Efficient Intrusion Detection Research Articles

An efficient intrusion detection technique for traffic pattern learning

Efficient intrusion detection algorithms are required for network traffic learning patterns in order to protect advanced network communication channels. These systems can be used to detect normal and unusual patterns, signatures, and rule violations. In recent years, conventional and deep machine learning algorithms have been utilized in the field of network intrusion detection for network traffic learning systems. The use of machine learning opens up new attack surfaces that are very intriguing to investigate. Attackers can introduce noisy data into training data to influence testing patterns in computer networks. The goal of this work is to create an efficient intrusion detection solution for network traffic learning patterns using a supervised and unsupervised technique. We developed an effective intrusion detection system (IDs) using an appropriate NSLKDD dataset for network traffic patterns. The model was trained and evaluated using the Genetic Optimization Algorithm (GOA) and the Niave Bayesian technique to recognize usual and unexpected network traffic patterns. We created a strategy that begins with a random population and subsequent iterates through the fitness function, returning the best parents with high detection accuracy. The best parents were determined using the n-parameters iterated by the crossover and mutation procedures. A cross over function was created to combine genes from two fitness parents by randomly selecting portions from each parent. The individual components of the crossover offsprings are randomly flipped to achieve the mutation. The fitness of the previous generation was obtained to generate a new generation, and this process was repeated n times. This was created to detect network intrusions using Nave Bayes' binary categorization problem and evolutionary algorithms. We accomplished this task by aggregating noise into training set before broadcasting the average number, and it is critical not to have that public average too frequently. The experimental results reveal that our proposed GA fared better than the NB technique, with a detection accuracy of 95.0% versus a recommendable detection accuracy of 53.0%.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

An efficient intrusion detection model based on convolutional spiking neural network

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.

A hybrid deep learning approach for enhanced network intrusion detection

The contemporary era places paramount importance on network security and cloud environments, driven by increased data transmission demands, the flexibility of cloud services, and the prevalence of global resources. Addressing the escalating threat of computer malware, the development of efficient intrusion detection systems (IDS) is imperative. This research focuses on the challenges posed by imbalanced datasets and the necessity for unsupervised learning to enhance network security. The proposed hybrid deep learning method utilizes raw data from the CSE-CIC-IDS-2018 dataset, integrating imbalanced and unsupervised learning techniques. After preprocessing and normalization, feature extraction through principal component analysis (PCA) reduces dimensionality from seventy-eight fields to ten essential features. Clustering, employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm optimized with particle swarm optimization (PSO), is applied to the extracted features, distinguishing between attack and non-attack packets. Addressing dataset imbalances, imbalanced learning techniques are employed, and unsupervised learning is exemplified through the AutoEncoder (AE) algorithm. The attack cluster’s data is input into AE, a deep learning-based approach, yielding outputs for attack classification. The proposed technique (PCA+DBSCANPSO+AE) achieves an impressive 99.19% accuracy in intrusion detection, surpassing contemporary methodologies and five existing techniques. This research not only enhances accuracy but also addresses imbalanced learning challenges, utilizing the power of unsupervised learning for robust network security.

A novel anomaly detection model for the industrial Internet of Things using machine learning techniques

In recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance on automation, and limited computational resources at the edge, have reshaped global business dynamics, aiming to innovate business models through enhanced automation technologies. However, ensuring security in these environments remains a critical challenge, with real-time data streams introducing vulnerabilities to zero-day attacks and limited resources at the edge demanding efficient intrusion detection solutions. This study addresses this pressing need by proposing a novel intrusion detection model (IDS) specifically designed for Industry 4.0 environments. The proposed IDS leverages a Random Forest classifier with Principal Component Analysis (PCA) for feature selection. This approach addresses the challenges of real-time data processing and resource limitations while offering high accuracy. Based on the Bot-IoT dataset, the model achieves a competitive accuracy of 98.9% and a detection rate of 97.8%, outperforming conventional methods. This study demonstrates the effectiveness of the proposed IDS for securing Industry 4.0 ecosystems, offering valuable contributions to the field of cybersecurity.

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

An efficient intrusion detection model based on neutrosophic logic for optimal response from the arranged response set

While an Automated Intrusion Response System (AIRS) chooses and initiates a suitable reaction from the pool of response groups based on specific response choice requirements to reduce the intrusion immediately, an Intrusion Detection System (IDS) finds the intrusions and generates alerts. The accurate assessment of the critical weight of all responses chosen and the prioritization of the incursion response set are the biggest hurdles when creating an AIRS. This study suggested a multi-criteria decision-making (MCDM) method for ranking intrusion responses. The TOPSIS method is an MCDM method used to rank the alternatives. The TOPSIS method integrated with the single-valued neutrosophic set (SVNS) to overcome uncertainty. This study used 16 criteria and 10 alternatives to be evaluated by experts and decision-makers. The sensitivity analysis shows the rank of other options under different cases. The criteria weights are changed under 17 cases. The results of sensitivity analysis show the rank of alternatives is stable. The suggested method was compared with other MCDM methods to show its effectiveness and robustness.

An Efficient Intrusion Detection System in Cloud Network Based on Deep Learning and Improved Marine Predators-Particle Swarm Optimization

An Efficient Intrusion Detection System in Cloud Network Based on Deep Learning and Improved Marine Predators-Particle Swarm Optimization

F-TLBO-ID: Fuzzy fed teaching learning based optimisation algorithm to predict the number of [formula omitted]-barriers for intrusion detection

Ensuring fast and efficient Intrusion Detection and Prevention (IDP) at international borders is crucial for maintaining security and safeguarding nations. In this study, we propose an innovative approach that harnesses the power of machine learning and Wireless Sensor Networks (WSNs) to achieve faster and more accurate IDP. Our novel Fuzzy fed Teaching Learning Based Optimisation regression algorithm (F-TLBO-ID) revolutionises the prediction of the required number of k-barriers for rapid IDP. To develop and validate our approach, we synthetically generated pertinent features using Monte-Carlo simulations. These features encompass essential parameters such as the concerned region’s area, effective transmission range, effective sensing range, number of sensor nodes, and the fading parameter. Training the F-TLBO-ID algorithm with these features yielded exceptional results, accurately predicting the required number of k-barriers with an impressive correlation coefficient (R = 0.99), minimal Root Mean Square Error (RMSE = 11.32), and negligible bias (−3.66). To benchmark the performance of our F-TLBO-ID algorithm, we conducted comprehensive comparisons with fine-tuned benchmark algorithms, including AutoML, GPR, GRNN, RF, RNN, SVM, and ANN. Additionally, we evaluated the algorithm against 11 different variants of nature-inspired algorithms. Remarkably, our F-TLBO-ID algorithm outperformed all these methods in terms of accuracy, firmly establishing its superiority. Finally, we validated the performance of the F-TLBO-ID algorithm using publicly available datasets. The results were highly satisfactory, exhibiting a strong correlation coefficient (R = 0.84), acceptable RMSE (36.24), and minimal bias (−7.17). This study offers a robust and reliable algorithm to predict the required barriers for fast IDP, surpassing the accuracy of existing benchmark algorithms. By implementing our proposed algorithm, the efficiency of IDP systems at international borders can be significantly improved, ultimately enhancing security and facilitating smooth border operations.

An Efficient Intrusion Detection and Prevention System for DDOS Attack in WSN Using SS-LSACNN and TCSLR

Sensor Nodes (SNs) are utilized by Wireless Sensor Networks (WSNs) to recognize their environment; in addition, the WSN delivers data from sensing nodes to the sink. The WSNs are exposed to several security threats owing to the broadcast performance of transmission along with the increase in the growth of application regions. Countermeasures like Intrusion Detection and Prevention Systems (IDPS) should be adopted to overcome the aforementioned attacks. By implementing these systems, several intrusions can be detected in WSN; also, WSN can be prevented from various security attacks. Therefore, identifying the general attack that influences the SNs mentioned as Distributed Denial of Service (DDoS) attack and recuperating the data utilizing Soft Swish (SS)-Linear Scaling-centered Adam Convolution Neural Network (SS-LSACNN) along with Two’s Compliment Shift Reverse (TCSLR) operation are the intentions of this work. Firstly, for extracting the vital features, the data gathered as of the dataset are utilized. After that, the extracted features are pre-processed. It is then utilized for attack detection. The null features and the redundant data are removed in preprocessing. By employing the Correlation Coefficient-centered Synthetic Minority Oversampling Technique (CC-SMOTE) methodology, data separation regarding classes and data balancing was performed to prevent the imbalance issue. Subsequently, to provide the preprocessed data for attack detection, the Numeralization and feature scaling are executed. After that, by utilizing Chebyshev Distance (CD)-centric K-Means Algorithm (KMA), the real-time SNs are initialized as well as clustered. The data gathered as of the SNs are utilized for attack detection following the clustering phase. Following the detection phase, the data being attacked are amassed in the log file; similarly, the non-attacked data are inputted into the prevention phase. Next, the experiential analysis is carried out for examining the proposed system’s efficacy. The outcomes revealed that the proposed model exhibits 98.15% accuracy, 97.59% sensitivity, 95.72% specificity, and 95.48% F-measure, which displays the proposed model’s efficacy.

Towards Effective Intrusion Detection in OpenFlow-Based SDN Architectures

This research article develops and assesses a thorough intrusion detection system (IDS) to investigate improvements in security inside Software-Defined Networking (SDN) environments. With an emphasis on packet-level analysis for the detection and mitigation of possible network intrusions, the study explores the integration of IDS features into SDN controllers. The suggested IDS is simulated and empirically assessed in a variety of network circumstances, such as traffic fluctuations, delay changes, and increased iperf scenarios, using a Mininet-based framework. The research advances our understanding of efficient intrusion detection techniques inside the SDN paradigm by providing insights into security issues and possible solutions for upcoming SDN deployments.

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder

The increasing prevalence of unknown-type attacks on the Internet highlights the importance of developing efficient intrusion detection systems. While machine learning-based techniques can detect unknown types of attacks, the need for innovative approaches becomes evident, as traditional methods may not be sufficient. In this research, we propose a deep learning-based solution called the log-cosh variational autoencoder (LVAE) to address this challenge. The LVAE inherits the strong modeling abilities of the variational autoencoder (VAE), enabling it to understand complex data distributions and generate reconstructed data. To better simulate discrete features of real attacks and generate unknown types of attacks, we introduce an effective reconstruction loss term utilizing the logarithmic hyperbolic cosine (log-cosh) function in the LVAE. Compared to conventional VAEs, the LVAE shows promising potential in generating data that closely resemble unknown attacks, which is a critical capability for improving the detection rate of unknown attacks. In order to classify the generated unknown data, we employed eight feature extraction and classification techniques. Numerous experiments were conducted using the latest CICIDS2017 dataset, training with varying amounts of real and unknown-type attacks. Our optimal experimental results surpassed several state-of-the-art techniques, achieving accuracy and average F1 scores of 99.89% and 99.83%, respectively. The suggested LVAE strategy also demonstrated outstanding performance in generating unknown attack data. Overall, our work establishes a solid foundation for accurately and efficiently identifying unknown types of attacks, contributing to the advancement of intrusion detection techniques.

Federated Continuous Learning Based on Stacked Broad Learning System Assisted by Digital Twin Networks: An Incremental Learning Approach for Intrusion Detection in UAV Networks

The edge of the Internet of Things (IoT), which consists of Unmanned Aerial Vehicles (UAVs), is vulnerable to network intrusion because software and wireless connections are used extensively in the IoT. Designing an efficient Intrusion Detection System (IDS) model is imperative. However, when creating IDS models with distributed data collected by UAVs, it is necessary to take precautions to protect the data’s security and privacy. Furthermore, most of the IDS models are focused on one-time learning but not on continuous learning. To this end, we propose a Federated Continuous learning framework with a Stacked Broad Learning System (FCL-SBLS) based on Digital Twin Network (DTN), which can learn and train the IDS model on new data quickly and continuously. In order to improve the efficiency and quality of the IDS model when training and aggregation, we employ an asynchronous federated learning architecture, and a Deep Deterministic Policy Gradient (DDPG)-based UAV selection scheme assisted by DTN is proposed to help the global IDS model aggregation. The presented algorithm is validated using the CIC-IDS2017 dataset, and the simulation results reveal that our algorithm achieves higher efficiency and accuracy than the existing FL scheme.

An Effective and Efficient Intrusion Detection System of Network Attacks Using Stacked CNN and Voting Technique

IDS are crucial to network security because they can identify malicious activity and halt it in its tracks. Network intrusion data is often masked by a sea of benign data, making it difficult to train a model or perform a detection with a high FPR. This is because networks are inherently dynamic and change over time. In this research, we offer a ML & DL model-based method to ID, and we demonstrate how to deal with the issue of data imbalance by using a hybrid sampling technique. Conventional firewalls and data encryption technologies are unable to provide the level of security required by current networks. As a result, IDSs have been endorsed for use against network threats. Recent mainstream ID approaches have benefited from ML, but they have low detection rates & need a lot of feature engineering to be truly useful. Using layered CNN and Voting classifier (XGBoost and LGBM), this study introduces ML-DL-NIDS to address the issue of subpar detection precision. Using a publicly available NSL-KDD & UNSW-15 benchmark datasets for network intrusion detection, we find that this model outperforms competing methods according to accuracy and F1-score obtained from experimental evaluations.

A Predictive Model Using Deep Learning Neural Network for Efficient Intrusion Detection

Abstract: Network intrusion detection system helps to detect exploitations and mitigate damages. A network intrusion detection system detects the network traffic that deviates from the normal behavioral pattern. Developing an efficient intrusion detection system has many challenges and the patterns associated with one type of intrusion differ from other intrusions. In such situations, understanding different patterns and differentiating intrusions becomes essential to detect anomalies and attacks in the network. Deep learning models offer more power and intelligence to the detection system and extend the ability to differentiate & understand the network feature characteristics, also machine learning models with feature selection showed high performance in intrusion detections. This paper evaluates the proposed deep learning neural network model and machine learning models using feature selection for efficient intrusion detection using real world dataset.

An efficient intrusion detection framework for mitigating blackhole and sinkhole attacks in healthcare wireless sensor networks

A Wireless Sensor Network (WSN) is made up of physically dispersed autonomous sensors which monitor the network and gather data about its surroundings. A sensor effectively captures information securely with the goal to detect potentially dangerous user conduct in the network. However, identifying an attack from ordinary nodes of sensors remains a difficult task. As a result, the suggested efforts concentrate on building effective attack discovery methods for secured data packet broadcasting from source to destination in the WSN. Proportional Overlapping Score-Based Minkowski K-Means Clustering (POS-MKC) is proposed to improve attack detection accuracy with lesser computational complexity in WSN healthcare applications. The simulation results illustrate that the proposed MK-Means framework widely generates an optimized performance with the reduction of delay, computational complexity and improvements in packet delivery ratio with high attack detection accuracy as compared to the state-of-the-art works.

Mitigation of network security attacks in wireless multimedia sensors networks using intrusion detection system

<p>In this work, research propose a deep transfer learning-based Lecun network P-Lenet technique to move the knowledge contained in source domain data to the target domain and integrate to produce an efficient intrusion detection system (IDS) that would enhance the detection accuracy for any wireless multimedia sensors networks (WMSN) ecosystems. To prevent attacks on the software defined network (SDN) platform in real time, the proposed method placed a strong emphasis on anomaly detection as the primary mechanism. The Lecun network (LeNet) was investigated in this work, and a new variant of the network called the Lenet was proposed. In addition, research make use of techniques like as feature normalization to increase the accuracy of the algorithm predictions and to optimize the training process in such a way that it consumes the least amount of time and resources. The performance of the proposed model that was recommended was superior to that of existing network intrusion detection system (NIDS) algorithms.</p>

Enhancing IoT network security through deep learning-powered Intrusion Detection System

The rapid growth of the Internet of Things (IoT) has brought about a global concern for the security of interconnected devices and networks. This necessitates the use of efficient Intrusion Detection System (IDS) to mitigate cyber threats. Deep learning (DL) techniques provides a promising approach to effectively detect irregularities in network traffic, enhancing IoT network security and reducing cyber threats. In this paper, DL-based IDS is proposed using Feed Forward Neural Networks (FFNN), Long Short-Term Memory (LSTM), and Random Neural Networks (RandNN) to protect IoT networks from cyberattacks. Each DL model has its potential benefit as reported in this paper. For example, the FFNN can handle complex IoT network traffic patterns, while the LSTM is good in capturing long-term dependencies present in the network traffic. With its random connections and flexible dynamics, the RandNN model uses its data learning ability to adapt and learn from network data. These algorithms boost cybersecurity by enabling defense mechanisms against challenging cyber threats and ensuring the security of sensitive data as IoT networks expand. The proposed technique exhibits superior performance when compared with the current state-of-the-art DL-IDS using the CIC-IoT22 dataset. An accuracy of 99.93 % is achieved for the FFNN model, 99.85 % for the LSTM model, and 96.42 % for the RandNN model in detecting intrusion. Moreover, the models have the potential to enhance intrusion detection in IoT networks by generating swift responses to security problems in IoT networks.

Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks.

The Smart Grid aims to enhance the electric grid's reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid's communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%.

Particle swarm optimization based artificial neural network (PSO-ANN) model for effective k-barrier count intrusion detection system in WSN

Wireless Sensor Networks (WSN) offers an extensive array of possibilities and finds application to nearly every wake of human lifetime. Two of the methods are intrusion detection and monitoring in dangerous environments. Generally, WSN are extremely vulnerable to various kinds of network attacks, lifespan of whole network is reduced by interfering with data transmission and communication operations. A novel approach, provides accurate measurement of several obstacles to achieve rapid identification and avoidance of disruptions. In order to more accurately estimate k-barrier number for efficient intrusion detection and prevention, a Particle Swarm Optimization based Artificial Neural Network (PSO-ANN) is developed. The region's size, sensor's detecting field, area where sensor transmits its signals, several sensors constitute the four important features utilized for learning and evaluating PSO-ANN model. The extraction of the different attributes is achieved with the help of simulation through Monte Carlo. It has been inferred from the research that the approach is accurate in predicting number of invasions, correlation coefficient, root mean square error, accuracy, precision are achieved to be excellent in contrast to the remaining approaches.