Finding Critical Link and Critical Node Vulnerability for Network

This document presents a review of using recurrent neural networks for network intrusion detection. It begins with an introduction to intrusion detection systems and the types of attacks they aim to detect. It then discusses previous research on machine learning approaches for intrusion detection, including the use of autoencoders, support vector machines, and other classifiers. The proposed approach uses a recurrent neural network for feature selection and classification of network data. The framework involves data collection, preprocessing including feature selection, training the recurrent neural network classifier, and then using the trained model to detect attacks in new data. Experimental results on benchmark intrusion detection datasets are presented and compared to other machine learning methods.

The main goal of Intrusion Detection Systems (IDSs) is to detect intrusions. This kind of detection system represents a significant tool in traditional computer based systems for ensuring cyber security. IDS model can be faster and reach more accurate detection rates, by selecting the most related features from the input dataset. Feature selection is an important stage of any IDs to select the optimal subset of features that enhance the process of the training model to become faster and reduce the complexity while preserving or enhancing the performance of the system. In this paper, we proposed a method that based on dividing the input dataset into different subsets according to each attack. Then we performed a feature selection technique using information gain filter for each subset. Then the optimal features set is generated by combining the list of features sets that obtained for each attack. Experimental results that conducted on NSL-KDD dataset shows that the proposed method for feature selection with fewer features, make an improvement to the system accuracy while decreasing the complexity. Moreover, a comparative study is performed to the efficiency of technique for feature selection using different classification methods. To enhance the overall performance, another stage is conducted using Random Forest and PART on voting learning algorithm. The results indicate that the best accuracy is achieved when using the product probability rule.

This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.