Cybersecurity Risk Management: A Complete Framework for IT Enterprises
Anil Pandurang Gaikwad, Prof. Krutika Balram Kakpure, Anjali Ambadas Landge, Satish Gunderao Kulkarni, Meenakshi PramodJ adhav, Mohit Tiwari · 2023
A growing number of businesses depend on densely connected network systems. In these situations, residual risks brought on by the interconnectedness of business information security decisions make it more difficult to defend against cyberattacks. The management tactics employed by a company influence IT security in addition to its own. This study examines how two broadly utilized security risk management methods-self-protection investments and cyber insurance-are affected by correlated IT security threats. Utilizing an economic lens allows for a methodical investigation of the managerial and policy consequences of interrelated risks and potential solutions that could aid in enhancing information security. For this research, machine learning techniques were applied to data from phishing websites to improve cybersecurity by contrasting five techniques and offering knowledge that the broad accessible may employ to prevent common phishing traps. The outcomes of the research imply that the neural network (NN) technique is the most effective one. Since NNs are built on multi-layer perceptrons, the foundation of intelligence and phishing identification will eventually be automated and transformed into an artificial intelligence endeavor.