Browsing by Author "Assamnew, Fitsum"

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    Accelaration of Preprocessors of the Snort Network Intrusion Detection System Using General Purpose Graphics Processing Unit
    (Addis Ababa University, 2015-04) Yihunie, Simegnew; Assamnew, Fitsum
    Advances in networking technologies enable interactions and communications at high speeds and large data volumes. But, securing data and the infrastructure has become a big issue. Intrusion Detection Systems such as Snort play an important role to secure the network. Intrusion detection systems are used to monitor networks for unauthorized access. Snort has a packet decoder, pre-processor, detection engine and an alerting system. The detection engine is the most compute intensive part followed by the pre-processor. Previous work has shown how general purpose graphics processing units(GP-GPU) can be used to accellerate the detection engine. This work focused on the pre-processors of Snort, speci cally, the stream5 pre-processor as pro ling revealed it to be the most time consuming of the pre-processors. The analysis shows that the individual implementation of stream5 using Compute Uni ed Device Architecture(CUDA) achieved up to ve times speed up over the baseline. Also, an over all 15.5 percent speed up on the Defense Advanced Research Projects Agency(DARPA) intrusion detection system dataset was observed when integrated in Snort.
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    Distributed Denial of Service Attack Detection: A Hybrid Intelligent System Approach
    (Addis Ababa University, 2008-04) Assamnew, Fitsum; Raimond, Kumudha (PhD)
    The occurrence of distributed denial of service (DDoS) attacks has become more frequent in today’s network environment. Detecting these attacks would prevent the unnecessary utilization of resources which otherwise could have been used to service legitimate users. This requires the implementation of an effective DDoS detection system. Many researches have proposed a number of DDoS detection systems and one of the recent ideas is to use the hybrid intelligent systems for the effective detection of DDoS attacks. In this work, adaptive neuro-fuzzy inference system (ANFIS) has been used as the hybrid intelligent system for the detection of DDoS attacks. An experimental environment has been setup to collect the normal and attack traffic data for training and testing purposes. A detection system has been proposed having ANFIS as its detection core. The proposed system has been tested in the detection of TCP SYN flooding attack. It is found that ANFIS is able to classify the TCP SYN DDoS data with very good precision.
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    Performance Improvement of Multi-Scale Modeling of Concrete Performance Solver Using Hybrid Cpu-Gpu System
    (Addis Ababa University, 2015-04) Felleke, Lina; Assamnew, Fitsum
    The multi-scale modeling of concrete performance solver is a tool developed at University of Tokyo. It is used to help engineers predict the material and structural properties of concrete structures under coupled mechanistic and environmental actions. The current implementation of multi-scale modeling of concrete performance solver is developed for Central Processing Unit (CPU) and CPU clusters and has longer runtimes. In this work, we present a CPU- Graphics Processing Unit (GPU) hybrid implementation of the tool. Changes were made to the LU factorization module by moving the computations to the GPU after profiling revealed it to be compute intensive. Tests conducted on the modified tool using real world sample concrete structure show that using the hybrid CPU-GPU implementation improved the performance by 35.29% on single node and 37.5% on cluster.