An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection

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

Chapter 5 - Intrusion Prevention and Detection Systems

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

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.

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.

The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios.

Attack-aware Security Function Management

Software-defined networking (SDN) is an innovative network technology. It changed the world of computer networking by providing solutions to many challenges. SDN provides programmability, easy and centralized network management, dynamic configuration, and improved security. Although SDN offers remarkable benefits but it provides centralized network management which is prone to attacks. So, intrusion detection systems (IDS) are essential to detect and prevent security attacks in SDN. Traditional IDS follow a centralized machine learning approach which causes vulnerabilities in IDS. Old-style IDS lack data privacy preservation, and solution for training data unavailability due to privacy. Federated learning (FL) is a distributed machine learning approach which provides a collaborative training approach without data sharing. In FL, training is performed on multiple nodes creating a global model without sharing the data. To address challenges and the limitations of traditional IDS, we proposed a FL based multi class classification IDS for SDN. FL delivers an efficient and scalable solution to address challenges of traditional IDS. The proposed model enhances security of SDN by not requiring the centralization of data. To test the impact and efficiency of proposed model, we used a latest and realistic cybersecurity dataset. We also compared the proposed model with state of art existing multi class classification studies. The results and their comparison with existing studies highlight the potential of proposed model to enhance network security while providing a privacypreserving learning environment for intrusion detection.

Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail.

This paper presents Intrusion Detection Systems (IDS), Intrusion Detection and Prevention Systems (IDPS) and their classification emphasizing on the use of neural networks in IDS. Contemporary IDS usually include both signature verification and anomaly detection approaches realized by rule-based expert system and statistical module correspondingly. Neural networks may be used mainly as additional module to the statistical module to better recognize the user behavior. User behavior may be represented as frequency pattern of users command history. The paper presents an example of user profile vector for Unix-based platforms.

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.

In this paper, by combining the known deep learning related literature in the field of intrusion detection, and the existing in the design of intrusion detection system based on the deep learning common low detection efficiency and high rate of false positives, as well as the training data set used by the problems of unbalanced data, etc., through the depth study of feature extraction and data processing ability. To study the intrusion detection method of the Internet of Things based on deep learning. The work and achievements are as follows:First of all, will be used for feature selection embedded model (EM) and used in intrusion detection of the convolution neural network (CNN), combined with lightweight intrusion detection model (XCNN), because it is an embedded model (EM), so can reduce the impact on the equipment, at the same time compared with other algorithms, the computing time is shorter, the same or even better.Second, proposed to the Attention mechanism (Self - Attention) is applied to intrusion detection system, due to the nature of the Attention mechanism, the mechanism and long Attention can greatly reduce the training time, through introducing the residual network, application feature coding technique again the sparse features thickened into low density at high altitudes, and join to its relative position encoding, So it can sense global information and solve the long-term dependence problem of recurrent neural network.Finally, the results of the intrusion detection method were analyzed on the Pthon simulation platform, and the relevant indicators were verified by using THE NSL-KDD dataset, THE CIC-IDS2017 dataset and the CSE-CIC-IDS2018 dataset. The effectiveness of the method was proved by building a laboratory Internet of Things environment for actual testing. The experimental results show that the algorithm can greatly reduce training time, improve training efficiency, and have the same or even better performance results.

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.

Computer networks are being attacked every day. Intrusion detection systems (IDS) are used to detect and reduce effects of these attacks. The currently used of hybrid intrusion detection systems that was base on signature and anomaly based detection techniques were became inefficient for detecting attacks because it have nearly less than or equal to 95.5% for the detection rate and 1.8% for false positive rate, nowadays these values are unsatisfied for the detection so that the needs to enhanced hybrid intrusion detection system has became the most important issues. In this study, the enhanced hybrid intrusion detection system has been proposed to provide better results with high accuracy of the detection rate and reduce the value of false positive rate that will done by proposing new method based on decision tree of data mining techniques that is based on C4.5 algorithm to show that the proposed model is more efficient and it gives better optimum results.

Systems employing the Internet and Voice over Internet Protocol (VoIP) are alternative to the legacy landline telephony system. The services offered by these systems allow users to communicate with their family members, friends, banks and business partners whenever they are online. Particularly, the VoIP service is popular with the Internet users because charges are bound into usually fixed access costs making the price of the long distance calls themselves appear economical and even free. However, VoIP services have several disadvantages. One of the disadvantages of VoIP is, it is exposed to Internet security vulnerabilities, threats and attacks. The attacks come in many forms. One of the methods to attack the VoIP service, is by sending a Spam over Internet Telephony (SPIT). SPIT is normally executed by malicious parties initiating automated, unsolicited, and unwanted communications that used VoIP or video conferencing services, just like an email SPAM. Solutions to mitigate SPIT are still lacking. To defense a VoIP system from SPIT, we have come out with the idea of Automated Intrusion Detection and Prevention System over SPIT (AIDPoS) that can detect and prevent SPIT from external parties to enter the VoIP network. AIDPoS is a combination system of Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) on Voice over IP (VoIP) server. This is an experimental research to find a solution to combat SPIT if it happens in the future. Our result shows that an automated Intrusion detection and Prevention system over SPIT attack is feasible. Our work is on an open source VoIP network. Other vulnerabilities and threats are important, but beyond our research scope and is not covered in this paper.