A harmony search-based feature selection technique for cloud intrusion detection

Recently cloud computing has enjoyed widespread patronage due to its economy of scale and flexibility. However Cloud computing is confronted with security challenges. Intrusion detection can be used to protect computer resources from unauthorized access. However the presence of insignificant features in Intrusion Detection dataset may have a negative effect on the accuracy of Intrusion Detection System (IDS). Feature selection is utilized to remove noisy and insignificant attribute to improve IDS performance. However, existing feature selection techniques proposed for cloud IDS cannot guarantee optimal performance. Therefore, this research article proposes a Harmony Search based feature selection technique to improve the performance of cloud IDS. The attributes selected were assessed using Random Forest classifier and experimental results of the Harmony Search based technique achieved an attack detection rate of 79% and a false alarm rate of 0.012%. In addition performance comparison shows that the proposed Harmony search outperforms existing feature selection technique proposed for cloud IDS

Application of Genetic Algorithms in the Construction of Invertible Substitution Boxes

Existing literature shows that genetic algorithms can be successfully used for automated construction of S-boxes. In this paper we show the usage of genetic algorithm, more specifically NSGA-II, as an aid in designing and testing of invertible substitution boxes which are special case of substitution boxes. Many cryptographic properties of Sboxes are often contradicting each other. It is therefore difficult to find an optimal solution. NSGA-II proved to be a valuable tool in finding a range of solutions from which we can later select an appropriate S-box for a cipher. We also show that we can use NSGA-II to test integration of S-boxes with a cipher and automatically reject S-boxes which make the cipher weak