Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

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Mobile Ad Hoc Network (MANET) suffers from temporary link failures and route changes. Moreover, TCP performs poorly when most packet losses are due to congestion. Most of research performed for improving TCP performance over MANET requires feedback from lower layers. Several attempts have been proposed for a layered TCP improvement. Yet, their percentage enhancements are not satisfactory. In this paper, we explore a new approach to improve TCP performance using a TCP layered approach. The proposed methodology depends on beginning transmission as soon as a failed route is reestablished. It utilizes an adaptive back-off response strategy through which congestion window and slow start threshold values are decreased when an acknowledgement is received. The proposed technique does not require feedback from the network layer. Simulation results showed that this approach had achieved an average performance improvement of 170/0.

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