NextGen Network security
In the current world that is full of technological advancement, a threat of attack by hackers is very real to both small and big firms as well as personal computers. To overcome such challenges, the Hybrid Rule-Based approach, dubbed NetworkFort is presented in this project. Consisting of both, non-machine learning and machine learning methods. It uses ML models operating with predefined rules for the detection of a number of cyber threats. Unlike other machine learning methods that need to be trained to detect and respond to threats before use, the approach inherent to the proposed solution will allow for immediate threat detection and response including detection and prevention of DoS-DDoS, DNS, DNS exfiltration, encrypted traffic, ransomware, anomaly detection and lateral movement etc.
Key Components of the Hybrid Rule-Based Approach
Machine Learning Model: A feature vector stored in a ML model network security monitor for capturing of network traffic.
Data Preprocessing: Collecting network traffic data, it is preprocessed to extract only beneficial peculiarities. This is done through normalization of data, making sure that data formats are right and dealing with issues to do with missing values.
Rule Application: Subsequently, the security rules and thresholds are deployed and utilized on the preprocessed context to accomplish threat predictions and detections.
Testing and Integration
Model Testing: The performance of the ML models is evaluated upon attack complexity as a way of estimating their efficiency in a real setting. The information collected in the testing process is recorded, and a video of the live demonstration of the prototypes is filmed.
Elasticsearch Integration: This is in combination with storing data using ‘Elasticsearch’ an open source search and analytics engine.
API Integration: It provides APIs to integrate in the other system and tools while providing a security that is complete and compatible.
Thus, the following are the benefits associated with the hybrid rule-based approach:
Immediate Threat Detection: As opposed to the previous methodologies, the Hybrid Rule-Based can detect and respond to the threat right away without requiring training.
Comprehensive Threat Coverage: The solution is capable of identifying and mitigating various types of cyber threats such as; DoS/DDoS, DNS flood, encrypted traffic, ransomware, anomalies, lateral movement, and MITRE ATT&CK Framework.
Improved Efficiency: Comprising both non-ML and ML techniques, the Hybrid Rule-Based method provides an increased effectiveness of recognizing and combating cyber threats.
Scalability and Flexibility: Openness and extensibility are achieved by the integration of the solution with Elasticsearch and APIs and this is crucial for all-scales and adaptability to the changing needs of organizations and the changing threat landscape. As seen by the Hybrid Rule-Based approach, organizations gain substantial improvements on the current ways of protecting themselves against cyber-attacks. As a result, it is possible to note that the use of this approach helps to enhance threat detection and minimize its negative consequences and as such is a worthy tool in the fight against cyber threats.
This document describes the legal and expected resources and outputs that need to be used and achieved with the new method, the Hybrid Rule-Based plan, to implement the NetworkFort product.