Machine Learning in the Nick of Time for Sophisticated Cybersecurity Threat Detection

Machine Learning in the Nick of Time for Sophisticated Cybersecurity Threat Detection

연구 분야: Safety

학회: International Conference on Advanced Network Technologies and Computational Intelligence

To bridge a market gap, our project is developing a cutting-edge machine learning (ML) cybersecurity defence. Machine learning is essential for countermeasure execution as well as real-time cyberattack detection and response. The increasing dependence on technology in the digital era raises cybersecurity concerns. As a result, criminals target it in order to steal sensitive information. Traditional signature-based security solutions, although effective, are inadequate due to the evolution of cyber threats. This study investigates why machine learning, a kind of artificial intelligence, has been more successful in combating these threats in recent years. Machine learning scans vast databases for anomalies that might indicate future security vulnerabilities. This study investigates the different cybersecurity applications of machine learning. These applications safeguard data networks and endpoints by detecting malware, preventing phishing, and identifying insider threats. To achieve ultimate security, we must overcome some obstacles. Many deep learning algorithms are ‘black boxes,’ creating concerns about privacy, security, and interpretability. Despite these challenges, machine learning has considerable potential for cybersecurity. This sector is quickly transitioning from reactive to proactive, flexible, and data-driven solutions. The power of machine learning to swiftly analyse massive data sets, detect patterns, and draw conclusions has altered cybersecurity. This study shows how machine learning may change cybersecurity. This research suggests a new cybersecurity detection tool. Technique performance has improved in recall (0.93), accuracy (0.95), precision (0.92), specificity (0.97), ROC AUC (0.98), and precision-recall AUC (0.96). The frequency of false positives and negatives has lowered significantly, improving operating safety and efficacy. This method keeps accuracy balanced in skewed datasets better than others. Modern cybersecurity prioritises efficiency and real-time data management. This technique improves cyber threat detection. The research also looks at the difficulties and answers to machine learning cybersecurity. Safeguard personal information and computer-to-human communication. This paper highlights the importance of machine learning in this market for assisting governments, security specialists, and businesses in combating developing threats.