An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

Intrusion Detection System (IDS) is a network security mechanism that analyses all users' and applications' traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the input characteristics for the IDS from the input data. Then, the optimized features are utilized by the LTH-CNN, which returns the attacked and non-attacked data. After that, the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security phases. To prevent the system from cyber-attack, the Source and Destination IP address is converted into a complex binary format named 1's Complement Reverse Shift Right (CRSR), where, in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography (PXORP-ECC) Algorithm to improve the data security. The Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset and real-time sensor are used to train and evaluate the proposed LTH-CNN. The suggested model is evaluated based on accuracy, sensitivity, and specificity, which outperformed the existing IDS methods, according to the results of the experiments.