Intrusion Detection Analysis Research Articles

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Performance Analysis of Anomaly-Based Network Intrusion Detection Using Feature Selection and Machine Learning Techniques

Performance Analysis of Anomaly-Based Network Intrusion Detection Using Feature Selection and Machine Learning Techniques

Machine Learning to Enhance Security Systems

This research explores the integration of machine learning into security systems, addressing the growing challenges posed by cyber threats. It traces the historical context of cybersecurity measures and the development of machine learning in intrusion detection, anomaly detection, and behavioral analysis. By examining traditional security approaches and their limitations, the study highlights the transformative potential of machine learning. Case studies provide concrete examples of successful applications, demonstrating how machine learning reduces cyber threats. Ethical considerations, implementation challenges, biases, and regulatory aspects are discussed, highlighting the complexities of integrating machine learning into security frameworks. Furthermore, the research explores emerging technologies in cybersecurity and offers insights into the future of machine learning in security. In conclusion, the importance of continuous research is emphasized, positioning machine learning as a dynamic force shaping the future of digital defense and overcoming various challenges in the cybersecurity landscape. Index Terms— Machine Learning, Security Systems, Cybersecurity.

Performance analysis of network intrusion detection using T-Pot honeypots

Performance analysis of network intrusion detection using T-Pot honeypots

Analysis of open-source intrusion detection and prevention systems for integration with Russian operating systems

The article reviewed IDS/IPS such as Snort, Suricata, Fail2Ban and OSSEC, which have opensource code. Their mechanisms for ensuring network security, including architecture, functions, tools and implemented tasks, are analyzed. The possibility of integrating these systems with Russian operating systems is considered. In conclusion, it is emphasized that IDS/IPS are just one of the many layers of protection that must be implemented to ensure effective security. Only an integrated approach to security can be the key to protecting against modern cyber threats.

Attention-based ResNet for intrusion detection and severity analysis using sliding window blockchain and firewall in IoT

Attention-based ResNet for intrusion detection and severity analysis using sliding window blockchain and firewall in IoT

Securing Industrial Control Systems: A Deep Dive into AI Approaches

In the era of increasingly interconnected infrastructures and the emergence of the Internet of Things (IoT), industrial control systems (ICS) are encountering a growing susceptibility to cyber threats. This paper delves into the growing adoption of artificial intelligence (AI) as a means to mitigate cybersecurity risks for ICS. AI-driven solutions play a pivotal role in detecting anomalies, pinpointing potential threats, and executing real-time responses to curtail risks. The authors extensively discuss the application of AI-based cybersecurity measures for ICS, encompassing techniques like anomaly detection, intrusion detection, and behavioral analysis. Furthermore, the narrative explores the challenges associated with implementing AI-centric cybersecurity solutions, such as concerns related to data privacy, system intricacies, and the imperative for continuous monitoring and updates. The authors draw upon case studies showcasing successful deployments of AI-driven cybersecurity solutions in industrial settings. Concluding the discussion, the authors propose a novel hybrid machine learning approach designed for anomaly detection in ICSs. This chapter serves as a comprehensive resource, shedding light on the role of AI in fortifying cybersecurity for industrial control systems. Its insights aim to enhance resilience against cyber-attacks, minimize the potential for system disruptions, and mitigate the risk of data breaches.

Modern approaches to information security: analysis of intrusion detection, firewalls and auditing in the context of organizational data protection

Today’s business environment presents organizations with growing information security challenges. The threats posed by cyber-attacks and the leakage of sensitive information require effective and innovative approaches to data protection. This paper explores a combination of tools and techniques, including intrusion detection system (IDS), firewalls, and information security auditing, as key elements of organizational data protection. The paper provides a detailed overview of each of these components and discusses their roles in information security. For an intrusion detection system, its ability to detect and respond to different types of attacks in real time is investigated. For firewalls, consider how they restrict access to network resources and regulate traffic. Finally, information security audits act as a means of assessing the effectiveness of security measures and identifying weaknesses. Statistical data and best practices are presented to support our recommendations. This article discusses the integration of these systems in a comprehensive information security strategy and provides tips for maximizing data protection in today’s digital world.

A comprehensive review of machine learning's role in enhancing network security and threat detection

As network security threats continue to evolve in complexity and sophistication, there is a growing need for advanced solutions to enhance network security and threat detection capabilities. Machine learning (ML) has emerged as a powerful tool in this context, offering the potential to detect and mitigate threats in real-time by analyzing vast amounts of network data. This comprehensive review explores the role of machine learning in enhancing network security and threat detection. The review begins by providing an overview of the current landscape of network security threats and the challenges faced by traditional security approaches. It then delves into the fundamental principles of machine learning and its application to network security. Various machine learning techniques, including supervised learning, unsupervised learning, and deep learning, are discussed in detail, highlighting their strengths and limitations in the context of threat detection. Next, the review examines the application of machine learning in different aspects of network security, including intrusion detection, malware detection, anomaly detection, and behavioral analysis. Case studies and real-world examples are presented to illustrate the effectiveness of machine learning-based approaches in identifying and mitigating security threats. Furthermore, the review discusses the challenges and considerations associated with deploying machine learning in network security environments, such as data privacy, model interpretability, and adversarial attacks. Strategies for addressing these challenges and improving the robustness of machine learning models are explored. Finally, the review outlines future research directions and opportunities for leveraging machine learning to enhance network security. Areas such as federated learning, adversarial machine learning, and explainable AI are identified as promising avenues for further investigation. In summary, this comprehensive review provides insights into the potential of machine learning in enhancing network security and threat detection. By leveraging the capabilities of machine learning algorithms and techniques, organizations can strengthen their defenses against cyber threats and better protect their networks and sensitive data.

Stacked denoised auto-encoding network-based kernel principal component analysis for cyber physical systems intrusion detection in business management

At present, the network data under the environment of industrial information physical system is larger and more complex. Traditionally, feature extraction by machine learning is cumbersome and computation-intensive, which is not conducive to anomaly detection of industrial network data. To solve the above problems, this paper proposes a stacked denoised auto-encoding network based on kernel principal component analysis for industrial cyber physical systems intrusion detection. Firstly, a novel kernel principal component analysis method is used to reduce the data feature dimension and obtain a new low-dimension feature data set. Then, a multi-stacked denoised auto-encoding network model is used to classify and identify the data after dimensionality reduction by voting. Experimental results show that the proposed method has better classification performance and detection efficiency by comparing the state-of-the-art intrusion detection methods.

Performance Analysis of Machine Learning-Based Intrusion Detection with Hybrid Feature Selection

Performance Analysis of Machine Learning-Based Intrusion Detection with Hybrid Feature Selection

Analysis of Network Intrusion Detection Based on Semi-Supervised and SS-DGM

Analysis of Network Intrusion Detection Based on Semi-Supervised and SS-DGM

Devising a method for detecting “evil twin” attacks on IEEE 802.11 networks (Wi-Fi) with KNN classification model

The object of research is IEEE 802.11 (Wi-Fi) networks, which are often the targets of a group of attacks called "evil twin". Research into this area is extremely important because Wi-Fi technology is a very common method of connecting to a network and is usually the first target of cybercriminals when they attack businesses. With the help of a systematic analysis of the literature focused on countering attacks of the "evil twin" type, this work identifies the main advantages of using artificial intelligence systems in the analysis of network data and identification of intrusions in Wi-Fi networks. To evaluate the effectiveness of intrusion detection and cybercrime analysis, a number of experiments as close as possible to real attacks on Wi-Fi networks were conducted. As part of the research reported in this paper, a method is proposed for detecting cybercrimes in IEEE 802.11 (Wi-Fi) wireless networks using artificial intelligence, namely a model built on the basis of the k-nearest neighbors method. This method is based on the classification of previously collected data, namely the signal strength from the access point, and then continuous comparison of the newly collected data with the trained model. A compact and energy-efficient prototype of a hardware and software system has been designed for the implementation of monitoring, analysis of ethernet network packets and data storage based on time series. In order to reduce the load on the computer network and taking into account the limited computing power of the system, a method of data aggregation was proposed, which ensures fast transfer of information. The results, namely 100 % of test cases (more than 7 thousand), were classified correctly, which indicates that the chosen method of data analysis will significantly increase the security of information and communication systems at the state and private levels

Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security

Secure data transmission in wireless mesh networks is a necessary attribute for machine learning-based intrusion detection systems (IDS). Numerous attacks may have an adverse effect on the system's computing efficiency. In order to accurately detect an attack and enable data protection, Whale with Cuckoo search optimization (WCSO) based quantum neural network (QNN) and elliptical curve cryptography (ECC) is presented. Whale optimization algorithm (WOA) is used to choose the features in the network data that aid in precisely detecting intrusions. To identify attacks, the optimized quantum network which combines the WOA approach with the feed-forward and backpropagation algorithms, is used. Sensitive data retrieving requires an encryption procedure that is enabled by the ECC algorithm, which could safely save the data files in the server, in order to, secure the documentation with security measures. In the event that the data owner maintains sensitive data on a server, the document is encrypted using the encryption method. To determine the best key optimized ECC is used. The QNN with WOA-based IDS framework is a solid option for real-time intrusion detection analysis with high accuracy of 98.5%. Thus, the study has demonstrated that the suggested effort will also provide better secure data storage, resolving security concerns.

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network. The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent. The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol (SAW_WDA). The network has been analyzed by authentication protocol to avoid malware attacks. The data of network users will be collected and classified using multilayer perceptron gradient decision tree (MLPGDT) classifiers. Based on the classification output, the decision for the detection of attackers and authorized users will be identified. The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput, end-end delay, network security, network concept drift, and results based on classification with regard to accuracy, memory, and precision and F-1 score.

Network Intrusion Detection and Comparative Analysis Using Ensemble Machine Learning and Feature Selection

Network Intrusion Detection and Comparative Analysis Using Ensemble Machine Learning and Feature Selection

Ambient intelligence approach: Internet of Things based decision performance analysis for intrusion detection

In recent infrastructures, Internet of Things (IoT) have become an important technology for connecting various actuators and sensors over wireless networks. Due to increase in mission-critical infrastructures, we make use of these new technologies for reliable communication but their security is always not promising in terms of availability, confidentiality, integrity, and privacy of network services. Users can be compromised and vulnerable by a motivated malicious opponent unless they are not adequately protected by a robust defense. Due to this reason, an ambient intelligence approach for Intrusion Detection System (IDS) is required. In this research, ww proposed Ambient Approach based on Reinforcement Learning Integrated Deep Q-Neural Network (RL-DQN) model for WSNs and IoT in which it leverages the Markov decision process (MDP) formalism to enhance the decision performance in IDS. We deploy RL-DQN-IDS over Edge-cloud intrusion detection infrastructure in which binary attack classification of the network traffic is performed at the edge network while multi-attack classification is performed at the cloud network. To identify intrusions, we use a two-phase process that includes an initial learning phase that relies on RL, followed by a detection and classification phase that relies on DQN. We used four datasets namely UNSW-NB-15, BoTNeTIoT-L01, CICIDS2017 and IoTID20 with a smart house simulation environment configured with WSN and IoT technologies to evaluate performance. Accuracy, precision, and recall were all considered while assessing the dataset under consideration. When compared to five other machine learning models, the RL-DQN model method has demonstrated superior performance. This model outperforms the other five that were tested.

Monitoring OpenFlow Virtual Networks via Coordinated Switch-Based Traffic Mirroring

As network virtualization becomes ubiquitous, legacy hardware-based traffic monitoring systems are no longer viable for dynamic traffic inspection at arbitrary locations in virtual networks. In this paper, we present the design and evaluation of Open Virtual Tap (OVT), a software-defined solution to replace hardware taps for traffic monitoring in OpenFlow virtual networks by utilizing mirroring capabilities of OpenFlow switches. The key idea behind OVT is the joint configuration of all switches in the substrate physical network in order to efficiently mirror flows from all virtual networks. We show that such a design avoids inefficiencies that result from existing software-based traffic mirroring solutions in which each virtual network configures its own switches independently of other virtual networks. We evaluate OVT using model-driven simulations as well as Mininet experiments with realistic applications for intrusion detection and video telephony analysis. Specifically, in our experiments, we observe that OVT can achieve up to 20% improvement in flow coverage compared to existing traffic mirroring approaches.

Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions

In the last decade, huge growth is recorded globally in computer networks and Internet of Things (IoT) networks due to the exponential data generation, approximately zettabyte to a petabyte. Consequently, security issues have also been arisen with the network growth. However, intrusion detection in such big data is challenging. Smart homes, cities, grids, devices, objects, e-commerce, e-banking, e-government, etc., are different advanced applications of the evolving networks. Many Intrusion Detection Systems (IDS) have been developed recently due to most computer networks’ exposure to security and privacy threats. Data confidentiality, integrity, and availability damage will occur in case of IDS prevention failure. Conventional techniques are not effective enough to cope the advanced attacks. Advanced deep learning techniques have been proposed for automatic intrusion detection and abnormal behavior identification of networks. This research aims to provide an inclusive analysis of intrusion detection based on deep learning techniques followed by different intrusion detection systems. In this review, public network-based datasets of IDS are fully explored and analyzed. Deep learning techniques for IDS have been critically evaluated based on different performance metrics (accuracy, precision, recall, f-1 score, false alarm rate, and detection rate). Furthermore, existing challenges and possible solutions for networks security and privacy have been discussed.

KVMIveggur: Flexible, secure, and efficient support for self-service virtual machine introspection

Virtual machine introspection (VMI) has evolved into a widely used technique for purposes such as digital forensics, intrusion detection, and malware analysis. The recent integration of enhanced VMI capabilities into KVM further facilitates the use of VMI. A significant obstacle, however, remains: VMI usually requires highly privileged access to the host system. Existing research prototypes that address this issue either target only the Xen hypervisor, are extremely slow, offer only a subset of the desired functionality, or are hard to deploy in real-life systems. We present our flexible KVMIveggur architecture as a novel solution to these challenges. It offers three flavors of isolation (using containers, virtual machines, and network remote access) that all enable access control for secure self-service VMI in cloud environments. It enables the full use of passive and active VMI, supports continuous monitoring also during live VM migration, and can be tailored for low overhead and minimal resource utilization on the host system. The experimental evaluation of our prototype demonstrates the feasibility and the efficiency of our approach and provides detailed insights into the differences between the three flavors.