A new intrusion detection and alarm correlation technology based on neural network

This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS). Intrusion Detection and Prevention System (IDPS) technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works.Keywords: Intrusion Detection and Prevention System, Protocol Analysis, Sensor, Signature, State(ProQuest: ... denotes formulae omitted.)1 IntroductionIncreasing size and complexity of the Internet and Intranet networks have led to increasing number of vulnerabilities that could be exploited. Thus, the internal and external attacks on the information systems are increasing at an alarming rate. Also, these are becoming more severe and sophisticated. The attackers find ingenious ways to bypass the security controls and to compromise the security and the well functioning of the information systems. They are motivated by financial, political, and military objectives. In this context, defending wide area networks from malicious traffic, unauthorized access to systems involves many problems.In security information systems Network Intrusion Detection and Prevention Systems (NIDPS) are important tools to detect possible incidents and also, to attempt to stop them in real time. Due to changing attacks, intrusion detection methodologies and technologies continuously evolve, adding new detection capabilities, to avoid detection. They must adapt to new forms of malware, to the public networks, increased traffic.2 Concepts of Intrusion DetectionAn intrusion is a successful action to gain access to an information system, to compromise it or to make it unavailable. This is possible due to the presence of vulnerability in the target system that can be exploited by a motivated intruder.Intrusion Detection and Prevention is the process of monitoring the information systems by sensors or agents and analyzing the collected information to detect and to attempt to stop the attacks in real time, identifying vulnerabilities, the violation of security policies or standard security practices.An Intrusion Detection and Prevention System (IDPS) is a tool that monitors information systems, collects, analyzes information, and initiates responses when an intrusion is detected.Intrusion Detection Systems (IDSs) mainly work as defensive mechanisms. They only alert the system administrators that an incident has occurred. Intrusion Prevention Systems (IPSs) can take some actions to attempt to stop the attack, such as breaking the connection or modifying the firewall rules to deny access to the intruder. The response of the classic IDS can be slow if the system administrator is busy while the response of the IPS is automatic. An architecture that uses together IPS and IDS technologies is the best solution for defense in depth.Conceptually, a generic IDPS consists of modular components. It mainly has the following components: monitoring system, storage, analyzer, and responder.* Monitoring system - monitors and logs the events in a computer system or network;* Storage - stores information, called audit record, about suspicious activities or intrusions; also, the security policies used in analysis are stored;* Analyzer - uses different analysis methodologies to detect the incidents;* Responder - the response mechanism of incidents.The IDPSs could be classified as:* By detection methodology [12], [18]:- misuse-based detection- anomaly-based detection- stateful protocol analysis* By activity [12]:- network-based- wireless-based- network behavior analysis- host-based* By behavior on detection:- passive- active* By collection and analysis frequency:- continuous- periodicThe detection methodologies describe the characteristics of the analyzer. …

The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established attack mitigation system. However, the integration of NIDS and HIDS introduces formidable challenges, notably the elevated rates of False Positive and False Negative alerts. To surmount these challenges, the researcher employs machine learning techniques in the form of Snort plugins and comparison methods to heighten the precision of attack detection. The obtained results unequivocally illustrate the effectiveness of this approach. Using a Support Vector Machine for static analysis of the NSL-KDD dataset attains an outstanding 99% detection rate for Denial of Service (DoS) attacks and an impressive 98% detection rate for Probe attacks. Furthermore, in dynamic real-time attack simulations, the machine learning plugins exhibit remarkable proficiency in detecting various types of DoS attacks, concurrently offering more comprehensive identification of SYN Flooding DoS attacks compared to the Snort community rules set. These findings signify a significant advancement in intrusion detection, paving the way for more robust and accurate network security systems in an era of escalating cyber threats.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

Chapter 5 - Intrusion Prevention and Detection Systems

A Rule Status Monitoring Algorithm for Rule-Based Intrusion Detection and Prevention Systems

The novelty and growing sophistication of cyber threats mean that high accuracy and interpretable machine learning models are needed more than ever before for Intrusion Detection and Prevention Systems. This study aims to solve this challenge by applying Explainable AI techniques, including Shapley Additive explanations feature selection, to improve model performance, robustness, and transparency. The method systematically employs different classifiers and proposes a new hybrid method called Hybrid Bagging-Boosting and Boosting on Residuals. Then, performance is taken in four steps: the multistep evaluation of hybrid ensemble learning methods for binary classification and fine-tuning of performance; feature selection using Shapley Additive explanations values retraining the hybrid model for better performance and reducing overfitting; the generalization of the proposed model for multiclass classification; and the evaluation using standard information metrics such as accuracy, precision, recall, and F1-score. Key results indicate that the proposed methods outperform state-of-the-art algorithms, achieving a peak accuracy of 98.47% and an F1 score of 96.19%. These improvements stem from advanced feature selection and resampling techniques, enhancing model accuracy and balancing precision and recall. Integrating Shapley Additive explanations-based feature selection with hybrid ensemble methods significantly boosts the predictive and explanatory power of Intrusion Detection and Prevention Systems, addressing common pitfalls in traditional cybersecurity models. This study paves the way for further research on statistical innovations to enhance Intrusion Detection and Prevention Systems performance.

With the rapid development of machine learning and Internet technology, the combination of the two methods is well appreciated recently.An anomaly and intrusion detection system is a mechanism that monitors network or system activities for malicious activities.Intrusion detection and prevention systems are primarily focused on identifying possible incidents, logging information about them and reporting attempts.As far as other usages of Intrusion detection and prevention systems are concerned, such as identifying problems with security policies and deterring individuals from violating security policies.Anomaly detection systems are becoming an important addition to the security infrastructure of nearly every organization.In this paper, we propose a novel mechanism for real-world traffic and research there cases with theoretical analysis.

As an active network security protection scheme, intrusion detection system (IDS) undertakes the important responsibility of detecting network attacks in the form of malicious network traffic. Intrusion detection technology is an important part of IDS. At present, many scholars have carried out extensive research on intrusion detection technology. However, developing an efficient intrusion detection method for massive network traffic data is still difficult. Since Generative Adversarial Networks (GANs) have powerful modeling capabilities for complex high-dimensional data, they provide new ideas for addressing this problem. In this paper, we put forward an EBGAN-based intrusion detection method, IDS-EBGAN, that classifies network records as normal traffic or malicious traffic. The generator in IDS-EBGAN is responsible for converting the original malicious network traffic in the training set into adversarial malicious examples. This is because we want to use adversarial learning to improve the ability of discriminator to detect malicious traffic. At the same time, the discriminator adopts Autoencoder model. During testing, IDS-EBGAN uses reconstruction error of discriminator to classify traffic records.

Malware authors are continuously developing crime toolkits. This has led to the situation of zero-day attacks, where malware harm computer systems despite the protection from existing Intrusion Detection Systems (IDSs). We propose an Intelligent Intrusion Detection and Prevention System (IIDPS) approach that combines the Signature based Intrusion Detection system (SIDS), Anomaly based Intrusion Detection System (AIDS) and Response Intrusion Detection System (RIDS). We used a risk assessment approach to determine an appropriate response action against each attack event. We also demonstrated the IIDPS make the detection and prevention of malware more effective.

: Attacks on the nation's computer infrastructures are a serious problem. Over the past 12 years, the growing number of computer security incidents on the Internet has reflected the growth of the Internet itself. Because most deployed computer systems are vulnerable to attack, intrusion detection (ID) is a rapidly developing field. Intrusion detection is an important technology business sector as well as an active area of research. Vendors make many claims for their products in the commercial marketplace so separating hype from reality can be a major challenge. A goal of this report is to provide an unbiased assessment of publicly available ID technology. We hope this will help those who purchase and use ID technology to gain a realistic understanding of its capabilities and limitations. The report raises issues that we believe are important for ID system (IDS) developers to address as they formulate product strategies. The report also points out relevant issues for the research community as they formulate research directions and allocate funds.

The network security is getting more important due to the wide-spread computer viruses and increasing network attacks. Nowadays, more and more security mechanisms, such as firewalls and intrusion detection systems (IDS), are introduced to protect the network from malicious attacks. This paper proposes an agent and service based intrusion detection and response system for active network. In contrast to a traditional passive network, an active network gives the nodes programmable ability to exercise various active network technologies. The intrusion response, service deployment, and service update mechanisms are centered on this technology. The proposed model of intrusion detection and response system (IDRS) catches network attacks and responses to stop the attacks at the first time to reduce the damage. Detecting, reporting, and responding capabilities are all embedded and integrated in the proposed system. A prototype system is developed using a novel data mining technology (the support vector machine) to enhance the detection function. In addition, several experiments were conducted to verify the system and results showed that the system was able to effectively identify the intrusions and respond promptly. Experiments also showed that the support vector machine outperforms the competitive neural networks in identifying the intrusions.

Intrusion detection and prevention systems (IDPS) are security systems that are used to detect and prevent security threats to computer systems and computer networks. These systems are configured to detect and respond to security threats automatically there by reducing the risk to monitored computers and networks. Intrusion detection and prevention systems use different methodologies such as signature based, anomaly based, stateful protocol analysis, and a hybrid system that combines some or all of the other systems to detect and respond to security threats. The growth of systems that use a combination of methods creates some confusion when trying to choose a methodology and system to deploy. This paper seeks to offer a clear explanation of each methodology and then offer a way to compare these methodologies.

Rapid growth in Internet and in parallel attacks, vulnerability and threats, has made intrusion detection systems very essential component in all parts of security infrastructure. Building IDS is not a new task, classical signature based IDS are used but they are unable to handle novel attacks. In this paper artificial neural network based intrusion detection is proposed for complete KDD cup 99 dataset. Performance of the proposed ANN based IDS system is evaluated and results shows high anomaly detection accuracy for the complete KDD cup 99 dataset as compared to existing techniques.