Published Paper Details:

The recent rapid growth in big data, networking, and machine learning is due to exponential advances in processing, storage, and network technologies. As the world becomes more digitalized, there is a greater need for comprehensive and sophisticated security technologies and strategies to address the increasingly complex nature of cyber-attacks. This paper examines how machine learning, big data is being used in cyber security, both in defence and offense, with a focus on cyber-attacks against machine learning models. Machine learning can be used to carry out cyber-attacks, such as smart botnets, sophisticated spear fishing, and evasive malware. In the field of defence, big data analytics refers to the ability to collect large volumes of digital data in order to analyse, visualize, and derive knowledge that can help predict and prevent cyber-attacks. It gives us a stronger cyber defence stance when combined with security technologies. They allow businesses to identify patterns of behaviour that indicate network threats. Machine learning is used in cyber security for threat identification and prevention, malware detection and classification, and network risk rating, among other things.