An intrusion detection classification model base on projection pursuit

: Network-based intrusion detection systems (NIDSs) are one component of a comprehensive network security solution. The use of IPsec, which encrypts network traffic, renders network intrusion detection virtually useless unless traffic is decrypted at network gateways. One alternative to NIDSs, host-based intrusion detection systems (HIDSs), provides some of the functionality of NIDSs but with limitations. HIDSs cannot perform a network-wide analysis and can be subverted if a host is compromised. We propose an approach to intrusion detection that combines HIDS, NIDS, and a version of IPsec that encrypts the header and the body of IP packets separately. We refer to the latter generically as TwoKey IPsec. We show that all of the network events currently detectable by the Snort NIDS on un- encrypted network traffic are also detectable on encrypted network traffic using this approach. The NIDS detects network-level events that HIDSs have trouble detecting and HIDSs detect application-level events that can't be detected by the NIDS.

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.

Network security is of central significance in the current information world. Due to the rapid increase of network-enabled devices, there is a significant risk of network intrusion more than ever. Hackers and intruders can successfully attack to cause the crash of the networks and web services by the unauthorized intrusion, which may cause a significant loss to an organization in terms of data and money. So, it is high time to create an intrusion detection system that can detect all types of intrusion. Due to the rapid growth and significant results of machine learning (ML) algorithms in several areas, there has recently been much interest in applying them to network security. The network-based intrusion detection system (NIDS) has much promise to be the borderline of defense against intrusions in the current information communication technology (ICT) era, and it's a critical aspect of network security. Due to the dynamic nature of attacks, intrusion detection datasets are available publicly. Intrusion detection systems are the backbone of the networks and data protection. Various IDS approaches have been used over time to achieve maximum detection accuracy. This paper investigates the different machine learning methods used to deploy network-based intrusion detection systems. This survey could give scholars a better grasp of present methodologies and help them find research possibilities and do further research in this area.

Network security breaches continue to grow in both complexity and impact, making intrusion detection a critical component of modern defense strategies. Intrusion Detection Systems (IDS) monitor system and network activity to identify malicious behavior and stop it before causing harm. causes harm. Among them, Network-based IDS (NIDS) is particularly important because it analyzes network traffic at the packet or flow level, enabling protection across a wide range of infrastructure. Deep Learning (DL) has recently emerged as a transformative approach for NIDS by learning complex representations of normal and malicious traffic patterns without the need for extensive manual feature engineering. This survey provides a review of recent progress in DL for NIDS and introduces a taxonomy with four categories. These include reconstruction and generative models that detect anomalies, Transformer-based sequence models for capturing temporal dependencies, convolutional and deep neural networks for supervised classification, and hybrid or deployment-oriented approaches that combine techniques or support real-world deployment needs. This survey reviews 31 studies published between 2020 and 2025, covering their datasets, feature sets, model architectures, and reported performance. A comparative analysis highlights common practices and emerging trends. Finally, we identify open challenges related to data quality, scalability, adversarial robustness, privacy, and interpretability, and we outline promising directions for future research. This work contributes an updated review, a structured framework for comparison, and practical insights to guide the development of more secure and effective NIDS.

Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many more spectral intervals than previously possible. An example of that technology is the AVIRIS system, which collects image data in 220 bands. As a result of this, new algorithms must be developed in order to analyze the more complex data effectively. Data in a high dimensional space presents a substantial challenge, since intuitive concepts valid in a 2-3 dimensional space do not necessarily apply in higher dimensional spaces. For example, high dimensional space is mostly empty. This results from the concentration of data in the corners of hypercubes. Other examples may be cited. Such observations suggest the need to project data to a subspace of a much lower dimension on a problem specific basis in such a manner that information is not lost. Projection pursuit is a technique that will accomplish such a goal. Since it processes data in lower dimensions, it should avoid many of the difficulties of high dimensional spaces. The authors investigate some of the properties of projection pursuit. >

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Network-based Intrusion Detection Systems (NIDSs) are an important mechanism to identify malicious behaviour or policy violations within a network. Such detection systems typically face several challenges, among which are the base-rate fallacy and the resilience against adaptive adversaries. These challenges are often countered in modern NIDSs by combining multiple detection systems to diversify the used feature levels or utilize the advantages of multiple detection methods. However, currently there exists no suitable framework for a detailed analysis of such composed systems. Therefore, the contribution of this work is an evaluation framework for composed systems, which builds on previous information-theoretic approaches and highlights the utility of information-theoretic redundancies for robustness evaluations. This framework enables an attribution of the overall system performance to its individual components, to fine-tune parameters and to study the dynamics between classifiers. The versatility of the framework is demonstrated by designing and evaluating a composed NIDS example based on systems described in the literature and using an open data set. Studying the impact of an evasion attempt with adversarial examples on this system highlighted the importance of robustness against false-alarms as well as detection evasion. Moreover, the framework enables general insights on how to improve the design of composed NIDSs: based on the dynamics between classifiers, it can be shown that optimizing the operation point of each component individually does not necessarily maximize the overall system performance from an information-theoretic perspective. Additionally, it can be shown that existing classification redundancies might not be fully utilized during an attack on the NIDS components, due to a static system design.

LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system

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.

Intrusion detection is a major research problem in network security. Intrusion Detection Systems (IDS), analyses information from the network trying to identify suspicious behaviors and detect intentions to attack the system. Intrusion attempts are nonlinear with an unpredictable behavior on the network traffic. The process of selecting the key features that allows discriminate attacks from normal traffic, is a crucial task in information security to obtain an effective IDS. We propose in this paper to use an Evolutionary Algorithm and an evaluation function from a classifier, to automatically select key features from a data set before defining a Decision Tree that can be used to discriminate among the network data type. The purpose of this study, is to propose an intrusion detection technique that selects key features using a specially designed evolutionary algorithm with individual evaluations done using C4.5, a wellknown classifier that discriminate data using decision trees. We report very encouraging results of our approach using NSL-KDD intrusion detection benchmark data sets.

In this work, a Behaviour-based Intrusion Detection Model is suggested. The proposed model can be employed from a single host configuration to a distributed mixture of host-based and network-based Intrusion Detection Systems (IDSs). Unlike most state-of-the-art IDSs that rely on analysing lower-level, raw-data representations, our proposed architecture suggests to use higher-level notions -behaviours- instead; this way, the IDS is able to identify more sophisticated attacks. To assess our premise, a Behaviour-based IDS (BIDS) prototype has been designed and developed that scans file system data to identify attacks. BIDS achieves high detection rates with low corresponding false positive rates, superseding other state-of-the-art file system IDSs.

A new round of scientific and technological revolution and industrial reform promote the intelligent development of automobile and promote the deep integration of automobile with Internet, big data, communication and other industries. At the same time, it also brings network and data security problems to automobile, which is very easy to cause national security and social security risks. Intelligent vehicle Ethernet intrusion detection can effectively alleviate the security risk of vehicle network, but the complex attack means and vehicle compatibility have not been effectively solved. This research takes the vehicle Ethernet as the research object, constructs the machine learning samples for neural network, applies the self coding network technology combined with the original characteristics to the network intrusion detection algorithm, and studies a self-learning vehicle Ethernet intrusion detection algorithm. Through the application and test of vehicle terminal, the algorithm generated in this study can be used for vehicle terminal with Ethernet communication function, and can effectively resist 34 kinds of network attacks in four categories. This method effectively improves the network security defense capability of vehicle Ethernet, provides technical support for the network security of intelligent vehicles, and can be widely used in mass-produced intelligent vehicles with Ethernet.

With rapid advancements in in-vehicle network (IVN) technology, the demand for multiple advanced functions and networking in electric vehicles (EVs) has recently increased. To enable various intelligent functions, the electrical system of existing vehicles incorporates a controller area network (CAN) bus system that enables communication among electrical control units (ECUs). In practice, traditional network-based intrusion detection systems (NIDSs) cannot easily identify threats to the CAN bus system. Therefore, it is necessary to develop a new type of NIDS—namely, on-the-move Intrusion Detection System (OMIDS)—to categorise these threats. Accordingly, this paper proposes an intrusion detection model for IVNs, based on the VGG16 classifier deep learning model, to learn attack behaviour characteristics and classify threats. The experimental dataset was provided by the Hacking and Countermeasure Research Lab (HCRL) to validate classification performance for denial of service (DoS), fuzzy attacks, spoofing gear, and RPM in vehicle communications. The proposed classifier’s performance was compared with that of the XBoost ensemble learning scheme to identify threats from in-vehicle networks. In particular, the test cases can detect anomalies in terms of accuracy, precision, recall, and F1-score to ensure detection accuracy and identify false alarm threats. The experimental results show that the classification accuracy of the dataset for HCRL Car-Hacking by the VGG16 and XBoost classifiers (n = 50) reached 97.8241% and 99.9995% for the 5-subcategory classification results on the testing data, respectively.

Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing network traffic data to detect security violations. Mining approach can play very important role in developing an intrusion detection system. The network traffic can be classified into normal and anomalous in order to detect intrusions. In our paper, top-ten classification algorithms namely J48, BayesNet, Logistic, SGD, IBK, JRip, PART, Random Forest, Random Tree and REPTree were selected after experimenting with more than twenty most widely used classification algorithms. The comparison of these top-ten classification algorithms is presented in this paper based upon their performance metrics to find out the best suitable algorithm available. Performance of the classification models is measured using 10-fold cross validation. Experiments and assessments of these methods are performed in WEKA environment using NSL-KDD dataset.