Predicting future community intrusions using a novel type and encryption mechanism architecture for attack node mitigation

Abstract

The recent exponential rise in the number of cyber-attacks has demanded intensive study into community intrusion detection, prediction, and mitigation systems. Even though there are a variety of intrusion detection technologies available, predicting future community intrusions is still a work in progress. Existing approaches rely on statistical and/or superficial device mastery techniques to solve the problem, and as a result, feature selection and engineering are required. The truth is that no single classifier can provide the highest level of accuracy for all five types of training data set. Cyber-attack detection is a technique for detecting cyber-attacks as they emerge on a laptop or network device, intending to compromise the gadget's security. As a result, using a novel type and encryption mechanism, this paper offered a unique architecture for attack node mitigation. The input UNSW-NB15 dataset is first acquired and divided into training and testing statistics. First and foremost, the information is pre-processed and capabilities are retrieved in the training section. The Taxicab Woodpecker Mating Algorithm (TWMA) is then used to select the critical characteristics. The attacked and non-attacked information are then classified using the BRELU-ResNet (Bernoulli's Leaky Rectified Linear Unit - Residual Neural Community) classifier. The encrypted at Ease Hash Probability-Based Elliptic-Curve Cryptography (ESHP-ECC) technique is used to encrypt the ordinary facts, which are subsequently kept in the security log report. Following that, using Euclidean distance, the shortest course distance is estimated. Finally, the records are decrypted using a set of principles known as Decrypted Relaxed Hash Probability-Based Elliptic-Curve Cryptography (DSHP-ECC). If the input appears in the log file during testing, it is regarded as attacked data and is prevented from being transmitted. If it isn't found, the procedure of detecting cyber-attacks continues.