Mohammed Ali

Universiti Malaysia Pahang (UMP), Fskkp, PhD student in Networking

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PhD student
Phone: +601133433588
Address: Faculty of Computer Systems & Software Engineering
Universiti Malaysia Pahang,
Lebuhraya Tun Razak, 26300 Gambang,
Kuantan, Pahang Darul Makmur

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Princess Nora University

Hanan Hindy

Abertay University

Christos Tachtatzis

University of Strathclyde, Glasgow

Xavier Bellekens

University of Strathclyde, Glasgow

Wolfgang Banzhaf

Memorial University of Newfoundland

Tolga Kaban

Rupam Kumar Sharma

Assam Don Bosco University

Journal of Computer Science IJCSIS

Pritam Sapate

VNIT, Nagpur

Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)

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Papers by Mohammed Ali

A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization

Supervised intrusion detection system is a system that has the capability of learning from exampl... more Supervised intrusion detection system is a system that has the capability of learning from examples about the previous attacks to detect new attacks. Using artificial neural network (ANN)-based intrusion detection is promising for reducing the number of false negative or false positives, because ANN has the capability of learning from actual examples. In this paper, a developed learning model for fast learning network (FLN) based on particle swarm optimization (PSO) has been proposed and named as PSO-FLN. The model has been applied to the problem of intrusion detection and validated based on the famous dataset KDD99. Our developed model has been compared against a wide range of meta-heuristic algorithms for training extreme learning machine and FLN classifier. PSO-FLN has outperformed other learning approaches in the testing accuracy of the learning. INDEX TERMS Fast learning network, KDD Cup 99, intrusion detection system, particle swarm optimization.

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A Review of Challenges and Security Risks of Cloud Computing

—Cloud computing has been an attention in the new era of the IT technologies as there is an incre... more —Cloud computing has been an attention in the new era of the IT technologies as there is an increase demand in the services or utility computing all over the wide world web. Security risk resulting from resource sharing throughout the cloud computing becomes one of the most challenging concerns in providing powerful processing and storage as on-demand services. Taking the advantage of low cost derived from the increase in efficiency and performance facilitated by cloud computing, governments and organizations around the globe are motivated to build or migrate to the cloud. However, there are still many technical issues relating to the features of cloud computing and the provision of quality service, leading to a delay in adopting cloud computing. This review paper highlights the security risks and challenges of cloud computing and study the security requirements for cloud computing. The primary aim of this review is to classify the security risks and challenges related to the different forms of cloud computing (SaaS, PaaS and IaaS).

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The Design Trends of Keystream Generator for Stream Cipher for High Immunity Attacks

Due to the latest changes in observing the external network related threats within the stream cip... more Due to the latest changes in observing the external network related threats within the stream cipher, it become necessary to address these threats in order to identify the generator suitable for avoiding such threats. In this paper, the researcher addresses the current threats of immunity attacks in the stream cipher. Such attacks are resulted from the correlation within the key stream's multiplexer. The key stream generators are also introduced in order to clarify its working process in avoiding attacks. After all, a comparison of key stream generators are resulted where it can be used as guidelines for other researches in designing key stream generator for high immunity attacks in the cipher.

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REVIEW ON HYBRID EXTREME LEARNING MACHINE AND GENETIC ALGORITHM TO WORK AS INTRUSION DETECTION SYSTEM IN CLOUD COMPUTING 5 PUBLICATIONS 3 CITATIONS SEE PROFILE

Today security is a major challenge, many tools provided in this issue of instant firewall and in... more Today security is a major challenge, many tools provided in this issue of instant firewall and intrusion-detection
system (IDS) .IDS is one of the powerful tools in the security. IDS works depend on the fastest to detect and accuracy of
detection. In other hand the IDS facing problem with high false alarm rate. This work proposes to solve this problem by
hybrid between the Extreme Learning Machine (ELM) and Genetic Algorithm (GA). ELM work depends on two
parameters weight (W) and biases (B) that will provide by GA. ELM has set of properties that make it attractive to be
adopted for intrusion detection system in cloud environment. our work approach and integrate GA ELM work as IDS with
high hopes detection rate and accuracy to the second problem and suggest dividing the training mode for virtual training
and virtual testing to ensure selecting a best classifier.

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A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization

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A Review of Challenges and Security Risks of Cloud Computing

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The Design Trends of Keystream Generator for Stream Cipher for High Immunity Attacks

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REVIEW ON HYBRID EXTREME LEARNING MACHINE AND GENETIC ALGORITHM TO WORK AS INTRUSION DETECTION SYSTEM IN CLOUD COMPUTING 5 PUBLICATIONS 3 CITATIONS SEE PROFILE

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