A Sophisticated Deep Learning Framework of Advanced Techniques to Detect Malicious Users in Online Social Networks

Malicious user detection is a cybersecurity exploration domain because of the emergent jeopardies of data breaches and cyberattacks. Malicious users have the potential to detriment the system by engaging in unauthorized actions or thieving sensitive data. This paper proposes the dual-powered CLM technique (Convolution neural networks and LSTM) and optimization technique, a sophisticated methodology for distinguishing malicious user behavior that assimilates LSTM and CNN, and finally optimization technique to enhance the results. A genetic algorithm is used to augment the model's capability to perceive altering and nuanced malicious performance by fine-tuning its parameters. Due to the rising vulnerabilities of data breaches and cyber-attacks, malicious user identification in OSN (Online Social Networks) is a significant topic of research in cybersecurity. The proposed technique pursues to ascertain anomalous user behavior patterns by assessing vast quantities of data generated by digital systems with CLM and optimizing detection accuracy with genetic algorithms. On a public dataset of social media bot dataset, a twibot-20 dataset comprehending user activity data, was explored to measure the performance of the suggested methodology. The outcomes demonstrated that, in comparison to conventional machine learning algorithms like SVM and RF, which respectively obtained 92.3% and 88.9% accuracy, our technique, had a better accuracy of 98.7%. Moreover, the other metrics measures were assessed, and the proposed technique outperformed traditional machine learning algorithms in each situation.