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Abstract

In today???s society, information and communications technology (ICT) is the force that drives prosperity and provides a higher standard of living. All other e-services and infrastructures tend to play a major role in our daily life and global economy. The growing dependence on such systems, however, has increased their vulnerability to cyber attacks. Any failure to these systems typically would lead to a huge impact, not only on businesses, but also human life, that depends on such interconnected systems. The growing potential for telecommunications network infrastructures problems stems from their nature of openness. A successful attempt for a network attack to a particular organization???s network could have devastating effects on the security of the organisation. In this paper we propose an innovative way to counteract distributed protocols attacks such as distributed denial of service (DDoS) attacks using intelligent fuzzy agents

The security of Cyber Physical Systems and any digital forensic investigations into them will be highly dependent on data that is stored and processed in the Cloud. This paper looks at a number of the issues that will need to be addressed if this environment is to be trusted to securely hold both system critical and personal information and to enable investigations into incidents to be undertaken

The growing dependence of modern society on telecommunication and information networks and e‐type systems has become inevitable. However, those types of systems are vulnerable to malicious attacks. The speed and automation in network attack techniques continue to increase. An achievable automated attack or unauthorised access to a particular organization network could lead to devastating effects on its reputation and imminent loss of life. In this paper an innovative way is proposed to detect network attacks of a distributed nature such as denial of service (DoS) attacks. The proposed scheme is mainly based on neuro‐fuzzy intelligence in order to learn and determine the fuzzy parameter functions that represent network traffic behaviour. Neuro‐fuzzy agents combine the features of fuzzy logic and neural networks and they have been proposed to overcome the limitations of human expertise in defining fuzzy membership functions, especially for complex environments, such as information networks.