Intrusion Detection System Framework Research Articles

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.

CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET

Vehicular ad hoc networks (VANETs) incorporating vehicles as an active and fast topology are gaining popularity as wireless communication means in intelligent transportation systems (ITSs). The cybersecurity issue in VANETs has drawn attention due to the potential security threats these networks face. An effective cybersecurity measure is essential as security threats impact the overall system, from business disruptions to data corruption, theft, exposure, and unauthorized network access. Intrusion detection systems (IDSs) are popular cybersecurity measures that detect intrusive behavior in a network. Recently, the machine learning (ML)-based IDS has emerged as a new research direction in VANET security. ML-based IDS studies have focused on improving accuracy as a typical classification task without focusing on malicious data. This study proposes a novel IDS for VANETs that offers more attention to classifying attack cases correctly with minimal features required by applying principal component analysis. The proposed Cascaded ML framework recognizes the difference between the attack and normal cases in the first step and classifies the attack data in the second step. The framework emphasizes that an attack should not be classified into the normal class. Finally, the proposed framework is implemented with an artificial neural network, the most popular ML model, and evaluated with the Car Hacking dataset. In addition, the study also investigates the efficiency of typical classification tasks and compares them with results of the proposed framework. Experimental results on the Car Hacking dataset have revealed the proposed method to be an effective IDS and that it outperformed the existing state-of-the-art ML models.

FD‐DBN: Flow directed deep belief network for accurate anomaly detection in cloud computing

SummarySecurity becomes the key concern in a cloud environment, as the servers are distributed throughout the globe and involve the circulation of highly sensitive data. Intrusions in the cloud are common because of the huge network traffic that paves the way for intruders to breach traditional security systems with sophisticated software. To avoid such problems, intrusion detection systems (IDSs) have been introduced by various researchers. Each IDS was developed to achieve a particular objective, that is, providing security by detecting intrusions. Most of the available IDS are inefficient and are unable to provide accurate classification. Also, some of them are computationally expensive to be implemented in practical scenarios. This article proposes a new and efficient IDS framework that can accurately classify the intrusion type through effective training to address the existing drawbacks. The proposed framework, named flow directed deep belief network (FD‐DBN), involves three main phases: pre‐processing, clustering, and classification. In pre‐processing, certain data mining operations are carried out to clean the data. The clustering phase is carried out using the game‐based k‐means (GBKM) clustering algorithm. The clustered data is then provided as input to the FD‐DBN classification framework, where the training process is carried out. The deep belief network (DBN) training is performed with dataset features, and the flow direction algorithm is adopted for tuning the weight parameters of DBN. Through tuning, the model yielded accurate classification outcomes. The simulations are done in Python 3.6, and the results proved that the proposed framework is much more effective than the existing IDS frameworks.

P‐DNN: Parallel DNN based IDS framework for the detection of IoT vulnerabilities

SummaryThe rapid growth of the Internet of Things (IoT) in our daily life has recently received attention from hackers in releasing novel attacks. This is because the existing traditional Intrusion Detection System (IDS) uses an alert‐based approach that cannot detect new emerging attacks, making it unfeasible for devices with limited resources. The study presents the deployment of a Parallel Deep Neural Network (P‐DNN) IDS framework to improve the detection rate and resource constraint issues. To validate the framework for detecting the latest IoT vulnerabilities, we generated a proprietary dataset created under the IoT environment. The experimental results reveal that the P‐DNN IDS framework proposed in this study outperformed the K‐nearest Neighbour (KNN), Support Vector Machine (SVM), and Naive Bayes algorithms with respect to performance metrics. In addition, a comparative analysis of the P‐DNN framework with Snort and Suricata IDS results demonstrates a significant reduction in CPU consumption, memory utilization, and processing time. The results of this investigation indicate that the deployment of the P‐DNN framework has the capability to detect vulnerabilities in resource‐constraint IoT environments effectively.

An adaptable deep learning-based intrusion detection system to zero-day attacks

The main challenge of an intrusion detection system (IDS) is detecting novelties (i.e., zero-day attacks) in addition to generating a report about the known attacks (i.e., classifying known attacks). Another challenge of an IDS is the adaptation to the detected novelties. For this purpose, it needs sufficient labeled samples of the new attacks. On the other hand, the labeling procedure is a time-consuming task for the security expert teams.This paper proposes a DL-based IDS framework adaptable to new attacks, consisting of different phases. The first phase uses deep learning-based open set recognition methods to identify unknown samples (i.e., new attacks), and make a report from different known attacks simultaneously. Then, the novel samples are clustered by combining the deep model and clustering algorithms. These clusters are the main key to making the labeling procedure more practical and reducing the time and effort of the expert knowledge team. Finally, we use the labeled groups to update the model and make it adaptable to new traffic behaviors.To evaluate the proposed framework, we compare our proposed DOC++ alongside different algorithms (including DOC, OpenMax, AutoSVM, and CROSR) in the open set recognition phase of the framework and use both the CIC-IDS2017 and CSE-CIC-IDS2018 data sets for the evaluation. Our results show that DOC++ achieves the best performance among the other open set recognition approaches. Besides, the completeness and homogeneity of the clustering phase prove that the generated labeled groups are good enough for the supervised labeling and updating phases.

Deep-discovery: Anomaly discovery in software-defined networks using artificial neural networks

This paper proposes Deep-Discovery, an Intrusion Detection System (IDS), to perform Anomaly Discovery in Software-Defined Networking (SDN) using Artificial Neural Network (ANN). The proposed IDS framework utilizes the Multi-Layer Perceptron (MLP), a Feedforward (FF) ANN, to detect volume-based and protocol-based Distributed Denial of Service (DDoS) attacks on the data plane of SDN. The proposed model considers the attack detection a multi-class classification problem and classifies the network traffic into six attack classes with an accuracy of 98.81% and a minimal False Alarm Rate (FAR) of 0.002. The proposed classification model addresses the binary classification problem to compare and analyze the classification performance metrics. The Deep-Discovery that deals with the binary classification problem categorizes the network traffic into anomalous and normal traffic with 99.79% accuracy and a nominal FAR of 0.0001. The novelty of this work is its emphasis on obtaining the optimal performance metrics with a simple neural network with minimal computational overhead rather than an intricate and complex model.

PIGNUS: A Deep Learning model for IDS in industrial internet-of-things

The heterogeneous nature of the Industrial Internet of Thing (IIoT) has a considerable impact on the development of an effective Intrusion Detection System (IDS). The proliferation of linked devices results in multiple inputs from industrial sensors. IDS faces challenges in analyzing the features of the traffic and identifying anonymous behavior. Due to the unavailability of a comprehensive feature mapping method, the present IDS solutions are non-usable to identify zero-day vulnerabilities.In this paper, we introduce the first comprehensive IDS framework that combines an efficient feature-mapping technique and cascading model to solve the above-mentioned problems. We call our proposed solution deeP learnIG model intrusioN detection in indUStrial internet-of things (PIGNUS). PIGNUS integrates Auto Encoders (AE) to select optimal features and Cascade Forward Back Propagation Neural Network (CFBPNN) for classification and attack detection. The cascading model uses interconnected links from the initial layer to the output layer and determines the normal and abnormal behavior patterns and produces a perfect classification. We execute a set of experiments on five popular IIoT datasets: gas pipeline, water storage tank, NSLKDD+, UNSW-NB15, and X-IIoTID. We compare PIGNUS to the state-of-the-art models in terms of accuracy, False Positive Ratio (FPR), precision, and recall. The results show that PIGNUS provides more than 95% accuracy, which is 25% better on average than the existing models. In the other parameters, PIGNUS shows 20% improved FPR, 10% better recall, and 10% better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs.

A Novel Multi-Attack IDS Framework for Intelligent Connected Terminals Based on Over-the-Air Signature Updates

Modern terminals are developing toward intelligence and ubiquitous connection. Such ICTs (intelligent connected terminals) interact more frequently with the outside world and expose new attack surfaces. IDSs (intrusion detection systems) play a vital role in protecting ICT security. Multi-attack IDSs that can cover both intra-terminal and inter-terminal networks are a promising research direction for improving detection accuracy and the strength of security protection. However, a major challenge is the frequent dynamic signature updates across the network boundary, which cause significant computational overheads and result in losses in detection performance. In light of this, we propose a novel IDS framework based on OTA (over-the-air) signature updates to implement multi-attack detection. It updates the attack signatures of the target ICTs and adds the new attack signatures to the signature database in order to minimize the local memory storage and computing resources. It employs a CNN (convolutional neural network) based on an auto-encoder to achieve multi-attack detection, which can ensure the detection accuracy of multi-attacks with the multiple classification function. We evaluated our framework on four types of real-world ICT attack data, drawing comparisons with four widely used IDS schemes, and demonstrated the non-negligible superiority of our scheme over all benchmarks in terms of accuracy, recall, precision, and F1-score. Our work represents an important step toward an IDS that can detect multi-attacks in both intra-terminal and inter-terminal networks.