What is it about?
With the explosive growth and increasing, the complexity of network applications, malware attacks such as worm attacks against networks is critical. Although of the evolution of worm detection techniques, worms are still the most malware threats attacking computer systems. Early detection of unknown worms is still a problem.
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Why is it important?
Swarm Intelligence (SI) in recent patents seeks inspiration in the behavior of swarms of insects or other animals such as ants. SI is applied in other fields with success. We used it in the field of worm detection. Artificial neural networks may either be used to gain an understanding of biological neural networks or for solving artificial intelligence problems without necessarily creating a model of a real biological system.
Perspectives
This paper introduces a system for detecting unknown worms based on the collected information from the local victim using Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN). This system can detect unknown worms effectively in both small and large size networks. In addition, this system produces predictions of the infection percentage in the network. This prediction mechanism supports the network administrator in the decision-making process to respond quickly to worm propagation accurately.
Dr Tarek Salah Sobh
Read the Original
This page is a summary of: Hybrid Swarm Intelligence and Artificial Neural Network for Mitigating Malware Effects, Recent Patents on Computer Science, June 2014, Bentham Science Publishers,
DOI: 10.2174/2213275907666140612003641.
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