Towards the Development of a Machine Learning Enhanceed Framework for Honeypot and CAPTCHA Intrusion Detection Systems

With the continuous prevalence of cyber-attacks, information safety has become very important to governments and organizations all over the world. Individuals, organizations and government suffers from financial and reputational damages consequent to cyber-attacks. Hence development of good cyber security technique became very important in the literature. Generally, traditional IDS are passive in such a way that they detect and report attacks based on predefined rules. Traditional IDS focus on how to detect attacks base on a given rule, i.e. either assigned or abnormality (Muhammad 2010). Other traditional securityy detection strategies such as firewall and CAPTCHA intrusion detection systems (IDS) have been invented to protect the system’s security, but there are still many critical issues which are reported every day (Salem et al 2008; Hauwa 2020). The situation worsens with the development of Internet technologies (Huang et al., 2019). Hence, cyber criminals continue to develop attack techniques against every cyber defense policy (Bukhari, S.et al,.2020). Therefore developed different cyber-attack methods such as phishing, image forgery, identity theft etc. On the other hand, academics and practitioners of cyber security are also developing cyber security measures. Hence, there is need for development of advanced security measures to curtail issues of cyber-attacks. This work proposes a framework that leverages on machine learning for same Key words: Machine Learning, Enhanceed Framework, Honeypot, CAPTCHA Intrusion Detection