We propose a new traceroute tool, FlashRoute for efficient large-scale topology discovery. FlashRoute reduces the time required for tracerouting the entire /24 IPv4 address space by a factor of three and half compared to previous state of the art. Additionally, we present a new technique to measure hop-distance to a destination using a single probe and uncover a bias of the influential ISI Census hitlist [18] in topology discovery.

Content delivery networks (CDNs) commonly use DNS to map end-users to the best edge servers. A recently proposed EDNS0-Client-Subnet (ECS) extension allows recursive resolvers to include end-user subnet information in DNS queries, so that authoritative DNS servers, especially those belonging to CDNs, could use this information to improve user mapping. In this paper, we study the ECS behavior of ECS-enabled recursive resolvers from the perspectives of the opposite sides of a DNS interaction, the authoritative DNS servers of a major CDN and a busy DNS resolution service. We find a range of erroneous (i.e., deviating from the protocol specification) and detrimental (even if compliant) behaviors that may unnecessarily erode client privacy, reduce the effectiveness of DNS caching, diminish ECS benefits, and in some cases turn ECS from facilitator into an obstacle to authoritative DNS servers' ability to optimize user-to-edge-server mappings.

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.

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.