Intrusion Detection Network Attacks Based on Whale Optimization Algorithm

Abstract

Network intrusion detection is a significant issue faced by the Information technology industry. Hacker's attacks set of techniques cause confusion network and computer systems, therefore, the need for a network penetration detection process became urgent. This paper proposed an optimization method to detect the potential of attacks on network packets effectively by defining unfamiliar patterns in a massive volume of the network traffic. Initially, the packet characteristics are defined as the normal dispersal of attributes from the data packet. This algorithm is used a bio-inspired meta-heuristic technique named whale optimization algorithm (WOA) to detect systems against the attackers. The detection method whale optimization (DMWO) is applied to calculate the process of standard deviation distribution to judge the anomaly of the data packet. The simulation algorithm performed by OPNET and Matlab-2015a to main factors of DMGO to classify the input to check whether any attack is present or not. Finally, the performance of this algorithm is a better flexibility result compared with other algorithms DMCM and IHMM.

Alzheimer’s diseases classification using YOLOv2 object detection technique

“…The model produces incredibly amazing predicted results, as seen in Figure 10. The model predicts an item based [22], [23]; if the probability is less than a specific threshold (here, 0.3), the model predicts that the object does not have Alzheimer's disease [24], [25]. An object's score is a calculation that determines whether or not an object will appear in the expected box.…”