Cyber Threat Using Deep Learning

The network faces dangerous incidents from both inside and outside sources that destroy computer systems. The array defense system features intrusion detection as one of its active elements which protects network security from undetected problems. Traditional intrusion systems face difficulties with detection precision and rate along with generating several false alerts that require extra resources to identify enlightened attack patterns. An ensemble-based breach detection serves as the proposed solution to fill the same detection gaps. Improved threat detection requires deep learning-based methodologies and unsupervised techniques. We use GAN technology to detect cyber threats which occur in IoT-based network systems. The outcome produces a detection model with increased accuracy along with stronger reliability. This solution delivers better detection rates while requiring fewer accuracy terms and achieves better reliability. The ensemble learning classify compromised True Negative Rates as well as Hit Detection Rates when it comes to identifying Bruteforce attacks among others. The study results display uniformity throughout every dataset inquiry. System data maintains confidential integrity through utilization of this security principle. Telemetry-based threat spotting integrated with continuous monitoring runs opposed to traditional system patterns; this model implements it. The main objective of this work focuses on improving IoT-driven systems' resilience by means of increased efficiency with no rise in resource requirements. The conceptual framework with technology foundation are detailed throughout this chapter. The end objective aims to improve IoT systems resilience but it maintains operational efficiency and resource-intensive operation. The chapter presents details about the conceptual design along with technological bases and implications of running model applications.