Predictive Analytics and Cybersecurity
Mohammed Sayeeduddin Habeeb · 2024
Recent years have seen an enormous increase in the amount of data being created and processed, as well as tremendous growth in communication systems and network size. Due to an increase in cyberattacks that are either new or modified versions of previous attacks, this has eventually led to numerous risks to communication networks as well. Therefore, the global cyber security sector is becoming increasingly concerned with effectively addressing cyber anomalies and attacks. Predicting cyberattacks is seen to offer enormous potential for proactively enhancing cybersecurity. Due to the fast growth of several types of cyberattacks and threats, conventional safety measures are unable to address current security problems. Artificial intelligence (AI) techniques are being used by research groups as well as the industry to offer cybersecurity problem prediction techniques and to improve detection accuracy. By using intelligent AI techniques, a researcher was able to develop a dynamically improved, automated, decision-making, and updated security system. In this chapter, we present a thorough overview of machine learning (ML) algorithms, highlighting how they may be used for proactive data analysis and automation in cybersecurity due to their capacity to uncover useful details from cybersecurity data. This intelligence was tested in predictive cybersecurity systems in real-world scenarios where data-driven intelligence, automation, and decision-making allow for the next generation of proactive cyber defense above current methods. Finally, our overall objective is to examine both the status of ML today, along with appropriate approaches, as well as how they can be used to make advancements in cybersecurity. It is generic and adaptable to the requirements of the particular.