Current schemes are insufficient in addressing the growing security risks and cybersecurity concerns that disrupt companies from all walks of life.

In this session we will explain some machine learning (M/L) and data mining (DM) techniques in cybersecurity and vulnerability analysis and discovery. We will explore trends, perspectives, and prospects in the field of machine learning to cultivate an understanding of how ML/DM help to advance the cybersecurity footprint. Data is a coveted commodity for businesses, and everyone needs to understand what steps can be done to automate and innovate the hardening on their data and infrastructure. We will discuss key significant advancements that have been accomplished in machine learning in addition to challenges that exist and future areas for improvement and study. Lastly, we will discuss the three types of cyber analytics and how to combat false alarms and mitigate against cybersecurity intrusion detection problems. We will discuss some cybersecurity intrusion case studies, limitations and challenges that lie ahead.

Key Takeaways:

• Machine learning can help modernize and advance businesses to run more efficiently and promptly mitigate cybersecurity attacks; which, add-value for businesses on all fronts to protect their company propriety information and customer personal data.

• Gain insight to machine learning and how it applies to cybersecurity field.

• Understand how behavior analysis approach can help understand client behavior.

• Build a better understanding of ML/DM strategies including limitations and advancements.

• Challenges businesses face with current objectives and how machine learning can innovate previous strategies.