Biblio

At present, machine learning (ML) algorithms are essential components in designing the sophisticated intrusion detection system (IDS). They are building-blocks to enhance cyber threat detection and help in classification at host-level and network-level in a short period. The increasing global connectivity and advancements of network technologies have added unprecedented challenges and opportunities to network security. Malicious attacks impose a huge security threat and warrant scalable solutions to thwart large-scale attacks. These activities encourage researchers to address these imminent threats by analyzing a large volume of the dataset to tackle all possible ranges of attack. In this proposed method, we calculated the fitness value of each feature from the population by using a genetic algorithm (GA) and selected them according to the fitness value. The fitness values are presented in hierarchical order to show the effectiveness of problem decomposition. We implemented Support Vector Machine (SVM) to verify the consistency of the system outcome. The well-known NSL-knowledge discovery in databases (KDD) was used to measure the performance of the system. From the experiments, we achieved a notable classification accuracies using a SVM of the current state of the art intrusion detection.

Security is becoming a major concern in computing. New techniques are evolving every day; one of these techniques is Hash Visualization. Hash Visualization uses complex random generated images for security, these images can be used to hide data (watermarking). This proposed new technique improves hash visualization by using genetic algorithms. Genetic algorithms are a search optimization technique that is based on the evolution of living creatures. The proposed technique uses genetic algorithms to improve hash visualization. The used genetic algorithm was away faster than traditional previous ones, and it improved hash visualization by evolving the tree that was used to generate the images, in order to obtain a better and larger tree that will generate images with higher security. The security was satisfied by calculating the fitness value for each chromosome based on a specifically designed algorithm.