Analyze and Forecast the Cyber Attack Detection Process using Machine Learning Techniques

One of the most crucial global concerns is the issue of cybercrime, which leads to significant financial losses for nations and their citizens every day. The frequency of cyberattacks has steadily increased, emphasizing the need to identify the individuals behind these criminal activities and understand their strategies. Detecting and preventing cyberattacks pose significant challenges, but recent advancements have introduced security models and prediction tools based on artificial intelligence to tackle these issues. Although there is a wealth of literature on crime prediction strategies, they may need to be more effectively suited for awaiting cybercrime and cyber-attack techniques. One potential solution to address this problem involves utilizing real-world data to determine the occurrence of an attack and identify the responsible party. This information encompasses details about the offense, offender demographics, property damage, and attack vectors. Forensic teams can collect information from victims of cyber-attacks through application processes. This research study employs machine learning techniques to analyze cybercrime using two models and predict how the attributes can contribute to identifying the method of cyber-attack and the criminal. This study has compared eight different machine-learning techniques, and discovered that they yielded similar results in terms of accuracy. The Support Vector Machine (SVM) linear model achieved the highest accuracy rate among the various cyber-attack methods tested. In the first model, valuable insights on the types of attacks victims were likely to face. Logistic regression, with a high success rate, was the most effective strategy for identifying malicious actors. The second model focused on comparing offender and victim attributes to make predictions regarding identification. Our findings indicate that the likelihood of becoming a victim of cyberattacks decreases with higher levels of education and wealth. This proposed concept is eagerly estimated for implementation by cybercrime departments, as it will simplify the detection of cyber-attacks and enhance the efficiency of the battle against them.