Machine Learning Based Intrusion Detection System

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. …

Recently, machine learning methodologies are playing an important role in detecting network intrusions (or attacks), which further helps the network administrator to take precautionary measures for preventing intrusions. In this project report, the authors propose to use various techniques like support vector machine, logistic regression, random forest, decision tree, and a hybrid model using artificial neural networks and random forest. The new hybrid model for network intrusion detection aims to achieve better precision, recall, accuracy, and F1 score. To reify this, combined artificial neural network and random forest algorithms are used. In the hybrid model, ANN works as a feature extractor and random forest as a classifier. The data is provided to ANN and the ANN generates new features on the basis of given input. The new features are then provided to random forest as an input. The random forest then classifies the data into different classes on the basis of new data. Here, KddCup99 dataset has been used to evaluate the models and accuracy achieved in each model.

Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. Machine learning (ML) and deep learning (DL) based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. The primary objective of this research is to generate tags and descriptions for the packets that are difficult to classify by the NIDS. Most NIDS datasets that are publicly available have focused on flow data, offering aggregated information about network connections, and have played a crucial role in enabling researchers and network security professionals to design and develop flow-based NIDS solutions. While flow records provide valuable information for detecting network-level anomalies and attacks, they do not consider packet-level information and payload contents. In this research, we propose a packet-level approach for NIDS that leverages the flow information with the packet header fields and payload. To facilitate this research, we have curated a comprehensive Packet-level dataset constructed by extracting the Packet Capture (PCAP) files from two widely used flow-level datasets, namely CIC-IDS2017 and UNSW-NB15. Recent advancements in Natural Language Processing (NLP) have demonstrated the effectiveness of Transformer-based models in handling sequence data with tasks such as token classification and text generation. We have adapted this technology to NIDS to extract key features and characteristics of the header and payload in the context of various attacks. Unlike traditional classification methods that assign predefined labels to network packets, this method focuses on generating tags based on the packet signature that explains the packet content and potential risks. The tags and descriptions offer network security professionals a tool to comprehend suspicious packets with an unfamiliar or potentially malicious signature, assess their nature, and help make informed decisions promptly.

Network intrusion detection systems, or NIDS, are essential for defending computer networks from several types of security threats and assaults. There is a growing need within network attacks become more complicated and frequent, sophisticated analytics methods to improve NIDS's capacity for detection and reaction. The creation and application of analytics techniques for network intrusion detection systems is the main topic of this study. Utilizing these methods canhelp NIDS be more accurate, efficient, and effective at detecting and mitigating security breaches. The first part of the study looks at the foundational ideas of network intrusion detection, including the various kinds of attacks and the difficulties in detecting them. The advantages of several NIDS systems, such as signature-based and anomaly-based systems, are highlighted and restricted. The overall goal of this research is to enhance the field of network intrusion detection by using analytics approaches to increase NIDS's capabilities. The suggested techniques can decrease false positives, enhance automated incident response, process large amounts of data more effectively, and increase the accuracy of attack detection. In the face of constantly changing cyber threats, the research findings will aid in the creation of stronger and more efficient network security solutions. Keywords - Network intrusion detection systems, Analytics techniques, Security breaches, False positives, Cyber threats

This paper describes the ESIDE-Depian intrusion detection and prevention system, which uses Bayesian structural and parametric learning and also evidence propagation and adaptation, in order to improve the accuracy and manageability of network intrusion detection systems (NIDS). Current NIDS do not consider the two main detection paradigms, i.e. misuse detection and anomaly detection, in an unified style, so the analysis is not inherently complete. Besides, historical data are not generally used, neither for analysis nor for sequential adaptation of the knowledge representation models used for detection; hence this wealthy information about the essence and the potential trends of the target system is not commonly considered. Thus, by the generalized use of Bayesian belief networks, ESIDE-Depian achieves the main goal of detecting and preventing both well-known and also zero-day attacks with excellent results, by means of unified real-time analysis of network traffic.

Network intrusion detection systems (NIDS) are essential for protecting computer networks against cyberattacks. The selection of a nominal set of essential features that may adequately discriminate malicious traffic from normal traffic is indispensable while developing a NIDS. As such, a more reliable and accurate detection result may be realized when intrusion detection is carried out on a dataset based on an inclusive feature representation. This work presents the pre-processing and feature selection workflow as well as its results in the case of the CIC-IDS-2017 dataset, with a focus on two cyber-attacks, namely Denial-of-Service (DoS) and PortScan. The study applied an ensemble feature selection method based on information gain and Random Forest to filter out important features. Recursive Feature Elimination method was then applied to the reduced features to optimize the selected feature subset. The selected feature subset was experimented with using two classification algorithms, namely support vector machine and multi-layer perceptron. In the evaluation process, four widely used performance metrics were considered. The study results demonstrated the efficacy of the proposed ensemble approach to optimize the selected feature subset for detecting PortScan and DoS attacks in network traffic. Experimental results revealed that the support vector machine had a slight advantage in accuracy and could train more quickly. According to the study's evaluation, the NIDS may be able to shorten processing times without sacrificing the ability to detect PortScan and DoS attacks accurately by choosing a narrow subset of informative features. This suggests the approach might be applicable to real-world NIDS scenarios involving these attacks. The study also provides encouraging perspectives on how ensemble feature selection utilizing MLP and SVM can enhance the effectiveness of NIDS. Building on these findings, more research can create NIDS solutions that are even more reliable and efficient for the dynamic field of cybersecurity.

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

Effective network intrusion detection using anomaly scores from unsupervised machine learning models depends on the performance of the models. Although unsupervised models do not require labels during the training and testing phases, the assessment of their performance metrics during the evaluation phase still requires comparing anomaly scores against labels. In real-world scenarios, the absence of labels in massive network datasets makes it infeasible to calculate performance metrics. Therefore, it is valuable to develop an algorithm that calculates robust performance metrics without using labels. In this paper, we propose a novel algorithm, Expectation Maximization-Area Under the Curve (EM-AUC), to derive the Area Under the ROC Curve (AUC-ROC) and the Area Under the Precision-Recall Curve (AUC-PR) by treating the unavailable labels as missing data and replacing them through their posterior probabilities. This algorithm was applied to two network intrusion datasets, yielding robust results. To the best of our knowledge, this is the first time AUC-ROC and AUC-PR, derived without labels, have been used to evaluate network intrusion detection systems. The EM-AUC algorithm enables model training, testing, and performance evaluation to proceed without comprehensive labels, offering a cost-effective and scalable solution for selecting the most effective models for network intrusion detection.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

Cyberspace is a concept describing a widespread, interconnected digital technology with numerous users. New standards add more concerns with tremendous information gathered from various network sources, which can be utilized for focused cyber-attacks. Digital attacks in networks are getting more complex and subsequently introducing expanding difficulties in precisely distinguishing network intrusions. Inability to forestall the network intrusions could debase the validity of safety administrations, for example, information privacy, trustworthiness, and accessibility. In this work, we center around developing a Network Intrusion Detection System which is implemented utilizing Machine Learning Techniques. IDS dependent on ML techniques are successful and exact in distinguishing varied networks intrusions. Also, many of the ML-based IDS experience the ill effects of an increment in false-positive rate, leading to lower precision and accuracy. Consequently, we present an exploration of the UNSW-NB15 intrusion detection dataset that will be utilized for preparing the machine learning models. In our tests, we carry out various ML approaches namely, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest Classifier (RF), Decision Tree (DT), Gradient boosted decision trees (GBDT). The results demonstrated that the hyperparameter optimization technique using Grid Search and Random Search methods and evaluation using k-fold cross-validation gives better optimization of model parameters and henceforth brings about better execution.KeywordsCross-validationCyberattackDecision tree (DT)Gradient boosted decision trees (GBDT)Hyperparameter TuningLogistic regression (LR)Network intrusion detection system (IDS)Random forest classifier (RF)Support vector machine (SVM)

Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and block attack-related network traffic. The network control is a complex model. Implementation of an IDS could make delay in the network. Several software-based network intrusion detection systems are developed. However, the model has a problem with high speed traffic. This paper reviews of many type of software architecture in intrusion detection systems and describes the design and implementation of a high-performance network intrusion detection system that combines the use of software-based network intrusion detection sensors and a network processor board. The network processor which is a hardware-based model could acts as a customized load balancing splitter. This model cooperates with a set of modified content-based network intrusion detection sensors rather than IDS in processing network traffic and controls the high-speed.

Network intrusion detection system (NIDS) is a tool that can detect various network attacks by analyzing network traffic. In recent years, traditional machine learning and deep learning methods have been widely used in NIDS. And these works achieved high detection rate and low false positive rate when identifying common attacks (such as DoS). However, the existing models performed poor in detecting rare or unseen network attacks. In this paper, we design a novel network intrusion detection model based on two-phase detection and manually labeling. This two-phase detection model (TPDM) is a multi-classification system, and it can classify network traffic into benign or specific attack type. TPDM gains the overall accuracy 98.35%, and average F1-score 99.23% on UNSW-NB15 dataset. The experiment shows that TPDM performs better than state of the art in detecting rare attacks with few training samples. Besides, it can detect unknown network attacks and then label these attacks.

TS-IDS: Traffic-aware self-supervised learning for IoT Network Intrusion Detection

As the reliance on digital networks grows, the need for robust security measures becomes paramount. It offers a thorough analysis of creating and assessing a Network Intrusion Detection System (NIDS) incorporating various machine learning algorithms. The selected algorithms consist of K Nearest Neighbour, Naïve Bayes, Decision Tree, Support Vector Machine, and Logistic Regression. Moreover, widely recognised ensemble learning models like CatBoost, AdaBoost, Gradient Boost, Random Forest, and Extreme Gradient Boost. The core objective is to improve the efficiency of intrusion detection by evaluating various machine-learning models. Utilizes feature selection techniques based on chi-square to enhance the relevance and effectiveness of the selected features. The evaluation metrics, particularly Matthews’ Correlation Coefficient (MCC) and ROC Curves, are used in assessing the models’ efficacy. They offer valuable insights into the models’ capability to differentiate between normal and malicious network activities. This study aims to advance cybersecurity by comparing classical machine learning and ensemble models for network intrusion detection. Significantly, our results underscore XGBoost’s effectiveness with 0.996 accuracy and 0.993 MCC, in enhancing digital network security.

In this paper the software system for network traffic analysis and intrusion detection has been developed. The system for monitoring and analyzing of traffic to detect anomalies and identify attacks based on Hurst parameter has been proposed. This system allows determining the Hurst flow parameter at different intervals of observation. This system can be installed on the network provider to more effectively use the bandwidth by balancing the load between different protocols and subscribers.