Chapter 5 - Intrusion Prevention and Detection Systems

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.

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.

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 Internet is the darling of the information age. Its rapid development has brought a lot of wealth to the society, but it has also brought about increasingly serious security problems. In particular, the widespread application of e-commerce has made network security the most important part of the development of network technology. Network security technology is increasingly valued by computer researchers. Most of the existing security products are passive. The firewall can only be statically disabled. Although the Intrusion Detection System can dynamically detect the intrusion behavior, it cannot block the detected attack behavior. However, the impact of the intrusion prevention system on network performance and the high intrusion detection false alarm rate restrict the development and application of the IPS system. Especially on gigabit high-speed networks, performance has become the primary bottleneck of IPS systems. In view of the above problems, this paper proposes a distributed intrusion detection and blocking system that can run efficiently on high-speed networks. Such systems are also known as intrusion prevention systems IPS. Explain the structure of distributed high-speed network intrusion prevention system, and also introduce the related implementation technology and the realization of each part of distributed intrusion prevention system.

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.

Attack-aware Security Function Management

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.

The growing complexity of cyber threats necessitates creative solutions beyond conventional rule-based security systems. This study presents a new method for the incorporation of artificial intelligence (AI) into intrusion detection and prevention systems (IDPS) that facilitates real-time threat mitigation, adaptive learning, and autonomous response. Through the use of machine learning (ML), behavioural analytics, and generative AI, this solution overcomes the weaknesses of legacy systems while maximizing accuracy, scalability, and operational efficiency in cybersecurity. Key Words: Artificial Intelligence (AI); Cybersecurity; Intrusion Detection System (IDS); Intrusion Prevention System (IPS); Real-Time Threat Detection; Machine Learning (ML); Anomaly Detection; Behavioural Analytics; User and Entity Behaviour Analytics (UEBA); Automated Incident Response; Adaptive Learning; Generative Adversarial Networks (GANs); Adversarial AI; Zero-Day Attack Detection; Security Orchestration, Automation, and Response (SOAR); Autonomous Cybersecurity; Cyber Threat Mitigation; Network Security; Deep Learning; Explainable AI (XAI); Federated Learning; Threat Intelligence; Real-Time Analytics; Predictive Cybersecurity; AI-Driven Defence Systems; Proactive Defence Mechanisms; Self-Improving Systems; Quantum-Resistant AI; Zero Trust Architecture; Cyber-Physical Systems Security

Network-based intrusion detection and prevention systems -NIDS /NIPS- have been widely implemented in order to build layered information security infrastructures. Many intrusion detection and prevention algorithms have been developed and its operation modes imply benefits and drawbacks that are not taken into account when choosing a network IDS/IPS. This paper assesses the network security provided by five NIDS/NIPS software solutions, by analyzing the protection against malicious traffic. Security platforms against network intrusion such as Snort Inline, Stonegate IPS, Strataguard, Intrupro and Packetalarm are tested and analyzed.

The security related issues in the Internet have drawn a constant attention due to rapid growth of attack and its devastating consequence. The essence of monitoring the networks and analyzing the packets coming from the network is to verify the authenticity of traffics. There have many Intrusion Detection & Prevention System (IDPS) techniques been proposed and accepted in the last few decades such as Snort, Bro IDS etc. In this paper we have proposed a novel string searching algorithm and an Intrusion Detection System using this algorithm. In addition, we have explored few exact-pattern searching algorithms and their comparative analysis as our background study. A dataset of five thousands records (a subset from KDD Cup dataset) with forty one features is taken for evaluating the efficacy of the proposed IDS. The corresponding global nucleotide sequences of all the features of the dataset helped us to implement our IDS.

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

We describe a situation-aware intrusion detection system that integrates heterogeneous sources of information to build and maintain a semantically rich knowledge-base about cyber threats and vulnerabilities. Most current intrusion detection and prevention systems rely on signature-based approaches to detect attacks. When an attack signature is not available, such as for a new exploit or a significantly modified known one, such systems are much less effective. Moreover, these intrusion detection systems are point-based solutions which do not make effective use of heterogeneous data sources, which can provide im-portant information related to intrusions which are not yet available as signature patterns. This information can also help detect low-and-slow attacks in which small intrusions that are spatially and temporally apart combine to build a more elaborate attack. For more information, see http://ebiq.org/j/98

Distributed Denial-of-Service (DDoS) attack has been a “nightmare” for cloud. A countermeasure is to establish an Intrusion Detection and Prevention System (IDPS) for cloud. Nevertheless, current IDPSes fail to achieve the detection and prevention in a flexible and lightweight way. In this paper, we propose a novel scheme of IDPS for overcoming the above problem, termed as Auto-scaling IDPS (AsIDPS). AsIDPS is based on Software-Defined Networking (SDN) and Docker container technologies. It first detects abnormal traffic based on the flow statistics collected in SDN switches in real-time. By the SDN controller, the abnormal traffic will be directed to the created Docker containers with Snort running on them for further detection and clean-up. Particularly, the Docker containers can be automatically scaled out or scaled down on demand. The Snort will also deliver an alert to the SDN controller if it detects attack traffic so as to perform a countermeasure if necessary. Benefitting from the flexible network management offered by SDN and the lightweight Docker container, AsIDPS is able to build a flexible and lightweight defense against DDoS attack in cloud. Based on our prototype implementation, we validate the effectiveness of AsIDPS in defending DDoS attack, and also verify its flexibility and lightweight.

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.

Network Intrusion detection and prevention Systems (NIDPS) are employed in monitoring a network which safeguards user integrity, privacy thereby ensuring the data security and availability in a network. Such systems not only monitor the suspicious activities in a network but also used as control systems to eliminate the malicious users from the network. In this paper, a Hybrid Adaptive Development Algorithm and Machine Learning Algorithm (ADA-MLA) method is proposed to identify the malicious activities and eliminating them from the network. The deployment of honeypot-based intrusion is improved adaptive development algorithm. Machine learning algorithm has been employed in the Hybrid IDPS for learning the network data patterns which also identifies the maximum probable attacks in the network. The signatures for the DARPA 99 data set have been updated during the implementation of intrusion prevention system on a real-time basis. The hybrid method works on (i) classifying the attacks based on protocols and (ii) classifying the attacks on pre-determined threshold values. Hence, both known and unknown attacks can be easily captured in the proposed hybrid IDPS method which thereby achieves higher attack detection and prevention accuracy while compared to the conventional attack detection and prevention methodologies.