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We present a novel implementation of the pairwise DNA sequence alignment problem other than the Dynamic programming solution presented by Smith Waterman Algorithm. The proposed implementation uses CUDA; the parallel computing platform and programming model invented by NVIDIA. The main idea of the proposed implementation is assigning different nucleotide weights then merging the subsequences of match using the GPU Architecture according to predefined rules to get the optimum local alignment. We parallelize the whole solution for the pairwise DNA sequence alignment using CUDA and compare the results against a similar semi-parallelized solution and a traditional SmithWaterman implementation on traditional processors; Experimental results demonstrate a considerable reduction in the running time.

Most of the MAC protocols proposed for the wireless sensor networks (WSN) assume sensor nodes to be static and therefore they usually fail or provide very bad network performance in mobile sensor networks. Since WSN mobile applications have become popular nowadays, there is a need for MAC protocols that consider mobility. In this paper, we propose a mobility-aware MAC protocol for WSN that can work with satisfactory performance in both stationary and mobile sensor networks. Furthermore, most of the WSN mobile applications are considered critical ones (e.g. a patient assistance system which monitors patients' health via wearable bio-sensors). Such applications require very quick responses. So, in addition to handling mobility, the proposed MAC protocol considers the problem of latency as well. In summary, this paper proposes a WSN MAC protocol (MD-SMAC) that is considered to be mobility-aware, delay-sensitive and provides satisfactory level of energy efficiency. In addition, we study the performance of the proposed MD-SMAC protocol by simulating it using the NS-2 simulator and comparing it to other WSN MAC protocols. The results show that the MD-SMAC protocol outperforms other existing WSN MAC protocols in terms of mobility-handling, delay-reduction, and energy-efficiency in scenarios involving mobile sensors.

The Session Initiation Protocol (SIP) is one of the most common protocols that are used for signaling function in Voice over IP (VoIP) networks. The SIP protocol is very popular because of its flexibility, simplicity, and easy implementation, so it is a target of many attacks. In this paper, we propose a new system to detect the Denial of Service (DoS) attacks (i.e. malformed message and invite flooding) and Spam over Internet Telephony (SPIT) attack in the SIP based VoIP networks using a linear Support Vector Machine with l1 regularization (i.e. l1-SVM) classifier. In our approach, we project the SIP messages into a very high dimensional space using string based n-gram features. Hence, a linear classifier is trained on the top of these features. Our experimental results show that the proposed system detects malformed message, invite flooding, and SPIT attacks with a high accuracy. In addition, the proposed system outperformed other systems significantly in the detection speed.

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