Effective Intrusion Detection Research Articles

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.

Multi-shift spatio-temporal features assisted deep neural network for detecting the intrusion of wild animals using surveillance cameras

The coexistence of human populations and wildlife in shared habitats necessitates the development of effective intrusion detection systems to mitigate potential conflicts and promote harmonious relationships. Detecting the intrusion of wild animals, especially in areas where human-wildlife conflicts are common, is essential for both human and animal safety. Animal intrusion has become a serious threat to crop yield, impacting food security and reducing farmer profits. Rural residents and forestry workers are increasingly concerned about the issue of animal assaults. Drones and surveillance cam-eras are frequently used to monitor the movements of wild animals. To identify the type of animal, track its movement, and provide its position, an effective model is needed. This paper presents a novel methodology for detecting the intrusion of wild animals using deep neural networks with multishift spatio-temporal features from surveillance camera video images. The pro-posed method consists of a multi-shift attention convolutional neural net-work model to extract spatial features, a multi-moment gated recurrent unit attention model to extract temporal features, and a feature fusion network to fully explore the spatial semantics and temporal features of surveillance video images. The proposed model was tested with images from three different datasets and achieved promising results in terms of mean accuracy and precision.

Designing an Adaptive Effective Intrusion Detection System for Smart Home IoT

Designing an Adaptive Effective Intrusion Detection System for Smart Home IoT

Intrusion Detection in IoT Networks Using Machine Learning Techniques

The advent of the IoT has undeniably transformed the manner in which we connect with the world innovative solutions across a multitude of sectors. However, the proliferation of IoT applications has brought to the fore a complex and multifaceted problem that warrants immediate attention: the security and energy efficiency challenges within IoT networks. As the number of IoT devices continues to surge and their applications diversify, the demand for ubiquitous and secure data access has never been greater. These applications, ranging from healthcare monitoring to industrial automation, require not only real-time data processing but also the assurance of data security and privacy. So, the research explores the machine learning techniques application for intrusion detection in IoT networks, aiming to enhance security in the rapidly evolving landscape of interconnected devices. The research emphasizes the significance of proactive measures in safeguarding IoT ecosystems and highlights the potential of machine learning to detect and mitigate intrusions effectively. In this research, a model was developed for detecting anomalies in Internet of Things networks using artificial neural networks. The research aims to develop and evaluate an effective intrusion detection model for IoT networks by leveraging machine learning techniques. The purpose is to enhance the security posture of interconnected devices, addressing the unique challenges posed by the dynamic and heterogeneous nature of IoT environments. The study tries to contribute important experiences and functional answers to reinforce the strength of IoT networks against developing digital dangers.

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Analyzing Autoencoder-Based Intrusion Detection System Performance

The rise in cyberattacks targeting critical network infrastructure has spurred an increased emphasis on the development of robust cybersecurity measures. In this context, there is a growing exploration of effective Intrusion Detection Systems (IDS) that leverage Machine Learning (ML) and Deep Learning (DL), with a particular emphasis on autoencoders. Recognizing the pressing need to mitigate cyber threats, our study underscores the crucial importance of advancing these methodologies. Our study aims to identify the optimal architecture for an Intrusion Detection System (IDS) based on autoencoders, with a specific focus on configuring the number of hidden layers. To achieve this objective, we designed four distinct sub-models, each featuring a different number of hidden layers: Test 1 (one hidden layer), Test 2 (two hidden layers), Test 3 (three hidden layers), and Test 4 (four hidden layers).We subjected our models to rigorous training and testing, maintaining consistent neuron counts of 30 and 60. The outcomes of our experimental study reveal that the model with a single hidden layer consistently outperformed its counterparts, achieving an accuracy of 95.11% for NSL-KDD and an impressive 98.6% for CIC-IDS2017. The findings of our study indicate that our proposed system is viable for implementation on critical network infrastructure as a proactive measure against cyber-attacks.

Cost based Random Forest Classifier for Intrusion Detection System in Internet of Things

Internet of Things (IoT) is the collection of physical and digital devices that are interconnected using Internet for exchange of information and delivery of services. The Internet of Things (IoT) is an extended application of Internet that is used to offer various services for users in the fields of agriculture, healthcare, education, smart homes and so on in the modern world. The significant issues of the intrusion present in IoT are network disconnection, network hacking and data theft from the source. So the challenging task for worldwide utilization of IoT is to address their security issues, because of the feature imbalance in the different types of attacks. The most essential task for addressing security issues is to predict and classify the intrusion in the network. In this paper, the Cost based Random Forest Classifier (CRFC) is proposed for developing an effective Intrusion Detection System (IDS). The CRFC based classification is improvised by incorporating the cost matrix calculated based on feature importance that helps to improve the process of splitting the features even if there is a feature imbalance. Further, three important libraries of Python namely, Spark, Kafka, and Scikit-learn are used in this IDS to improve the classification performances. In that, Spark is used to implement the distributed environment, Kafka is used for streaming the data and Scikit is used to implement CRFC. There are two datasets known as NSL-KDD and UNSW-NB15 that are used to evaluate the performance of the proposed CRFC-IDS method. The CRFC-IDS method is analyzed on the basis of accuracy, precision, recall, F1-Measure, Area Under the Curve (AUC), False Acceptance Rate (FAR) and Matthews Correlation Coefficient (MCC). The existing approaches OCSVM and DBF are used for comparison with the CRFC-IDS method. The accuracy of CRFC-IDS for NSL-KDD dataset is found to be 99.957%, which is highest when compared to OCSVM and DBF.

ILIDViz: An Incremental Learning-Based Visual Analysis System for Network Anomaly Detection

BackgroundWith the development of information technology, network traffic logs mixed with various kinds of cyber-attacks have grown explosively. Traditional intrusion detection systems (IDS) have limited ability to discover new inconstant patterns and identify malicious traffic traces in real-time. It is urgent to implement more effective intrusion detection technologies to protect computer security. MethodsIn this paper, we design a hybrid IDS, combining our incremental learning model (KAN-SOINN) and active learning, to learn new log patterns and detect various network anomalies in real-time. Results & ConclusionsThe experimental results on the NSLKDD dataset show that the KAN-SOINN can be improved continuously and detect malicious logs more effectively. Meanwhile, the comparative experiments prove that using a hybrid query strategy in active learning can improve the model learning efficiency.

Improved Feature Selection Algorithm for Intrusion Detection Using Data Mining Approach

With the rapid growth of Internet applications, there are more and more intrusions into network systems. In this case, it is necessary to provide security for the network through effective intrusion detection and prevention methods. This can mainly be achieved by creating effective interruption detection systems using efficient algorithms that can identify abnormal activities in network traffic and safeguard network resources from unlawful attack by interlopers. Although many interruption recognition frameworks have been proposed before, existing network intrusion detection has limitations in terms of accuracy and detection time. To overcome these shortcomings, In this paper, we propose a new intrusion detection system by developing a new intelligent feature selection algorithm based on conditional random fields (CRF) to optimize the number of features. Furthermore, algorithms based on existing hierarchical methods (LA) In this paper, we propose another interrupt recognition framework, fostering a book. Compared with the existing methods, the interruption identification framework provides high precision and achieves the efficiency of attack detection. The main advantages of this system are reduced detection time, improved classification accuracy and lower false positive rate.

Performance Comparison Analysis of Classification Methodologies for Effective Detection of Intrusions

Intrusion detection systems (IDS) are critical in many applications, including cloud environments. The intrusion poses a security threat and extracts privacy data and information from the cloud. The user has an Internet function that allows him to store personal information in the cloud environment. The cloud can be affected by various issues such as data loss, data breaches, lower security and lack of privacy due to some intruders. A single intrusion incident can result in data within computer and network systems being quickly stolen or deleted. Additionally, intrusions can cause damage to system hardware, resulting in significant financial losses and exposing critical IT infrastructure to risk. To overcome these issues, the study employs the performance comparison analysis of Autoencoder Convolutional neural network (AE+CNN), Random K-means clustering assisted deep neural network (RF+K-means+DNN), Autoencoder K-means clustering assisted long short term memory (AE+K-means+LSTM), Alexnet+Bi-GRU, AE+Alexnet+Bi-GRU and Wild horse AlexNet assisted Bi-directional Gated Recurrent Unit (WABi-GRU) models to choose the best methodology for effective detection of intrusions. The data needed for the analysis is collected from CICIDS2018, UNSW-NB15 and NSL-KDD datasets. The collected data are pre-processed using data normalization and data cleaning. Finally, through this research, the best model suitable for effective intrusion detection can be identified and used for further processes. The proposed models, such as RF+K-means+DNN, AE+K-Means+LSTM, AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU can obtain an accuracy of 99.278%, 99.33%, 99.45%, 99.50%, 99.65% for the CICIDS dataset 2018 for binary classification. In multi-class classification, the AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU can attain accuracy of 99.819%, 99.852% and 99.890%. In NSL-KDD, the AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU achieve accuracy of 99.34%, 99.546% and 99.7%. In UNSW-NB 15 dataset, AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU achieve accuracy of 99.313%, 99.399% and 99.53%. AlexNet Bi-GRU-based models can obtain better performances than other existing models.

Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection

Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users’ privacy, and maintaining critical systems’ reliability and safety. As the IoT landscape evolves, effective intrusion detection mechanisms become increasingly essential to mitigate the ever-growing spectrum of cyber threats. Practical security approaches, notably deep learning-based intrusion detection, have been introduced to tackle these issues. This study utilizes deep learning models, including convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), while introducing an ensemble deep learning architectural framework that integrates a voting policy within the model’s structure, thereby facilitating the computation and learning of hierarchical patterns. In our analysis, we compared the performance of ensemble deep learning classifiers with traditional deep learning techniques. The standout models were CNN-LSTM and CNN-GRU, achieving impressive accuracies of 99.7% and 99.6%, along with exceptional F1-scores of 0.998 and 0.997, respectively.

Network Intrusion Detection Using Autoencode Neural Network

In today's interconnected digital landscape, safeguarding computer networks against unauthorized access and cyber threats is of paramount importance. NIDS play a crucial role in identifying and mitigating potential security breaches. This research paper explores the application of autoencoder neural networks, a subset of deep learning techniques, in the realm of Network Intrusion Detection.Autoencoder neural networks are known for their ability to learn and represent data in a compressed, low-dimensional form. This study investigates their potential in modeling network traffic patterns and identifying anomalous activities. By training autoencoder networks on both normal and malicious network traffic data, we aim to create effective intrusion detection models that can distinguish between benign and malicious network behavior.The paper provides an in-depth analysis of the architecture and training methodologies of autoencoder neural networks for intrusion detection. It also explores various data preprocessing techniques and feature engineering approaches to enhance the model's performance. Additionally, the research evaluates the robustness and scalability of autoencoder-based NIDS in real-world network environments. Furthermore, ethical considerations in network intrusion detection, including privacy concerns and false positive rates, are discussed. It addresses the need for a balanced approach that ensures network security while respecting user privacy and minimizing disruptions. operation. This approach compresses the majority samples & increases the minority sample count in tough samples so that the IDS can achieve greater classification accuracy.

IoT Intrusion Detection System Based on Machine Learning

With the rapid development of the Internet of Things (IoT), the number of IoT devices is increasing dramatically, making it increasingly important to identify intrusions on these devices. Researchers are using machine learning techniques to design effective intrusion detection systems. In this study, we propose a novel intrusion detection system that efficiently detects network anomalous traffic. To reduce the feature dimensions of the data, we employ the binary grey wolf optimizer (BGWO) heuristic algorithm and recursive feature elimination (RFE) to select the most relevant feature subset for the target variable. The synthetic minority oversampling technique (SMOTE) is used to oversample the minority class and mitigate the impact of data imbalance on the classification results. The preprocessed data are then classified using XGBoost, and the hyperparameters of the model are optimized using Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to achieve the highest detection performance. To validate the effectiveness of the proposed method, we conduct binary and multiclass experiments on five commonly used IoT datasets. The results show that our proposed method outperforms state-of-the-art methods in four out of the five datasets. It is noteworthy that our proposed method achieves perfect accuracy, precision, recall, and an F1 score of 1.0 on the BoT-Iot and WUSTL-IIOT-2021 datasets, further validating the effectiveness of our approach.

Intrusion detection system for detecting distributed denial of service attacks using machine learning algorithms

<span>Today, the creation of more effective intrusion detection system (IDS) has become crucial due to the rise in computer malware. Ensuring the availability of the system is an important component of information security and the most important requirement of any network. Recently the machine learning algorithm (ML) has been used to improve intrusion detection over the network. It is currently necessary to release an updated version of these systems. The presented work aimed to build a reliable and accurate IDS based on ML to classify and prevent distributed denial of service attacks to protect any system working on the network from temporary or complete system failure. We presented five ML models to create the proposed distributed denial-of-services attack (DDoS)-IDS, including (decision tree, random forest, logistic regression, support vector machine, and multi-layer neural network) which were trained and evaluated using the CIC-IDS-2018 dataset. Furthermore, principal component analysis (PCA) was used to reduce the dimensionality of the dataset. According to the classification results, the proposed multi-layer neural network model reached optimal performance for detecting DDoS attacks and achieved classification accuracy at 99.9992%.</span>

Cloud Computing for Effective Cyber Security Attack Detection in Smart Cities

An astute metropolis is an urbanized region that accumulates data through diverse numerical and experiential understanding. Cloud-connected Internet of Things (IoT) solutions have the potential to aid intelligent cities in collecting data from inhabitants, devices, residences, and alternative origins. The monitoring and administration of carrying systems, plug-in services, reserve managing, H2O resource schemes, excess managing, illegal finding, safety actions, ability, numeral collection, healthcare abilities, and extra openings all make use of the processing and analysis of this data. This study aims to improve the security of smart cities by detecting attacks using algorithms drawn from the UNSW-NB15 and CICIDS2017 datasets and to create advanced strategies for identifying and justifying cyber threats in the context of smart cities by leveraging real-world network traffic data from UNSW-NB15 and labelled attack actions from CICIDS2017. The research aims to underwrite the development of more effective intrusion detection systems tailored to the unique problems of safeguarding networked urban environments, hence improving the flexibility and safety of smart cities by estimating these datasets.

Enhancing Privacy-Preserving Intrusion Detection through Federated Learning

Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective.

An effective technique for detecting minority attacks in NIDS using deep learning and sampling approach

Anomaly-based intrusion detection system have been consistently used in business organizations and military to detect a breach in network by identifying any activity that deviates from the baseline pattern. In this paper, we propose an effective intrusion detection technique to identify and predict the minority attacks with three layers. Here, the first layer utilizes a Weighted Deep Neural Network (WDNN) for identifying the suspicious traffic samples in network and it is passed to the second layer. Layer 2 classifies the traffic samples as normal or majority and minority attacks using Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). Any traffic sample classified as minority attack is sent to Layer 3 that utilizes XGBoost algorithm. Layer 3 classifies the samples into their respective minority attack classes. To boost the detection rate of minority attacks, system employs a One-Sided Selection under-sampling algorithm to remove noisy samples from the majority attack classes. An Adaptive Synthetic (ADASYN) oversampling algorithm generates synthetic samples of minority attack classes. To evaluate the system, the datasets namely NSL KDD, CICIDS-2017 and CIDDS 001 dataset are used. The system attained an overall accuracy of 97.94% on NSL KDD dataset, 98.3% on CICIDS-2017 dataset and 97.9% on CIDDS 001 dataset.

Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection

As Internet of Things devices become prevalent, using intrusion detection to protect IoT from malicious intrusions is of vital importance. However, the data scarcity of IoT hinders the effectiveness of traditional intrusion detection methods. To tackle this issue, in this paper, we propose the Adaptive Bi-Recommendation and Self-Improving Network (ABRSI) based on unsupervised heterogeneous domain adaptation (HDA). The ABRSI transfers enrich intrusion knowledge from a data-rich network intrusion source domain to facilitate effective intrusion detection for data-scarce IoT target domains. The ABRSI achieves fine-grained intrusion knowledge transfer via adaptive bi-recommendation matching. Matching the bi-recommendation interests of two recommender systems and the alignment of intrusion categories in the shared feature space form a mutual-benefit loop. Besides, the ABRSI uses a self-improving mechanism, autonomously improving the intrusion knowledge transfer from four ways. A hard pseudo label voting mechanism jointly considers recommender system decision and label relationship information to promote more accurate hard pseudo label assignment. To promote diversity and target data participation during intrusion knowledge transfer, target instances failing to be assigned with a hard pseudo label will be assigned with a probabilistic soft pseudo label, forming a hybrid pseudo-labelling strategy. Meanwhile, the ABRSI also makes soft pseudo-labels globally diverse and individually certain. Finally, an error knowledge learning mechanism is utilised to adversarially exploit factors that causes detection ambiguity and learns through both current and previous error knowledge, preventing error knowledge forgetfulness. Holistically, these mechanisms form the ABRSI model that boosts IoT intrusion detection accuracy via HDA-assisted intrusion knowledge transfer. Comprehensive experiments on several intrusion datasets demonstrate the state-of-the-art performance of the ABRSI method, outperforming its counterparts by 9.2%, and also verify the effectiveness of ABRSI constituting components and ABRSI’s overall efficiency.

Implementation of Efficient Intrusion Detection of Imbalanced Network Traffic using Deep Learning

A crucial component of network security is intrusion detection, but this task can be difficult when dealing with statistics on the distribution of network traffic, where benign cases exceed malicious ones by a large margin. The goal of this project is to create a powerful intrusion detection system using a recurrent neural network (RNN) to address this problem. RNNs are excellent at identifying temporal dependencies in sequential data, which makes them useful for examining network traffic patterns. The suggested methodology calls for preprocessing the data, balancing the dataset, feature extraction, choosing an RNN model, training, assessing, adjusting, and monitoring in real time. The network traffic data is cleaned and formatted during the preprocessing stage, and balancing algorithms make sure the RNN is trained on a representative dataset. It then extracts pertinent aspects, including behavioral patterns and packet header data. Effective intrusion detection requires the use of an appropriate RNN architecture, such as LSTM or GRU. The model is subsequently trained, assessed using a range of criteria, and refined through iterations. The optimized RNN model is then implemented for real-time intrusion detection, monitoring network traffic continuously and producing alerts for probable intrusions. This research seeks to improve the precision and efficacy of network security systems, maintaining the integrity of crucial information in the linked world of today, by utilizing RNNs for intrusion detection in imbalanced network trafficking. Keywords IDS, Imbalanced Network Traffic, Deep Learning, Recurrent Neural Network.

A Survey on Machine and Deep Learning Based Intrusion Detection Systems for IoT

Abstract: In recent times, the Internet of Things (IoT) and its diverse range of applications have emerged as one of the most popular and highly researched areas. The characteristics that make IoT easily applicable to real-life applications also expose it to cyber threats, thereby emphasizing the need for effective security measures. The rapid advancement of IoT is revolutionizing business processes and society as a whole. However, as this technology continues to evolve, it becomes increasingly important to prioritize the detection and awareness of vulnerabilities. Adopting a proactive approach is crucial to prevent unauthorized access to critical resources and business functions, thereby ensuring the continuous availability and operation of the system. Failing to prevent the occurrence of DoS and DDoS attacks can have adverse effects on data confidentiality, integrity, and availability, thereby highlighting the importance of effective intrusion detection and prevention measures. The literature has presented a wide range of intrusion detection methods aimed at addressing computer security threats. These methods can generally be categorized into