Development of Software System for Network Traffic Analysis and Intrusion Detection

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. …

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.

As explained in Chapter 7, sharing network traffic data has become a vital requirement in machine-learning (ML) algorithms when building an efficient and accurate network traffic classification and intrusion detection system (IDS). However, inappropriate sharing and usage of network traffic data could threaten the privacy of companies and prevent sharing of such data.This chapterpresents aprivacy-preserving strategy-based permutation framework, called PrivTra, in which data privacy, statistical properties, and data-mining utilities can be controlled at the same time. In particular, PrivTra involves the followings: (i) vertically partitioning the original dataset to improve the performance of perturbation; (ii) developing a framework to deal with various types of network traffic data, including numerical, categorical, and hierarchical attributes; (iii) grouping the portioned sets into a number of clusters based on the proposed framework; and (iv) accomplishing the perturbation process by altering the original attribute value with a new value (cluster centroid). The effectiveness of PrivTra is shown through several experiments, such as real network traffic, intrusion detection, and simulated network datasets. Through the experimental analysis, this chapter shows that PrivTra deals effectively with multivariate traffic attributes, produces compatible results as the original data, improves the performance of the five supervised approaches, and provides a high level of privacy protection.

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.

In recent times, secure communication protocols over web such as HTTPS (Hypertext Transfer Protocol Secure) are being widely used instead of plain web communication protocols like HTTP (Hypertext Transfer Protocol). HTTPS provides end-to-end encryption between the user and service. Nowadays, organizations use network firewalls and/or intrusion detection and prevention systems (IDPS) to analyze the network traffic to detect and protect against attacks and vulnerabilities. Depending on the size of organization, these devices may differ in their capabilities. Simple network intrusion detection system (NIDS) and firewalls generally have no feature to inspect HTTPS or encrypted traffic, so they rely on unencrypted traffic to manage the encrypted payload of the network. Recent and powerful next-generation firewalls have Secure Sockets Layer (SSL) inspection feature which are expensive and may not be suitable for every organizations. A virtual private network (VPN) is a service which hides real traffic by creating SSL-protected channel between the user and server. Every Internet activity is then performed under the established SSL tunnel. The user inside the network with malicious intent or to hide his activity from the network security administration of the organization may use VPN services. Any VPN service may be used by users to bypass the filters or signatures applied on network security devices. These services may be the source of new virus or worm injected inside the network or a gateway to facilitate information leakage. In this paper, we have proposed a novel approach to detect VPN activity inside the network. The proposed system analyzes the communication between user and the server to analyze and extract features from network, transport, and application layer which are not encrypted and classify the incoming traffic as malicious, i.e., VPN traffic or standard traffic. Network traffic is analyzed and classified using DNS (Domain Name System) packets and HTTPS- (Hypertext Transfer Protocol Secure-) based traffic. Once traffic is classified, the connection based on the server’s IP, TCP port connected, domain name, and server name inside the HTTPS connection is analyzed. This helps in verifying legitimate connection and flags the VPN-based traffic. We worked on top five freely available VPN services and analyzed their traffic patterns; the results show successful detection of the VPN activity performed by the user. We analyzed the activity of five users, using some sort of VPN service in their Internet activity, inside the network. Out of total 729 connections made by different users, 329 connections were classified as legitimate activity, marking 400 remaining connections as VPN-based connections. The proposed system is lightweight enough to keep minimal overhead, both in network and resource utilization and requires no specialized hardware.

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

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

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.

Network attacks have become necessary in today’s time due to increased network traffic. To determine whether network traffic is normal or anomalous a supervised machine learning system is developed. A network intrusion detection system (IDS) is a must-have piece of a security system. This proposed study aims to discover new patterns automatically from substantial quantities of network data, reducing time manually compiling intrusion and normal behavior patterns. The best model in terms of detection success rate was discovered using a supervised learning algorithm and feature selection method. AdaBoost outperforms Neural Network, kNN, and Naive Bayes in supervised machine learning with feature selection in this study, with a detection accuracy of 100.00%, 99.30%, 91.60%, and 99.70%, respectively. The Network Intrusion Detection dataset is used to classify network intrusions to evaluate the study and it has also been used in past studies. On the other hand, the proposed model proved to be more effective than other studies in terms of intrusion detection. The proposed approach can be used in various fields, including finance, health, and transportation. Furthermore, additional parameter tuning could be added, and different feature selection techniques could be used to improve the performance of the classifiers.

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)

The inherent complexity and heterogeneity of the Internet of Things (IoT) ecosystem present significant challenges for developing effective intrusion detection systems. While graph deep-learning-based methods have shown promise in cybersecurity applications, existing approaches primarily construct graphs based on physical network connections, which may not effectively capture node representations. This paper proposes a Top-K Similarity Graph Framework (TKSGF) for IoT network intrusion detection. Instead of relying on physical links, the TKSGF constructs graphs based on Top-K attribute similarity, ensuring a more meaningful representation of node relationships. We employ GraphSAGE as the Graph Neural Network (GNN) model to effectively capture node representations while maintaining scalability. Furthermore, we conducted extensive experiments to analyze the impact of graph directionality (directed vs. undirected), different K values, and various GNN architectures and configurations on detection performance. Evaluations on binary and multi-class classification tasks using the NF-ToN IoT and NF-BoT IoT datasets from the Machine-Learning-Based Network Intrusion Detection System (NIDS) benchmark demonstrated that our proposed framework consistently outperformed traditional machine learning methods and existing graph-based approaches, achieving superior classification accuracy and robustness.

Cybersecurity has risen to the pinnacle of technological concern due to the exponential growth in the variety and sophistication of cyberthreats. Network attacks are currently the most urgent problem facing contemporary civilization. To identify and stop hostile assaults inside networks, there has to be an intrusion detection system in place. In several industries, most notably information security, effective detection systems for intrusions are being developed using machine learning and deep learning. This is an investigation of how ML methods may be used to enhance cybersecurity defences, with a focus on network intrusion detection, prevention, and response. This study inspects the efficacy of machine learning, utilising CNN, ANN, and LSTM, and assesses them using F1-score, accuracy, recall, and precision. Outcomes demonstrate that CNN outperforms other models, achieving 99% in all key metrics, making them highly effective for detecting network intrusion. However, the study's reliance on the NSL-KDD dataset presents limitations, as it may not fully capture modern network intrusion. Future research should incorporate more recent datasets, real-time performance evaluations, and hybrid models to improve network intrusion detection accuracy and efficiency

GRU-based deep learning approach for network intrusion alert prediction

GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks