The huge increase amount of Cyber Attacks in computer networks emerge essential requirements of intrusion detection system, IDS to monitors the cybercriminals. The inefficient or unreliable IDS can decrease the performance of security services and today world applications and make the ongoing challenges on the Cyber Security and Data mining fields. This paper proposed a new detection system for the cyber-attacks with the ensemble classification of efficient cost sensitive decision trees, CSForest classifier and the least numbers of most relevant features are selected as the additional mechanism to reduce the cost. The standard dataset, NSL-KDD, IDS is used to appraise the results and compare the previous existing systems and state-of-the-art methods. The proposed system outperforms the other existing systems and can be public a new benchmark record for the NSL-KDD datasets of intrusion detection system. The proposed combination of choosing the appropriate classifier and the selection of perfect features mechanism can produce the cost-efficient IDS system for the security world.

Face Recognition plays a major role in the new modern information technology era for security purposes in biometric modalities and has still various challenges in many applications of computer vision systems. Consequently, it is a hot topic research area for both industrial and academic environments and was developed with many innovative ideas to improve accuracy and robustness. Therefore, this paper proposes a recognition system for facial images by using Deep learning strategies to detect a face, extract features, and recognize. The standard facial dataset, FEI is used to prove the effectiveness of the proposed system and compare it with the other previous research works, and the experiments are carried out for different detection methods. The results show that the improved accuracy and reduce time complexity can provide from this system, which is the advantage of the Convolution Neural Network (CNN) than other some of the previous works.