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Research Topics in Machine learning for Cyber security

Research Topics in Machine learning for Cyber security

   Cybersecurity refers to the security mechanism protecting cyberspace and user assets against unauthorized access and attacks. Machine Learning is a popular solution to act quickly against such attacks because it learns from experiences and responds to newer attacks on time. Machine learning plays a vital role in cyber security to be highly proactive in preventing threats and responding to active attacks in real-time. Machine learning techniques play critical roles on the attacker and cyber security sides. In the defense against criminal attacks, machine learning models play a vital role in providing robust and smarter techniques to decrease the impact and damage by improving the performance and early detection of attacks. Machine learning techniques contain a set of rules that are applied to find more interesting patterns, which plays a major role in cyber security.

   Generally, Machine learning techniques in cyber security are trained to detect and react the cyber attacks and anomalies accurately. It also imparts highly enhanced security measures to analyze and protect the user assets by finding new behavior changes and categorizing the specialized types of attacks. In machine learning, well-known supervised algorithms are subdivided into classification and regression models. Decision Tree, Naive Bayes, Support Vector Machines, Random Forest, Logistic regression, K-means, and K-nearest neighbor are the most widely used machine learning algorithms for cyber security. The most popular cyber security applications in machine learning are Phishing detection, Network intrusion detection, Social network spam detection, Cyber threats identification, Email monitoring, User behavior modeling, Webshell, Ransomware, Remote exploitation, and more. Advancements and future research of cyber security in machine learning are multi-attack detections, terrorism detection, mission-critical application, growing new attacks, adversarial defense and attacks, zero-day attacks, and more.

   • The advancements in technologies facilitate hackers to discover vulnerabilities and create viruses and malware, leading to continuously challenging in the cyber security industry.

   • Machine learning for cyber security helps identify cyber-security threats more efficiently than other software-oriented methodologies and reduces the burden on security analysts.

   • There are numerous challenges of machine learning in cyber security involving Imbalanced Data Sets, Concept Drift and Domain Adaptation.

   • Multi-Layered approaches are needed to keep the solution resilient against malware attacks and achieve high detection rates.