Intrusion Detection System Framework Research Articles

Novel IDS Framework: Integrating GWO Feature Selection with KAN Classification

The past few decades have seen significant lifestyle improvements through cutting-edge innovations like AI and fog computing enabled by the World Wide Web, which are crucial for practical applications involving both symmetrical and asymmetrical information distributions. However, these advancements also come with the risk off requent and dangerous cyber-attacks. To mitigate these unwanted malwares, researchers must develop and implement novel intrusion detection systems (IDSs). Challenges such as inadequate accuracy scores due to numerous unnecessary and ineffective features, poor identification of attacks through specific deep learning classification methods, the costly and inefficient use of labeled training databases, and the excessive time required for system development and evaluation hinder there liability and efficiency of IDSs.This paper proposes a hybrid approach that combines the Grey Wolf Optimizer (GWO) for feature selection and the Kolmogorov-Arnold Network(KAN) algorithm for classification to address these challenges.The GWO is utilized to extract important features through meta-heuristic methods, demonstrating its effectiveness in feature selection. The proposed model achieves a high accuracy score, precision, recall, and F1-score of 92.09%, indicating its capability to accurately identify both normal and intrusive activities. The Matthews Correlation Coefficient (MCC) of 0.9019 further validates the model's robustness.

Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment.

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.

Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions

Intrusion Detection Systems (IDS) are essential for securing computer networks by identifying and mitigating potential threats. However, traditional IDS face challenges related to scalability, privacy, and computational demands as network data complexity increases. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training on decentralized data sources while preserving data privacy. Each participant retains local data repositories, ensuring data sovereignty and precluding data sharing. Leveraging the FL framework, participants locally train machine learning models on their respective datasets, subsequently transmitting model updates to a central server for aggregation. The central server then disseminates the aggregated model updates to individual participants, collectively striving to bolster intrusion detection capabilities. This article presents a comprehensive survey of FL applications in IDS, covering core concepts, architectural approaches, and aggregation strategies. We evaluate the strengths and limitations of various FL methodologies for IDS, addressing privacy and security concerns and exploring privacy-preserving techniques and security protocols. Our examination of aggregation strategies within the FL framework for IDS aims to highlight their effectiveness, limitations, and potential enhancements.

A distributed and cooperative signature-based intrusion detection system framework for multi-channel man-in-the-middle attacks against protected Wi-Fi networks

A Multi-Channel Man-in-the-Middle (MC-MitM) attack is an advanced form of MitM attack, characterized by its ability to manipulate encrypted wireless communications between the Access Point (AP) and clients within a WiFi network. MC-MitM attacks can target any Wi-Fi client, regardless of the authentication method used with the AP. Notable examples of such attacks include Key Reinstallation Attacks and FragAttacks, which have impacted millions of WiFi systems worldwide, especially those involving Internet of Things devices. Current defense mechanisms are inadequate against these attacks due to interoperability challenges and the need for modifications to devices or protocols within the targeted Wi-Fi networks. This paper introduces a distributed and cooperative signature-based wireless intrusion detection mechanism designed for online passive monitoring to detect malicious traffic patterns during MC-MitM attacks in any environment, from apartments and houses to large areas like hotels, offices or industrial sites. We implemented the proposed framework on Raspberry Pis and evaluated it in real-world settings. Our evaluation demonstrates that this framework can effectively identify MC-MitM attacks with an average accuracy of 98% when deployed across different locations within our experimental testbed.

A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network

A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network

A hybrid machine learning framework for intrusion detection system in smart cities

A hybrid machine learning framework for intrusion detection system in smart cities

Unveiling the core of IoT: comprehensive review on data security challenges and mitigation strategies

The Internet of Things (IoT) is a collection of devices such as sensors for collecting data, actuators that perform mechanical actions on the sensor's collected data, and gateways used as an interface for effective communication with the external world. The IoT has been successfully applied to various fields, from small households to large industries. The IoT environment consists of heterogeneous networks and billions of devices increasing daily, making the system more complex and this need for privacy and security of IoT devices become a major concern. The critical components of IoT are device identification, a large number of sensors, hardware operating systems, and IoT semantics and services. The layers of a core IoT application are presented in this paper with the protocols used in each layer. The security challenges at various IoT layers are unveiled in this review paper along with the existing mitigation strategies such as machine learning, deep learning, lightweight encryption techniques, and Intrusion Detection Systems (IDS) to overcome these security challenges and future scope. It has been concluded after doing an intensive review that Spoofing and Distributed Denial of Service (DDoS) attacks are two of the most common attacks in IoT applications. While spoofing tricks systems by impersonating devices, DDoS attacks flood IoT systems with traffic. IoT security is also compromised by other attacks, such as botnet attacks, man-in-middle attacks etc. which call for strong defenses including IDS framework, deep neural networks, and multifactor authentication system.

An effective ensemble classification framework for intrusion detection system using LSTM autoencoder

In intrusion detection, the curse of dimensionality and the trade-off between maintaining a low false alarm rate and achieving a high detection rate are significant challenges. This research suggests a unique strategy based on dimensionality reduction methods to improve the performance of network intrusion detection systems (NIDS). Compressing high-dimensional network traffic data using a Long Short-Term Memory Autoencoder (LSTMAE) allows the reduced characteristics to be submitted to a classifier to identify anomalies that may indicate an attack. Using standard datasets, including Network Security Laboratory - Knowledge Discovery in Datasets (NSL-KDD), UNSW-NB15, and Canadian Institute for Cyber Security - Intrusion Detection Systems (CICIDS2017), the proposed model is tested with classifiers like Random Forest (RF) and LightGBM (Light Gradient Boosting Machines). It is hoped that by adopting this method, NIDS response times may be improved while costs associated with storing and processing data are minimized. Precision, recall, F-score, accuracy, detection rate (DR), and false alarm rate (FAR) are only a few of the performance measures used to assess the quality of the suggested models. The experimental findings show that the proposed LSTMAE model reduces prediction errors more effectively than classic machine learning techniques such as Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Autoencoder (AE), and Long Short-Term Memory (LSTM). The results also show that the proposed solution outperforms the state-of-the-art methods of detection accuracy and computing complexity using accuracy, precision, recall, F1_Score, detection rate, and FAR.

Intrusion Detection System on Cloud Computing using Ensemble SVM

This study introduces a specialized Intrusion Detection System (IDS) designed specifically for cloud computing environments. By leveraging the UNSW_NB15 dataset, it employs feature selection techniques such as SelectKBest and ANOVA to extract relevant features, thereby improving the overall performance of the model. The IDS framework encompasses data preprocessing, Ensemble SVM model training, and performance evaluation utilizing standard metrics. The key methodology revolves around training SVM models using bagging and boosting techniques on preprocessed data, resulting in resilient intrusion detection models. These models are subsequently utilized to determine whether a given set of input features signifies a network intrusion. Through experimental analysis, the research demonstrates the system's efficacy in accurately detecting network intrusions, shedding light on its robustness and dependability.

Optimization techniques for IDS‐Generated traffic congestion control in VANET

AbstractVehicular Ad‐hoc Network (VANET) is an emerging field of wireless networks that enables a variety of vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable and secure communication among nodes. However, IDS requires a significant amount of data to process for monitoring intrusive activities in the network. As a result, the volume of traffic increases, resulting in the network congestion. Motivated by this fact, this study provides an overview of the optimization techniques for VANET traffic congestion control. It discusses a state‐of‐the‐art analysis along with the requirements for IDS‐generated traffic congestion control. It highlights the congestion control approaches for the traffic generated by an IDS and identifies the challenges in this domain. This study also proposes a novel IDS framework for reducing IDS‐generated network traffic by combining the Local Outlier Factor and Random Forest classifier. The proposed study achieved a high precision while yielding low false positive and false negative rates. The study outperformed the existing studies with an increase in accuracy of 1.16% and a reduction in attack detection time of 1.1869 seconds. Additionally, it discusses the possible future research directions that can be applied to address the issues of IDS‐generated traffic congestion. Overall, this study serves as a comprehensive guide to the current status of IDS‐generated traffic congestion control and diverse approaches to lessen it that can be employed by academicians and researchers.

Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT

The proliferation of the Internet of Things (IoT) in various sectors, including healthcare, smart cities, and industrial automation, has significantly enhanced operational efficiency and service delivery. However, this widespread adoption has introduced new vulnerabilities, making IoT networks a prime target for cyberattacks. Traditional security mechanisms often fall short in protecting IoT devices due to their limited computational resources and the unique nature of IoT network traffic. This paper introduces a novel intrusion detection system (IDS) that leverages network traffic profiling and machine learning techniques tailored for the IoT ecosystem. By analyzing the behavioral patterns of network traffic, the proposed system can accurately identify malicious activities and potential threats in real-time, ensuring the integrity and confidentiality of IoT networks. The methodology encompasses data collection, feature extraction, model training, and evaluation stages, employing a combination of supervised and unsupervised machine learning algorithms to optimize detection accuracy. Experimental results, conducted on real-world IoT network datasets, demonstrate the effectiveness of our approach in detecting a wide range of cyber threats with high precision and recall rates. This research contributes to the cybersecurity domain by providing a scalable, efficient, and adaptive IDS framework that can be integrated into various IoT infrastructures to mitigate the risk of cyber intrusions.

Intrusion Detection System using Blockchain

Abstract: This paper investigates the integration of an Intrusion Detection System (IDS) within the context of blockchain technology. The objective is to enhance the security posture of blockchain networks by detecting and mitigating potential intrusions. Through a meticulous examination of the current threat landscape and the unique challenges posed by blockchain systems, this research proposes a robust IDS framework tailored to the specific requirements of decentralized and distributed ledger environments. The study employs [specific methodology/approach] to assess the effectiveness of the proposed IDS, presenting conclusive findings that contribute to the ongoing discourse on securing blockchain ecosystems. The implications of this research extend to bolstering the resilience of blockchain networks against emerging threat.

Implementing AI-Driven Intrusion Detection System with Python and Light Connect Object

The escalating number of cybersecurity threats poses significant challenges for ensuring the security of networked systems. Intrusion Detection Systems (IDS) play a vital role in detecting and preventing malicious activities. This paper focuses on the implementation of an AI-driven IDS using Python and Light Connect Object (LCO) to enhance the detection capabilities and improve the overall security of networked systems. By integrating AI techniques into the IDS framework, we aim to effectively identify both known and unknown attacks. The proposed system is evaluated using real- world network traffic data, and its performance is measured using metrics such as detection accuracy and false positive rate. The results demonstrate the effectiveness and practicality of the AI-driven IDS in enhancing network security

A coherent salp swarm optimization based deep reinforced neuralnet work algorithm for securing the mobile cloud systems

<p>Protecting the mobile cloud computing system from the cyber-threats is the most crucial and demanding problems in recent days. Due to the rapid growth of internet technology, it is more essential to ensure secure the mobile cloud systems against the network intrusions. In the existing works, various intrusion detection system (IDS) frameworks have been developed for mobile cloud security, which are mainly focusing on utilizing the optimization and classification algorithms for designing the security frameworks. Still, some of the challenges associated to the existing works are complex to understand the system model, educed convergence rate, inability to handle complex datasets, and high time cost. Therefore, this research work motivates to design and develop a computationally efficient IDS framework for improving the mobile cloud systems security. Here, an intrinsic collateral normalization (InCoN) algorithm is implemented at first for generating the quality improved datasets. Consequently, the coherent salp swarm optimization (CSSO) technique is deployed for selecting the most relevant features used for intrusion prediction and categorization. Finally, the deep reinforced neural network (DRNN) mechanism is implemented for accurately detecting the type of intrusion by properly training and testing the optimal features. During validation, the findings of the CSSO-DRNN technique are assessed and verified by utilizing various QoS parameters.</p>

A Hybrid Intrusion Detection Approach for Cyber Attacks

The field of cybersecurity constantly evolves as attackers develop new methods and technologies. Defending against cyberattacks involves a combination of robust security measures, regular updates, user education, and the use of advanced technologies, such as intrusion detection systems and artificial intelligence, to find out the threats in real-time. IDS are designed to identify and address any unauthorized actions or potential security threats within a computer network or system. A hybrid intrusion detection system (IDS) combines many detection techniques and strategies from different IDS types into a single, coherent solution. Combining the benefits of each approach should result in more comprehensive and effective intrusion detection. This paper outlines a proposed anomaly intrusion detection system (AIDS) framework that leverages a hybrid of deep learning strategies. It incorporates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which were developed using XGBoost, and their efficacy was assessed with the NSL-KDD dataset. The evaluation of the suggested model focused on its accuracy, detection capabilities, and the rate of false positives. The outcomes of this research are noteworthy within the cybersecurity field. In this paper, a framework of an Anomaly IDS is proposed. The purpose of an anomaly IDS, or AIDS, is to spot odd behavior on a network or system that might point to a security breach or malevolent attempt to hack it. Anomaly-based IDSs concentrate on finding departures from accepted typical behavior, in contrast to signature-based detection systems, which depend on a predefined database of known attack patterns.

A Signature-Based Wireless Intrusion Detection System Framework for Multi-Channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks

A Signature-Based Wireless Intrusion Detection System Framework for Multi-Channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks

Implementation of a multi-stage intrusion detection systems framework for strengthening security on the internet of things

The Internet of Things (IoT) expansion has introduced a new era of interconnectedness and creativity inside households. Various independent gadgets are now controlled from a distance, enhancing efficiency and organization. This results in increased security risks. Competing vendors rapidly develop and release novel connected devices, often paying attention to security concerns. As a result, there is a growing number of assaults against smart gadgets, posing risks to users' privacy and physical safety. The many technologies used in IoT complicate efforts to provide security measures for smart devices. Most intrusion detection methods created for such platforms rely on monitoring network activities. On multiple platforms, intrusions are challenging to detect accurately and consistently via network traces. This research provides a Multi-Stage Intrusion Detection System (MS-IDS) for intrusion detection that operates on the host level. The study employs personal space and kernel space data and Machine Learning (ML) methods to identify different types of intrusions in electronic devices. The proposed MS-IDS utilizes tracing methods that automatically record device activity, convert this data into numerical arrays to train multiple ML methods, and trigger warnings upon detecting an incursion. The research used several ML methods to enhance the ability to see with little impact on the monitoring devices. The study evaluated the MS-IDS approach in a practical home automation system under genuine security risks.

Machine Learning based Network Packet Classification

Network packet classification plays an important role in modern networks irrespective of host or network-based classification, serving as the foundation for efficient routing, malicious activity detection, and security enforcement. With the continuous growth of network traffic volume and complexity, traditional static rule-based classification methods have faced difficulties in scalability and adaptability. As a solution, the study decided to enforce machine learning techniques to tackle these challenges effectively. This study presents an extensive and original review of machine learning- based approaches for network packet classification. The smart Intrusion Detection System framework with network packet classification evolution looks forward to designing and deploying security systems that use various parameters for analysing current and dynamic traffic trends and are highly time-efficient in predicting intrusions. Various machine learning algorithms commonly employed in packet classification, such as decision trees, support vector machines, and neural networks are analysed and their merits and demerits are compared with their behaviour and accuracy percentage in this study. machine learning-based techniques offer an efficient and accurate network packet classification for the protection of the systems when compared to the conventional methods of packet classification. By leveraging the power of machine learning algorithms and intelligent feature selection, network administrators and Security Operation Center (SOC) analyst can enhance network performance, improve security, and the robustness of the log generated in the network.

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

FL-IIDS: A novel federated learning-based incremental intrusion detection system

With the advantage of analyzing data of multiple work sites comprehensively while ensuring data privacy, federated learning-based intrusion detection systems (IDS) are emerging as a distributed intrusion detection paradigm. Most of these IDS are assumed to work on static data. However, in the actual network environment, the practice of setting data as static will lead to the phenomenon known as catastrophic forgetting, where old classes that have already appeared would be forgotten. In this paper, we propose a novel IDS framework called FL-IIDS to effectively address the catastrophic forgetting problem. Firstly, a new loss function is synthetically designed for local model training. With the new function, the class gradient balance loss function assigns different learning weights to data of the new and old classes so that the learning rate of the new classes would decrease and the memory of the overall old classes would be deepened. Moreover, the sample label smoothing loss function leverages the knowledge distillation method to enhance the local model memory for every specific class of old classes. Secondly, the relay client fusing sample reconstruction is employed to mitigate the spread of catastrophic forgetting globally without compromising data privacy. Extensive experimental results on the UNSW-NB15 dataset and the CICIDS2018 dataset show that our proposed framework improves the memory capability for old classes substantially without affecting the detection effectiveness of the IDS for new classes.