The traditional electrical power grid is currently evolving into the smart grid. Smart grid integrates the traditional electrical power grid with information and communication technologies (ICT). Such integration empowers the electrical utilities providers and consumers, improves the efficiency and the availability of the power system while constantly monitoring, controlling and managing the demands of customers. A smart grid is a huge complex network composed of millions of devices and entities connected with each other. Such a massive network comes with many security concerns and vulnerabilities. In this paper, we survey the latest on smart grid security. We highlight the complexity of the smart grid network and discuss the vulnerabilities specific to this huge heterogeneous network. We discuss then the challenges that exist in securing the smart grid network and how the current security solutions applied for IT networks are not sufficient to secure smart grid networks. We conclude by over viewing the current and needed security solutions for the smart gird.

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Biologists often need to know the set of genes associated with a given set of genes or a given disease. We propose in this paper a classifier system called Monte Carlo for Genetic Network (MCforGN) that can construct genetic networks, identify functionally related genes, and predict gene-disease associations. MCforGN identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature. For a given gene g , the system first extracts the set of genes found within the abstracts of biomedical literature associated with g. It then ranks these genes to determine the ones with high co-occurrences with g . It overcomes the limitations of current approaches that employ analytical deterministic algorithms by applying Monte Carlo Simulation to approximate genetic networks. It does so by conducting repeated random sampling to obtain numerical results and to optimize these results. Moreover, it analyzes results to obtain the probabilities of different genes' co-occurrences using series of statistical tests. MCforGN can detect gene-disease associations by employing a combination of centrality measures (to identify the central genes in disease-specific genetic networks) and Monte Carlo Simulation. MCforGN aims at enhancing state-of-the-art biological text mining by applying novel extraction techniques. We evaluated MCforGN by comparing it experimentally with nine approaches. Results showed marked improvement.

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In recent years, network intrusion detection systems (NIDS) have faced a serious throughput challenge as a result of the rapid increase of network links to 1 and 10 Gbps rates. Consequently, this calls for NIDS to have wire-speed packet processing and real-time detection of malicious traffic. Snort is the most popular NIDS. Snort is an open source software-based NIDS and runs as a single threaded application. Snort processing and detection capabilities can be limited in networks with 1 and 10 Gbps network links. To overcome such a limitation, we present a design and implementation of two layer NIDS for accelerating Snort detection. The design combines hardware and software components whereby Snort operates as the second line of defense after hardware-assisted inspection of packet headers. In our design, Snort's frequently used rules are offloaded from Snort to a NetFPGA-based hardware layer. The NetFPGA implementation is based on Bloom filter to analyze and filter incoming packets with header fields matching those of frequently used rules. The second line of defense will dynamically offload the most frequently triggered rules to the NetFPGA and will only be executed if deep packet analysis is required for the incoming packet. The experimental results show a significant improvement in the CPU usage and an enormous reduction in packet loss when using Snort with NetFPGA filtering.