Network-based Intrusion Detection System Research Articles

Probabilistic Analysis of Random Check Intrusion Detection System

The ubiquitous adoption of network-based technologies has left organizations vulnerable to malicious attacks. It has become vital to have effective intrusion detection systems (IDS) that protect the network from attacks. In this paper, we study the intrusion detection problem through the lens of probability theory. We consider a situation where a network receives random malicious signals at discrete time instances, and an IDS attempts to capture these signals via a random check process. We aim to develop a probabilistic framework for intrusion detection under the given scenario. Concretely, we calculate the detection rate of a network attack by an IDS and determine the expected number of detections. We perform extensive theoretical and experimental analyses of the problem. The results presented in this paper would be helpful tools for designing and analyzing intrusion detection systems. We propose a probabilistic framework that could be useful for IDS experts; for a network-based IDS that monitors in real-time, analyzing the entire traffic flow can be computationally expensive. By probabilistically sampling only a fraction of the network traffic, the IDS can still perform its task effectively while reducing the computational cost. However, checking only a fraction of the traffic increases the possibility of missing an attack. This research can help IDS designers achieve appropriate detection rates while maintaining a low false alarm rate. The groundwork laid out in this paper could be used for future research on understanding the probabilities related to intrusion detection.

Artificial Intelligence-Based Intrusion Detection System for Cloud Computing

Abstract: It is possible to communicate with others and do business across this global network, which is comprised of hundreds of millions of computers running a variety of hardware and software configurations. This makes it simpler for hackers to abuse resources and conduct Internet attacks since computers are linked to one another. There are significant roadblocks to the development of a security-oriented approach that may be flexible and adaptive in light of the expanding number of Internet assaults. Threats from the internet could be identified using an intrusion detection system (IDS). The utilization of an intrusion detection system, or IDS, is necessary to preserve network security. Because the cloud platform is continuously expanding and becoming more prevalent in our everyday lives, it is imperative that we develop an effective IDS for it as well. On the other hand, typical intrusion detection systems may encounter difficulties when used in the cloud. A cloud segment may get overburdened by the pre-determined IDS architecture because of the added detection overhead. In the context of a networked system with an adaptable architecture. Using a neural network-based IDS, we show how to make full utilize available resources while not putting undue strain on any single cloud server. The proposed IDS uses a neural network machine learning to identify new threats even more effectively

A Case Study of Network-Based Intrusion Detection System Deployment in Industrial Control Systems with Network Isolation

A Case Study of Network-Based Intrusion Detection System Deployment in Industrial Control Systems with Network Isolation

Ensemble averaging deep neural network for botnet detection in heterogeneous Internet of Things devices

The botnet attack is one of the coordinated attack types that can infect Internet of Things (IoT) devices and cause them to malfunction. Botnets can steal sensitive information from IoT devices and control them to launch another attack, such as a Distributed Denial-of-Service (DDoS) attack or email spam. This attack is commonly detected using a network-based Intrusion Detection System (NIDS) that monitors the network device’s activity. However, IoT network is dynamic and IoT devices have many types with different configurations and vendors in IoT environments. Therefore, this research proposes an Intrusion Detection System (IDS) by ensemble-ing traffic from heterogeneous IoT devices. This research proposes Deep Neural Network (DNN) to create a training model from each heterogeneous IoT device. After that, each training model from each heterogeneous IoT device is used to predict the traffic. The prediction results from each training model are averaged using the ensemble averaging method to determine the final result. This research used the N-BaIoT dataset to validate the proposed IDS model. Based on experimental results, ensemble averaging DNN can detect botnet attacks in heterogeneous IoT devices with an average accuracy of 97.21, precision of 91.41, recall of 87.31, and F1-score 88.48.

Retraction Note: FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks

Retraction Note: FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks

Semi-Supervised Alert Filtering for Network Security

Network-based intrusion detection systems play a pivotal role in cybersecurity, but they generate a significant number of alerts. This leads to alert fatigue, a phenomenon where security analysts may miss true alerts hidden among false ones. To address alert fatigue, practical detection systems enable administrators to divide alerts into multiple groups by the alert name and the related Internet Protocol (IP) address. Then, some groups are deliberately ignored to conserve human resources for further analysis. However, the drawback of this approach is that the filtering basis is so coarse-grained that some true alerts are also ignored, which may cause critical security issues. In this paper, we present a new semi-supervised and fine-grained filtering method that uses not only alert names and IP addresses but also semi-supervised clustering results from the alerts. We evaluate our scheme with both a private dataset from a security operations center and a public dataset from the Internet. The experimental results demonstrate that the new filtering scheme achieves higher accuracy and saves more human resources compared to the current state-of-the-art method.

Network Based Intrusion Detection System Using Weighted Product Model (WPM)

A security technology called a network-based intrusion detection system (NIDS) was created to safeguard computer networks against unauthorised access and criminal activity. This technology works by analysing network traffic, spotting potential risks, and informing administrators of any possible incursions or attacks. NIDS research ensures that intrusion detection systems are built to minimise the gathering and storage of sensitive data by taking into account the value of privacy and data protection .In general, network-based intrusion detection system research has a major impact on how well these security measures operate, how efficiently they perform, and how adaptable they are.By addressing the evolving challenges posed by cyber threats, NIDS research helps organizations enhance their network security posture, protect sensitive information, and defend against potential intrusions and attacks." The weighted product model (WPM), a multi-criteria decision-making (MCDM) technique, is used to evaluate and rank solutions based on a variety of distinct criteria. It provides a methodical approach to decision-making by considering the relative importance of each attribute and the performance of other solutions in relation to those criteria. The WPM normalises the data, weights the criteria, and gives a weighted score for each alternative. The option with the greatest score is regarded as the ideal option. The weighted product model offers a structured framework for making decisions by taking into account many factors and their varying degrees of importance. It enables decision-makers to assess and contrast options using a wide range of criteria, resulting in more informed and unbiased choices. It's crucial to check nonetheless that the model's weights and normalisation techniques appropriately capture the decision-maker's preferences as well as the features of the choice problem.J48, Random Forest, JRIP, RIDOR, PART. The definition of true positive, false positive, true negative and false negative rates has already been established. These metrics for measuring the effectiveness of classification algorithms, anomaly detection systems, and binary decision-making processes are accurately presented. As can be seen from the results, J48 received the highest rank, while PART received the lowest .In order to increase the security of computer networks, network-based intrusion detection systems (NIDS) are essential. They provide real-time monitoring and analysis of network traffic to identify suspected breaches and malicious activities, enabling appropriate action to be taken. However, it is important to recognize that NIDS can have limitations and are not infallible.

Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks.

The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant security risks, including user information theft and denial-of-service attacks. This paper introduces a smart, network-based Intrusion Detection System (IDS) designed to protect IoT networks from distributed denial-of-service attacks. Our methodology involves generating synthetic images from flow-level traffic data of the Bot-IoT and the LATAM-DDoS-IoT datasets and conducting experiments within both supervised and self-supervised learning paradigms. Self-supervised learning is identified in the state of the art as a promising solution to replace the need for massive amounts of manually labeled data, as well as providing robust generalization. Our results showcase that self-supervised learning surpassed supervised learning in terms of classification performance for certain tests. Specifically, it exceeded the F1 score of supervised learning for attack detection by 4.83% and by 14.61% in accuracy for the multiclass task of protocol classification. Drawing from extensive ablation studies presented in our research, we recommend an optimal training framework for upcoming contrastive learning experiments that emphasize visual representations in the cybersecurity realm. This training approach has enabled us to highlight the broader applicability of self-supervised learning, which, in some instances, outperformed supervised learning transferability by over 5% in precision and nearly 1% in F1 score.

TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart devices within these networks are employed to handle sensitive information. However, the scale and openness of IoT networks make them highly vulnerable to security breaches and attacks, such as eavesdropping, weak authentication, and malicious payloads. Hence, there is a need for advanced machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS). Existing ML-based IoT-IDSs face several limitations in effectively detecting malicious activities, mainly due to imbalanced training data. To address this, this study introduces a transformer neural network-based intrusion detection system (TNN-IDS) specifically designed for MQTT-enabled IoT networks. The proposed approach aims to enhance the detection of malicious activities within these networks. The TNN-IDS leverages the parallel processing capability of the Transformer Neural Network, which accelerates the learning process and results in improved detection of malicious attacks. To evaluate the performance of the proposed system, it was compared with various IDSs based on ML and DL approaches. The experimental results demonstrate that the proposed TNN-IDS outperforms other systems in terms of detecting malicious activity. The TNN-IDS achieved optimum accuracies reaching 99.9% in detecting malicious activities.

A comparative analysis of gradient boosting, random forest and deep neural networks in intrusion detection system

The growing threat of advanced security attacks targeting enterprise information systems raises the need for novel security solutions that promptly identify and respond to these issues. These security strategies must automate threat detection and response in enterprise settings, enabling organizations to address emerging threats, ongoing attacks, and imminent risks adequately. Traditional security strategies that rely on rule-based approaches for intrusion detection systems are inefficient in achieving these objectives due to their limited capabilities in identifying new threats. As a result, machine learning strategies have been proposed to address these needs, offering an intelligent detection environment for novel threats. Classification algorithms such as random forest, gradient boosting and deep learning techniques like deep neural networks have been proposed in various studies. This paper examines the performance of these models, providing a comparative review of their detection capabilities based on precision, recall, accuracy, specificity, and sensitivity. The models are tested using a Python environment due to the extensive machine learning capabilities. These tests show that random forest is the ideal model for network-based intrusion detection systems

Utilizing Machine Learning for Intrusion Detection Systems in the Context of Cloud Computing

The rapid advancements in the internet and communication sectors have led to a huge expansion in the network dimension and the related data. The proliferation of new assault kinds as a result makes it challenging for community safety to accurately identify invasions. Furthermore, the presence of intruders who want to launch a lot of attacks against the network cannot be ignored. An intrusion detection device (IDS) is one such instrument that protects against prospective intrusions by monitoring community communication to guarantee its confidentiality, integrity, and availability. IDS continues to struggle with innovative intrusion detection, lowering false alarm rates, and improving detection accuracy despite substantial research backing. An intrusion detection system's main job is to protect resources against threats. It predicts user behaviour based on analysis, and determines whether that behaviour constitutes an assault or is simply normal behaviour. We use Support Vector Machine (SVM) and Rough Set Theory (RST) to detect network breaches (SVM). Computer learning (ML) and Python-based total IDS systems are recently being employed to identify intrusions across the network in an ecologically friendly manner. The taxonomy presented in this article is mostly based on the impressive machine learning and Python approaches used to develop network-based IDS (NIDS) systems. The article begins by defining IDS.

Packet Chunking for File Detection

Network-based intrusion detection and data leakage prevention systems inspect packets to detect if critical files such as malware or confidential documents are transferred. However, this kind of detection requires heavy computing resources in reassembling packets and only well-known protocols can be interpreted. Besides, finding similar files from a storage requires pairwise comparisons. In this paper, we present a new network-based file identification scheme that inspects packets independently without reassembly and finds similar files through inverted indexing instead of pairwise comparison. We use a content-based chunking algorithm to consistently divide both files and packets into multiple byte sequences, called chunks. If a packet is a part of a file, they would have common chunks. The challenging problem is that packet chunking and inverted-index search should be fast and scalable enough for packet processing. The file identification should be accurate although many chunks are noises. In this paper, we use a small Bloom filter and a two-level threshold strategy to solve the problems. To the best of our knowledge, this is the first scheme that identifies a specific critical file from a packet over unknown protocols. Experimental results show that the proposed scheme can successfully identify a critical file from a packet without packet reassembly.

Secure Intrusion Detection by Differentially Private Federated Learning for Inter-Vehicle Networks

Along with providing several benefits, the unprecedented growth of connected and automated vehicles brings worries about damaging cyber attacks. Network-based intrusion detection systems (IDSs) using deep learning methods can effectively mitigate the threats by promptly detecting malicious behaviors. However, the centralized learning mode may cause data leverage. Federated learning has emerged as a new distributed machine learning training paradigm to preserve data privacy by allowing clients to train and validate models locally with their data and then send the model parameters to the central server. First, we propose a new framework named DPFL-F2IDS scheme for an edge inter-vehicle network that transmits Basic Safety Messages, which consists of Differentially Private Federated Learning (DPFL) and F2IDS (Framework for IDS). DPFL can defend against the member inference attacks faced by the standard federated learning, but difficulties still exist in making a tradeoff on the utility metrics and privacy metrics. Second, experiments by centralized learning methods were performed on the VeReMi Extension dataset. Third, the performance of federated learning by different numbers of vehicles and different optimizers is evaluated. DPFL by different noise values is also evaluated. Results clarified that the F1-scores reached 0.9915 and 0.9700 by long short-term memory (LSTM)-based intrusion detection for binary classification and multi-classification in the centralized learning mode, respectively. The utility results achieved by federated averaging (FedAvg) with Adabound optimizer were closer to the centralized learning mode than the classical FedAvg algorithms. The optimal values of the noise multipliers were also found without degrading the model quality and preserving privacy.

Adv-Bot: Realistic adversarial botnet attacks against network intrusion detection systems

Due to the numerous advantages of machine learning (ML) algorithms, many applications now incorporate them. However, many studies in the field of image classification have shown that MLs can be fooled by a variety of adversarial attacks. These attacks take advantage of ML algorithms’ inherent vulnerability. This raises many questions in the cybersecurity field, where a growing number of researchers are recently investigating the feasibility of such attacks against machine learning-based security systems, such as intrusion detection systems. The majority of this research demonstrates that it is possible to fool a model using features extracted from a raw data source, but it does not take into account the real implementation of such attacks, i.e., the reverse transformation from theory to practice. The real implementation of these adversarial attacks would be influenced by various constraints that would make their execution more difficult. As a result, the purpose of this study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems (NIDS), demonstrating that it is entirely possible to fool these ML-based IDSs using our proposed adversarial algorithm while assuming as many constraints as possible in a black-box setting. In addition, since it is critical to design defense mechanisms to protect ML-based IDSs against such attacks, a defensive scheme is presented. Realistic botnet traffic traces are used to assess this work. Our goal is to create adversarial botnet traffic that can avoid detection while still performing all of its intended malicious functionality.

Network based Intrusion Detection System using the SPSS Method

An invasion detection system (NIDS) that makes use of networks can spot malicious activities on a network. For NIDS to inspect each and every packet of communication, including unicast traffic, invalid network access is often required. Along with being small structures that don't block traffic, NIDS are able to keep an eye on usernames and passwords, check for any signs of odd activity in the file integrity logs, port logs, mysql database file storage, etc. and alert the part of implementing as necessary. Since NIDS keeps an eye on live data, it can spot problems as they happen. On the other hand, HIDS uses historical data to identify skilled hackers who employ cutting-edge methods that are difficult to detect in real-time. A monitoring system called an intrusion notification system (IDS) looks for abnormal activity and sends out alarms when it does. A Counter Terrorism Center (SOC) analyst senior incident handler can look into the issue and take the necessary steps to resolve the threat based on these notifications. Every incoming packets are read by an NIDS, which scans them for any anomalous activity. Depending on the gravity of the danger, steps can be taken, such as alerting system administrators or preventing the initial IP address from connecting to the network. In the past decades, due to developments Increased use of networking techniques and internet, digital. Contacts entered into everything that happens on the world market. The hacker penetration attempts are taking place in parallel with these advances. Networks are expanding as well. Without permission, they attempted to modify some network data or increase network traffic in order to launch a denial-of-service attack. Intrusion prevention systems (IDS) are also favoured, especially for detecting malicious within a network system, even though a firewall may appear like an useful solution to avoid this type of attack. Machine learning algorithms have helped IDS become more successful in recent years, depending on the consequences of the training/learning process. Knowing what is important is very difficult and the learning algorithm is fast according to the problem Type. The complexity of the task, the size of the data sets, the number of nodes, the network design, the intended error rate, etc. all affect the choice of algorithm. Examine several network training functions in artificial neural networks with numerous layers created to provide effective intrusion detection systems. test outcomes The study provides evidence of the usefulness of the approach. Considering their speed and true-positive detection rates Death Penalty. SPSS statistics is multivariate analytics, business intelligence, and criminal investigation data management, advanced analytics, developed by IBM for a statistical software package. A long time, spa inc. Was created by, IBM purchased it in 2009. The brand name for the most recent versions is IBM SPSS statistics. Network Security, Intrusion Detection System, Neural Networks and Training Functions. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Emotional Intelligence the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

Data Auditing for Intelligent Network Security Monitoring

Data auditing is a process to consistently keep the quality of data high, but this process is generally missing in network security monitoring. When network-based intrusion detection systems catch any suspicious packet, they generate alert messages that are further investigated by security analysts. An alert is generally assigned to only one analyst at best, and then the analyst determines whether the alert is true or false, called labeling. Therefore, different analysts may label very similar alerts with different labels. In this article, we introduce this problem of inconsistent labeling in network security monitoring and present a new automatic data auditing method to check if any human mistake has occurred for the labeling. Through our experiments on two data sets, a private one from a real security operations center and an open data set for reproducible experiments, we confirm that the new auditing method can catch incorrect labels, and the accuracy of a machine learning model on the data set can be enhanced through the label correction.

Deep Learning Model Transposition for Network Intrusion Detection Systems

Companies seek to promote a swift digitalization of their business processes and new disruptive features to gain an advantage over their competitors. This often results in a wider attack surface that may be exposed to exploitation from adversaries. As budgets are thin, one of the most popular security solutions CISOs choose to invest in is Network-based Intrusion Detection Systems (NIDS). As anomaly-based NIDS work over a baseline of normal and expected activity, one of the key areas of development is the training of deep learning classification models robust enough so that, given a different network context, the system is still capable of high rate accuracy for intrusion detection. In this study, we propose an anomaly-based NIDS using a deep learning stacked-LSTM model with a novel pre-processing technique that gives it context-free features and outperforms most related works, obtaining over 99% accuracy over the CICIDS2017 dataset. This system can also be applied to different environments without losing its accuracy due to its basis on context-free features. Moreover, using synthetic network attacks, it has been shown that this NIDS approach can detect specific categories of attacks.

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

Network-based Intrusion Detection System (NIDS) forms the frontline defence against network attacks that compromise the security of the data, systems, and networks. In recent years, Deep Neural Networks (DNNs) have been increasingly used in NIDS to detect malicious traffic due to their high detection accuracy. However, DNNs are vulnerable to adversarial attacks that modify an input example with imperceivable perturbation, which causes a misclassification by the DNN. In security-sensitive domains, such as NIDS, adversarial attacks pose a severe threat to network security. However, existing studies in adversarial learning against NIDS directly implement adversarial attacks designed for Computer Vision (CV) tasks, ignoring the fundamental differences in the detection pipeline and feature spaces between CV and NIDS. It remains a major research challenge to launch and detect adversarial attacks against NIDS. This article surveys the recent literature on NIDS, adversarial attacks, and network defences since 2015 to examine the differences in adversarial learning against deep neural networks in CV and NIDS. It provides the reader with a thorough understanding of DL-based NIDS, adversarial attacks and defences, and research trends in this field. We first present a taxonomy of DL-based NIDS and discuss the impact of taxonomy on adversarial learning. Next, we review existing white-box and black-box adversarial attacks on DNNs and their applicability in the NIDS domain. Finally, we review existing defence mechanisms against adversarial examples and their characteristics.

Ensemble Framework of Artificial immune system based on Network Intrusion Detection System for Network Security Sustainability

The popularity and rapid growth of the internet have reemphasized the importance of intrusion detection systems (IDS) significance in the network security. IDS decreases hacking, data theft risk, privacy intrusion, and others. To save the system from external and internal intruders, the primary approaches of IDS are used. Many techniques[13], like genetic algorithms, artificial neural networks, and artificial immune systems, have been applied to IDS. This paper describes an Ensemble Framework of Artificial Immune System (AIS) based on Network Intrusion Detection System. Without placing a significant additional load on networks and monitoring systems, the large volume of data is analysed by a network-based Intrusion Detection System (NIDS). For determining the connection type, data from KDD Cup 99 competitions is utilized. To differentiate between attacks and valid connections, IDS can be utilized. Optimized feature selection is used to speed up the time-consuming rough set. The results obtained from the IDS system indicate that it can effectively identify the attacking connections with a high success rate.

Classification of Adversarial Attacks Using Ensemble Clustering Approach

As more business transactions and information services have been implemented via communication networks, both personal and organization assets encounter a higher risk of attacks. To safeguard these, a perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks, where obfuscation techniques are applied to disguise patterns of intrusive traffics. The current research focuses on non-payload connections at the TCP (transmission control protocol) stack level that is applicable to different network applications. In contrary to the wrapper method introduced with the benchmark dataset, three new filter models are proposed to transform the feature space without knowledge of class labels. These ECT (ensemble clustering based transformation) techniques, i.e., ECT-Subspace, ECT-Noise and ECT-Combined, are developed using the concept of ensemble clustering and three different ensemble generation strategies, i.e., random feature subspace, feature noise injection and their combinations. Based on the empirical study with published dataset and four classification algorithms, new models usually outperform that original wrapper and other filter alternatives found in the literature. This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks, and the second that focuses on recognizing obfuscated intrusions. In addition, analysis of algorithmic parameters, i.e., ensemble size and level of noise, is provided as a guideline for a practical use.