Making Network Intrusion Detection Work With IPsec

The Intrusion Detection System (IDS) is a useful infiltration detection tool. With IDS, it may mechanically categorize intrusions, attacks, or breaches of security stratagems across host-level within the network and within the organization. The Krill Herd Optimized Deep Neural Network (KH-DNN) is proposed in this document as a multiclass cyberattack methodology. This model is suggested by uniting the network-based intrusion detection system (NIDS) and the host-based intrusion detection system (HIDS) to advance flexible and actual IDS to categorize and detect unexpected and unpredictable cyber attacks. In this method, two types of IDS are classified, that is, NIDS and HIDS. Classification of the system call is exploited to discriminate behavior into normal and attack categories. In this manuscript accuracy, the parameter of the deep neural network classifier is optimized with the Krill Herd optimization algorithm. Finally, the DNN model is performed in the NIDS dataset and the HIDS dataset. For the HIDS dataset, the KDD Cup 99 dataset was used, which employed two datasets: ADFA-LD and ADFA-WD, and several types of attacks were detected and classified, including Adduser, Hydra-FTP, Java-Meterpreter, Hydra-SSH, and Web-Shell. The suggested IDS-KH-DNN algorithm attains higher accuracy 42.56% and 23.4%, high precision 84.74% and 52.43%, high F-score 53.5% 64.455, high sensitivity 33.4% and 45.23%, and high specificity 31.45% and 44.23% for the NIDS dataset. The proposed system is compared to two current processes: intrusion detection using Gray Wolf optimization-based support vector machine (IDS-GWO-SVM) and intrusion detection using artificial bee colony optimization algorithm based support vector machine (IDS-ABCO-SVM). Finally, the simulation results show that the suggested approach is capable of quickly and accurately locating optimal solutions.

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.

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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.

Application features such as port numbers are used by network-based intrusion detection systems (NIDSs) to detect attacks coming from networks. System calls and the operating system related information are used by host-based intrusion detection systems (HIDSs) to detect intrusions towards a host. However, the relationship between hardware architecture events and denial-of-service (DoS) attacks has not been well revealed. When increasingly sophisticated intrusions emerge, some attacks are able to bypass both the application and the operating system level feature monitors. Therefore, a more effective solution is required to enhance existing HIDSs. In this paper, we identify the following hardware architecture features: instruction count, cache miss, bus traffic and integrate them into a novel HIDS framework based on a modern statistical gradient boosting trees model. Through the integration of application, operating system and architecture level features, our proposed HIDS demonstrates a significant improvement of the detection rate in terms of sophisticated DoS intrusions.

A high growth rate in network traffic and the complexity of cyber threats have made it necessary to create more effective and flexible intrusion detection systems. Most traditional Network-based Intrusion Detection Systems (NIDS) can become weak at detecting new patterns of attacks due to the use of obsolete data or traditional machine learning models. To overcome the mentioned constraints, the current research presents a new deep learning solution that combines Sequential Deep Neural Networks (DNN) and Rectified Linear Unit (ReLU) activation unit with an Extra Tree Classifier feature selection procedure. The proposed model is trained and tested on the new rich and up-to-date UNSW-NB15 set, which provides a realistic reflection of the real-life network traffic and attack vectors. The interesting novelty of this study is the tactical use of ReLU-based DNN combined with feature optimization through the Extra Tree Classifier, which not only overcomes general problems like vanishing gradients and overfitting but also greatly increases the interpretability of the model and the efficiency of its computation. This dimensional reduction of the feature space (43 to only 8 highly relevant features) retains the high accuracy of the model but with better inference speed, which is a crucial aspect of the real-time deployment of NIDS. The results show that with the Sequential DNN approach, the binary class (0 for normal and 1 for attack records) achieved 97.93% accuracy, 97% Precision, 97% Recall and 97% F1-score. Furthermore, the detailed experimental testing, such as ROC curves and Confusion Matrices, confirmed that the Sequential DNN performed well in comparison to other Existing Studies. These findings underscore the effectiveness of deep learning architectures enhanced with optimized feature selection in detecting network intrusions, making the proposed system a promising solution for securing critical infrastructure in sectors such as finance, healthcare, and government networks.

An intrusion detection system, often known as IDS, is a piece of equipment or a piece of software that monitors a network or collection of devices in order to search for indications of possible intrusion. The frequency of cyber assaults has grown in recent years, and with it, the damage they do to society. The study of cyber security and the avoidance of cyber assaults, such as the use of intrusion detection as a defensive mechanism, is therefore needed. The internet services are widely used. Services based on computers, the internet, and other forms of technology are all considered part of the cyber world. The cyber world has advanced greatly thanks to new protocols and technologies. Cyber security is a major issue for every service that operates online. Network and host-based intrusion detection systems (NIDS/HIDS) are the backbones of any cyber security infrastructure. The NSL-KDD dataset is often used in algorithm research and verification and is widely employed in both the study and development of intrusion detection systems. In this study, we provide a neural network approach to intrusion detection system threat prediction. In this paper, the Python Spyder software is used for the simulation.

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.

As an important active defense technique for computer networks, intrusion detection has received lots of attention in recent years. However, the performance of current intrusion detection systems (IDSs) is far from being satisfactory due to the increasing number of complex sequential attacks. Aiming at the above problem, in this paper, a novel kernel-based reinforcement learning method for sequential behavior modeling in host-based IDSs is proposed. Based on Markov process modeling of host-based intrusion detection using sequences of system calls, the performance optimization of IDSs is transformed to a sequential prediction problem using evaluative reward signals. By using the kernel-based learning prediction algorithm, i.e., the kernel least-squares temporal-difference (kernel LS-TD) algorithm, which implements LS-TD learning in a kernel-induced feature space, the nonlinear modeling and prediction problem for sequential behaviors in IDSs is efficiently solved. Experiments on system call data from the University of New Mexico illustrate that the proposed kernel-based RL approach can achieve better detection accuracy than previous sequential behavior modeling methods including Hidden Markov Models (HMMs) and linear TD algorithms.

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

In Network-based Intrusion Detection, signatures discovery is an important issue, since the performance of an intrusion detection system heavily depends on accuracy and abundance of signatures. In most cases, we have to find these signatures manually. This is a time-consuming and error-prone work. Some papers introduce data mining into Intrusion Detection System. However, there are some drawbacks in these schemes. We present a data mining based approach to supporting signature discovery in network-based Intrusion Detection System. It has people find signatures of an intrusion easily. The main idea is that: First, Signature Discovery System (SDS) tries to find the most possible signatures that occur very frequently in the communication monitored. Second, SDS will find the relationships between these candidate signatures and construct rules based on these relationships found. Finally, SDS gives two kinds of hints: one is the signatures whose frequency of occurrence is greater than a threshold; the other is a set of rules composed of a set of signatures that are created by SDS in the second step. An experimental system called SigSniffer has been implemented to test the feasibility of the proposed approach.

An intrusion detection system (IDS) is an essential component of any modern network. The purpose of an IDS is to detect intrusion and generate appropriate alarms so that the intrusion can be mitigated. Implementing an IDS in a Software Defined Network (SDN) is easier since an SDN controller has a centralized view of the whole network. Researchers have made many efforts to use machine learning (ML) for developing network-based IDS in SDN. The network-based IDS analyzes different characteristics of incoming network traffic to detect intrusion. Early detection of intrusion is crucial for an IDS because if the intrusion is not detected quickly enough, it can cause severe damage, such as data breaches and service shutdowns. This paper focuses on detecting intrusion in SDN as early as possible using real-time flow-based features. Our aim is to detect intrusion with less amount of packets per flow, which not only facilitates early intrusion detection but also is useful when an intrusion flow has less number of packets. We show that although ML models perform well in offline training on a dataset, their performance decreases ~25% when fewer packets are used to generate features for the ML model. In all our experiments, a simple Random Forest (RF) algorithm outperforms a complex deep learning model on a publicly available dataset for intrusion detection in SDN.

Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

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.

Network-based Intrusion Detection Systems (NIDS), e.g., Snort, Bro or NSM, try to detect malicious network activity such as Denial of Service (DoS) attacks and port scans by monitoring network traffic. Research from network traffic measurement has identified various patterns that exploits on today's Internet typically exhibit. However, there has not been any significant attempt, so far, to design algorithms with provable guarantees for detecting exploit patterns from network traffic packets. In this work, we develop and apply data streaming algorithms to detect exploit patterns from network packet streams. In network intrusion detection, it is necessary to analyze large volumes of data in an online fashion. Our work addresses scalable analysis of data under the following situations. (1) Attack traffic can be stealthy in nature, which means detecting a few covert attackers might call for checking traffic logs of days or even months, (2) Traffic is multidimensional and correlations between multiple dimensions maybe important, and (3) Sometimes traffic from multiple sources may need to be analyzed in a combined manner. Our algorithms offer provable bounds on resource consumption and approximation error. Our theoretical results are supported by experiments over real network traces and synthetic datasets.