Ashraf B. El-Sisi

Work place: Computer Science Department, Faculty of Computers and Information, Menoufia University, Egypt



Research Interests: Autonomic Computing, Systems Architecture, Information Security, Network Security, Security Services, Distributed Computing


Prof. Dr. Ashraf B. El-Sisi received the B.Sc. and M.Sc. degrees in Electronic Engineering and Computer Science Engineering from Menoufia University, Faculty of Electronic Engineering in 1989 and 1995, respectively and received his PhD degree in Computer Engineering & Control from Zagazig University, Faculty of Engineering in 2001. His current research interests include cloud computing, privacy-preserving data mining, and intelligent systems.

Author Articles
An Efficiency Optimization for Network Intrusion Detection System

By Mahmoud M. Sakr Medhat A. Tawfeeq Ashraf B. El-Sisi

DOI:, Pub. Date: 8 Oct. 2019

With the enormous rise in the usage of computer networks, the necessity for safeguarding these networks is also increased. Network intrusion detection systems (NIDS) are designed to monitor and inspect the activities in a network. NIDS mainly depends on the features of the input network data as these features give information on the behaviour nature of the network traffic. The irrelevant and redundant network features negatively affect the efficacy and quality of NIDS, particularly its classification accuracy, detection time and processing complexity. In this paper, several feature selection techniques are applied to optimize the efficiency of NIDS. The categories of the applied feature selection techniques are the filter, wrapper and hybrid. Support vector machine (SVM) is employed as the detection model to classify the network connections behaviour into normal and abnormal traffic. NIDS is trained and tested on the benchmark NSL-KDD dataset. The performance of the applied feature selection techniques is compared with each other and the results are discussed. Evaluation results demonstrated the superiority of the wrapper techniques in providing the highest classification accuracy with the lowest detection time and false alarms of the NIDS.

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Network Intrusion Detection System based PSO-SVM for Cloud Computing

By Mahmoud M. Sakr Medhat A. Tawfeeq Ashraf B. El-Sisi

DOI:, Pub. Date: 8 Mar. 2019

Cloud computing provides and delivers a pool of on-demand and configurable resources and services that are delivered across the usage of the internet. Providing privacy and security to protect cloud assets and resources still a very challenging issue, since the distributed architecture of the cloud makes it vulnerable to the intruders. To mitigate this issue, intrusion detection systems (IDSs) play an important role in detecting the attacks in the cloud environment. In this paper, an anomaly-based network intrusion detection system (NIDS) is proposed which can monitor and analyze the network traffics flow that targets a cloud environment. The network administrator should be notified about the nature of these traffics to drop and block any intrusive network connections. Support vector machine (SVM) is employed as the classifier of the network connections. The binary-based Particle Swarm Optimization (BPSO) is adopted for selecting the most relevant network features, while the standard-based Particle Swarm Optimization (SPSO) is adopted for tuning the SVM control parameters. The benchmark NSL-KDD dataset is used as the network data source to build and evaluate the proposed system. Acceptable evaluation results state that the proposed system is characterized by detecting the intrusive network connections with high detection accuracy and low false alarm rates (FARs).

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Fast Mapping Algorithm from WSDL to OWL-S

By Ashraf B. El-Sisi

DOI:, Pub. Date: 8 Aug. 2014

Recently semantic web services represent the most technology developed for machine to machine interaction. The problem of discovering and selecting the most suitable web service represents a challenge for semantic web services. In this paper performance evaluation of mapping algorithm from web services annotations (WSDL) to semantic annotations (OWL-S) based on ontology search engine is presented. During mapping process primitive type remains without change. The complex type are converted to OWL ontology by extracted them and passing to ontology search and standardization process without need of conversion into temporary ontology. The keywords extracted in the linguistic search phase and are extended using word net. The mapping algorithm and its modification are implemented in Java and evaluated by 310 files WSDL. The output results of two algorithms are identical. But the proposed modified algorithm is faster than mapping algorithm.

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Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

By Arabi Keshk Ashraf B. El-Sisi Medhat A. Tawfeeq

DOI:, Pub. Date: 8 Apr. 2014

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different computing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimization algorithm for load balancing compared with different scheduling algorithms has been proposed. Ant Colony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. The main contribution of our work is to balance the system load while trying to minimizing the make span of a given tasks set. The load balancing factor, related to the job finishing rate, is proposed to make the job finishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simulated using Cloudsim toolkit package. Experimental results showed that, the proposed algorithm outperformed scheduling algorithms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).

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