Machine Learning and Deep Learning-Based Intrusion Detection for Cybersecurity

The study unequivocally demonstrates how machine learning algorithms may increase the accuracy of intrusion detection. Research Importance: Cyber security threats are on the rise, and as such, intrusion detection systems need to be more intelligent to detect potential cyber threats. The prediction of packet rates helps to improve intrusion detection accuracy, thus improving the reliability, scalability, and reliability of network security. Methodology: The paper makes use of various methodologies to improve intrusion detection accuracy by applying machine learning algorithms to predict packet rates. The paper makes use of statistical analysis to understand the relationships between variables. Input Parameters (Alternative): The study has also included other input parameters such as connection duration, failed login attempts, and intrusion detected. Here, connection duration refers to the time spent on session activities, failed login attempts may indicate unauthorized access, and intrusion detected may indicate malicious activities. These input parameters are significant in identifying patterns related to network anomalies and security issues. Evaluation Parameter (Output Parameter