Cyber-Attack Detection Using Machine Learning Technique

Cybersecurity is crucial for protecting users of the internet on various electronic devices in everyday life. It is crucial for safeguarding some extremely sensitive information, including biotechnology and military resources, that are gravely threatened by hackers. An organisation must be aware of the dangers posed by cyber-attacks, assess those dangers, and decide what kind of defences are necessary and where to place them. In this study, we conceptualise the prediction of cyber-attacks as a classification problem, with networking sectors predicting the type of network attack from a given dataset using machine learning techniques. ML techniques, which can learn from patterns in data and make predictions based on those patterns, have emerged as a promising approach to cybersecurity. Proposed work first reviews different cyber-attacks and the challenges associated with their detection and classification. It then describes the ML techniques that have been applied to this problem, including supervised and unsupervised learning algorithms, and discusses the features and data sources that are typically used in these models. The paper also presents experimental results from applying various ML techniques to a dataset of simulated cyber-attacks. The results show that ML techniques can achieve high accuracy in classifying different types of cyber-attacks, and that feature engineering and data pre-processing techniques can significantly improve classification performance. In this paper we have discussed the limitations and future directions of ML-based cyber-attack classification, including the need for more diverse and real-world datasets, we also have discussed the challenges also explain ability and interpretability in ML models, and the potential for adversarial attacks on ML-based cybersecurity systems.