Novel SHP-ECC Mechanism Architecture for Attack Node Mitigation and to Predict Future Community Intrusions
Keywords:Cyber-attack detection, Attack node mitigation, BAIT approaches, Feature extraction, Future Community Intrusions, SHP-ECC Mechanism
Purpose: Because of the apparent rapid advancement in the field of information and communication technology and its constant connection to the internet, customer and organizational data have become vulnerable to cyber-attacks, necessitating the explanation of solutions to ensure the security and protection of information throughout the industry. Today, it is critical for governments and major corporations to implement cybersecurity systems to ensure the confidentiality and security of data in the face of cyber-attacks. As community-based fully systems have become more important in today's society, they've become targets for malicious actions, prompting both industry and the research community to place a greater emphasis on resolving community intrusion detection difficulties. In network intrusion detection challenges, gadget examining algorithms have proven to be a valuable tool.
Design/Methodology/Approach: This research provided a fully unique architecture for attack node mitigation as a result of the use of a novel type and encryption mechanism. First, the UNSW-NB15 dataset is received and separated into training and testing data. Within the Training section, information is first and foremost pre-processed, and capabilities are extracted. The relevant features are then chosen using the Taxicab Woodpecker Mating algorithm (TWMA). The BRELU-ResNet classifier is then used to classify the attacked and non-attacked data. The typical statistics are encrypted using the ESHP-ECC method, which is then saved in the security log report. Following that, the shortest distance will be calculated using Euclidean distance. Finally, the information is decrypted utilizing a set of ideas known as DSHP-ECC. If the entry appears in the log record while testing, it is marked as attacked statistics and will not be communicated. The method of detecting cyber-assaults will continue if it is not detected.
Findings/Result: The analysis is based on the UNSW-NB 15 dataset, which shows that the proposed approach achieves an excessive security level of 93.75 percent.
Originality/Value: This experimental-based research article examines the malicious activities in the cyberspace and mitigated by using a SHP-ECC mechanism.
Paper Type: Research Article