern vehicles towards connectivity by interfacing them with various external entities. These entities are exposing the automobile to cyber attacks instead of ensuring its safety. Therefore it is important to consider the security aspect while developing these interfaces. Firstly, we try understand the automobile network architecture and the possible security threats associated with it. Next, we examine the various possible cyber-attacks on automobiles described in the literature. We experiment and analyze the attack scenarios by performing injection attacks on a vehicle. We collect the data during the injection attacks and apply multiple data analysis techniques. These techniques build a model based on data during normal operation. The observations from the data collected during injection attacks is fit into these techniques. The data points that do not fit the model are termed as attack points. Finally we examine and analyze the results and their accuracy in detecting injection attacks

ConnectIt: A Framework for Static and Incremental Parallel Graph Connectivity Algorithms

Connected components is a fundamental kernel in graph applications due to its usefulness in measuring how well-connected a graph is, as well as its use as subroutines in many other graph algorithms. The fastest existing parallel multicore algorithms for connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 37.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. Finally, to guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies in terms of their work and locality

Parallel Index-Based Structural Graph Clustering and Its Approximation

SCAN (Structural Clustering Algorithm for Networks) is a well-studied, widely used graph clustering algorithm. For large graphs, however, sequential SCAN variants are prohibitively slow, and parallel SCAN variants do not effectively share work among queries with different SCAN parameter settings. Since users of SCAN often explore many parameter settings to find good clusterings, it is worthwhile to precompute an index that speeds up queries. This paper presents a practical and provably efficient parallel index-based SCAN algorithm based on GS*-Index, a recent sequential algorithm. Our parallel algorithm improves upon the asymptotic work of the sequential algorithm by using integer sorting. It is also highly parallel, achieving logarithmic span (parallel time) for both index construction and clustering queries. Furthermore, we apply locality-sensitive hashing (LSH) to design a novel approximate SCAN algorithm and prove guarantees for its clustering behavior. We present an experimental evaluation of our algorithms on large real-world graphs. On a 48-core machine with two-way hyper-threading, our parallel index construction achieves 50--151$\times$ speedup over the construction of GS*-Index. In fact, even on a single thread, our index construction algorithm is faster than GS*-Index. Our parallel index query implementation achieves 5--32$\times$ speedup over GS*-Index queries across a range of SCAN parameter values, and our implementation is always faster than ppSCAN, a state-of-the-art parallel SCAN algorithm. Moreover, our experiments show that applying LSH results in faster index construction while maintaining good clustering quality

Conformational dynamics associated with calcium binding to calcium transducers

The Ca2+ association to calcium binding proteins (CaBPs) represents an essential step in Ca2+ signal transduction. This study presents a characterization of Ca interactions with two CaBPs, calmodulin and DREAM, using time-resolved photothermal and fluorescence techniques. Calcium binding to the calmodulin C-terminal domain is associated with a volume change of 40 mL mol-1and an enthalpy change of 35 ± 16 kcal mol-1. These parameters are consistent with the Ca2+ triggered exposure of hydrophobic patches on the calmodulin surface. Also, the rate limiting step for Ca2+ binding to calmodulin is the closed-to-open transition of the C-terminal domain that occurs with a lifetime of 400 μs. Unlike calmodulin, DREAM exists in a dynamic equilibrium of two conformations and Ca2+ binding shifts the equilibrium towards a more compact conformation. These data clearly demonstrate that conformational dynamics play a crucial role in the transmission of Ca2+ signals

Impact of Mobility models on Mobile Sensor Networks

Wireless sensor networks (WSN) is an emerging technology, finds variety of applications in military, movement tracking, industries and medical fields. WSN are self configurable, self healing networks. In mobile sensor network, (MSN) nodes are free to move with wireless links without infrastructure. In this paper, we have studied the impact of various mobility models with AODV and DSDV routing protocols and have compared the throughput of the models. Parameters such as loss ratio, hop counts, velocity of the nodes are analyzed by varying the node density using various mobility models and routing protocols