Enhanced Intrusion Detection Research Articles

A balanced communication-avoiding support vector machine decision tree method for smart intrusion detection systems

The Internet of Things field has created many challenges for network architectures. Ensuring cyberspace security is the primary goal of intrusion detection systems (IDSs). Due to the increases in the number and types of attacks, researchers have sought to improve intrusion detection systems by efficiently protecting the data and devices connected in cyberspace. IDS performance is essentially tied to the amount of data, data dimensionality, and security features. This paper proposes a novel IDS model to improve computational complexity by providing accurate detection in less processing time than other related works. The Gini index method is used to compute the impurity of the security features and refine the selection process. A balanced communication-avoiding support vector machine decision tree method is performed to enhance intrusion detection accuracy. The evaluation is conducted using the UNSW-NB 15 dataset, which is a real dataset and is available publicly. The proposed model achieves high attack detection performance, with an accuracy of approximately 98.5%.

A Review of Enhancing Intrusion Detection Systems for Cybersecurity Using Artificial Intelligence (AI)

Abstract The escalating complexity of cyber attacks demands innovative intrusion detection systems (IDS) to safeguard critical assets and data. The study aims to explore the potential of Artificial Intelligence (AI) in enhancing the IDS's ability to identify and classify network traffic and detect anomalous behavior. The paper offers a concise overview of IDS and AI and examines the existing literature on the subject, highlighting the significance of integrating advanced language models for cybersecurity enhancement. The research outlines the methodology employed to assess the efficacy of AI within IDS. Furthermore, the study considers key performance metrics such as detection accuracy, false positive rate, and response time to ensure a comprehensive evaluation. Findings indicate that AI is a valuable asset in enhancing the accuracy of AI for detecting and responding to cyber attacks. Nonetheless, the study also brings to light certain limitations and challenges associated with incorporating AI into IDS, such as computational complexity and potential biases in training data. This research emphasizes the potential of advanced language models like ChatGPT in augmenting cybersecurity solutions and offers insights into overcoming associated challenges for a more robust and effective defense against sophisticated cyber attacks.

Better Safe Than Never: A Survey on Adversarial Machine Learning Applications towards IoT Environment

Internet of Things (IoT) technologies serve as a backbone of cutting-edge intelligent systems. Machine Learning (ML) paradigms have been adopted within IoT environments to exploit their capabilities to mine complex patterns. Despite the reported promising results, ML-based solutions exhibit several security vulnerabilities and threats. Specifically, Adversarial Machine Learning (AML) attacks can drastically impact the performance of ML models. It also represents a promising research field that typically promotes novel techniques to generate and/or defend against Adversarial Examples (AE) attacks. In this work, a comprehensive survey on AML attack and defense techniques is conducted for the years 2018–2022. The article investigates the employment of AML techniques to enhance intrusion detection performance within the IoT context. Additionally, it depicts relevant challenges that researchers aim to overcome to implement proper IoT-based security solutions. Thus, this survey aims to contribute to the literature by investigating the application of AML concepts within the IoT context. An extensive review of the current research trends of AML within IoT networks is presented. A conclusion is reached where several findings are reported including a shortage of defense mechanisms investigations, a lack of tailored IoT-based solutions, and the applicability of the existing mechanisms in both attack and defense scenarios.

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks.

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner

Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS. However, its basic structure relies on the usage of traditional static machine learning models that receive onetime training, limiting its ability to handle CD. To address this issue, we propose an extended variant of the GPC using three main components. First, we replace existing classifiers with alternatives: online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM). Second, we add two new components to the GPC, specifically, a data balancing and a classifier update. Third, the coordination between the sub-models produces three novel variants of the GPC: GPC-KOS for KA-OSELM; GPC-FOS for FA-OSELM; and GPC-OS for OSELM. This article presents the first data stream-based classification framework that provides novel strategies for handling CD variants. The experimental results demonstrate that both GPC-KOS and GPC-FOS outperform the traditional GPC and other state-of-the-art methods, and the transfer learning and memory features contribute to the effective handling of most types of CD. Moreover, the application of our incremental variants on real-world datasets (KDD Cup '99, CICIDS-2017, CSE-CIC-IDS-2018, and ISCX '12) demonstrate improved performance (GPC-FOS in connection with CSE-CIC-IDS-2018 and CICIDS-2017; GPC-KOS in connection with ISCX2012 and KDD Cup '99), with maximum accuracy rates of 100% and 98% by GPC-KOS and GPC-FOS, respectively. Additionally, our GPC variants do not show superior performance in handling blip drift.

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

An enhanced intrusion detection method for AIM of smart grid

An enhanced intrusion detection method for AIM of smart grid

Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset (IDSAI)

Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset (IDSAI)

A novel methodology for enhancing intrusion detection system

An Intrusion Detection System (IDS) monitors network traffic for suspicious activity and alerts when such an activity is discovered. In this study, the NSL-KDD cup 99 dataset was used to evaluate anomaly detection from intruders. Intrusion Detection System, Distributed Denial of Service (DDoS), Deep Belief Network (DBN), Random Forest, Naïve Bayes, Security Attack, Machine Learning. Pre-processing and normalization processes were performed on the dataset with inadequate, noisy, or duplicate data. A hybrid K-means clustering algorithm is used to combine clusters, which are classified using Deep Belief Networks (DBNs), Random Forest and Naïve Bayes. The study analyzed the dataset based on accuracy, precision, F-score, and false alarm rate, among which the DBN showed better performance than the other two ML algorithms.

Enhancing Intrusion Detection Systems with XGBoost Feature Selection and Deep Learning Approaches

Enhancing Intrusion Detection Systems with XGBoost Feature Selection and Deep Learning Approaches

Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets. Besides, DRL model is employed for the proper identification and classification of intrusions that exist in the network. Furthermore, beetle antenna search (BAS) technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency. For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model, a wide-ranging experimental analysis is performed against benchmark dataset. The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

An Improved Intrusion Detection in Wireless Sensor Networks Using Hybrid Multiclass Over-Sampling and Deep Neural Networks

With the emergence of new attacks, there is a continual need for innovative approaches that can closely monitor and swiftly adapt to evolving threats. IDSs can be broadly categorized into misuse detection and anomaly detection, each utilizing machine-learning methods. Machine learning algorithms, particularly those relying on datasets like DARPA and KDD Cup 1999,have gained popularity. However, challenges include dataset limitations, overfitting, and the requirement for substantial computational power. This study focuses on the specific problem of class imbalance in Wireless Sensor Networks (WSN) datasets for intrusion detection. Existing techniques, such as oversampling and undersampling, have limitations, and the imbalance poses challenges for accurate intrusion detection model development. The research aims to develop an enhanced intrusion detection model addressing multiclass imbalance through an optimized KNN-SMOTE oversampling technique integrated with a Deep Learning model. The significance lies in its potential to greatly enhance the accuracy of intrusion detection systems, contributing to the security ofcomputer networks. The study's scope involves static dataset analysis and multiclass classification of intrusion attacks. The research questions revolve around the development of a multiclass oversampling technique, a hybrid model, and the performance evaluation of the developed system in comparison to existing IDS.The research proposes a novel OK-SMOTE-DL model combining the potential of the KNN model, firefly algorithm, and the SMOTE model fused with Deep Learning techniques to help create a better model for handling multiclass intrusion datasets that can similarly detect minority classes effectively in wireless sensornetworks

Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions

This paper introduces an innovative intrusion detection system that harnesses Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks, supplemented by Local Interpretable Model-Agnostic Explanations (LIME) for interpretability. Employing a GAN, the system generates realistic network traffic data, encompassing both normal and attack patterns. This synthesized data is then fed into an MSCNN-BiLSTM architecture for intrusion detection. The MSCNN layer extracts features from the network traffic data at different scales, while the BiLSTM layer captures temporal dependencies within the traffic sequences. Integration of LIME allows for explaining the model's decisions. Evaluation on the Hogzilla dataset, a standard benchmark, showcases an impressive accuracy of 99,16 % for multi-class classification and 99,10 % for binary classification, while ensuring interpretability through LIME. This fusion of deep learning and interpretability presents a promising avenue for enhancing intrusion detection systems by improving transparency and decision support in network security

Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.

Contrastive Learning Enhanced Intrusion Detection

With the continuous development of network technology, the diversity of network traffic constantly increased (intra-class diversity). Nevertheless, the boundary between malicious and benign actions became even ambiguous (inter-class similarity), causing lots of false detection and hindering the further optimization of the detection model. Focusing on challenges brought by intra-class diversity and inter-class similarity, we proposed a novel approach to enhance intrusion detection based on contrastive learning, which can make the right decision while disentangling samples from different classes. First, to bridge the gap when applying contrastive learning to intrusion detection data, we proposed a heuristic method to build contrastive tasks based on random masking of network packet sequences, which can reflect semantic relationships among samples. Then contrastive loss can be calculated to measure the inter-class and intra-class distances. Second, contrastive cross-entropy loss was proposed, which was a combination of contrastive loss and classification loss. Together with a dual branch deep structure, we can optimize the detection and sample distance requirements at the same time. Thirdly, experiments were conducted on diverse real-world and benchmark datasets using different model architectures under various parameter settings. Results on real-world dataset showed that our methods could stable experience a 5% increase in accuracy, and an 8% improvement in detection rate on those easily misdetected scenarios. To verify the method’s effectiveness on different traffic representations, we further conducted experiments on NSL-KDD and UNSW-NB15, which achieved a 7% accuracy improvement on NSL-KDD and a 6% accuracy improvement on UNSW-NB15. Extensive comparison with state of art intrusion detection models in recent five years showed that the proposed methods could effectively improve the accuracy of detection models.

Analysis of Machine Learning Systems for Cyber Physical Systems

This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.

Analysis of Machine Learning Systems for Cyber Physical Systems

This study summarizes major literature reviews on machine learning systems for network analysis and intrusion detection. Furthermore, it provides a brief lesson description of each machine learning approach. Because data is so important in machine learning methods, this study The primary tools for assessing network traffic and spotting anomalies are machine learning approaches, and the study focuses on the datasets utilized in these techniques. This research examine the multiple advantages (reasonable use) that machine learning has made possible, particularly for security and cyber-physical systems, including enhanced intrusion detection techniques and judgment accuracy. Additionally, this study discusses the difficulties of utilizing machine learning for cybersecurity and offers suggestions for further study.

Leveraging siamese networks for one-shot intrusion detection model

The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems (IDS) has been the subject of significant research. Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to retrain the model for every unseen cyber-attack class. However, retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data. Although anomaly detection systems provide a coarse-grained defence against unseen attacks, these approaches are significantly less accurate and suffer from high false-positive rates. Here, a complementary approach referred to as “One-Shot Learning”, whereby a limited number of examples of a new attack-class is used to identify a new attack-class (out of many) is detailed. The model grants a new cyber-attack classification opportunity for classes that were not seen during training without retraining. A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks. The performance of a pre-trained model to classify new attack-classes based only on one example is evaluated using three mainstream IDS datasets; CICIDS2017, NSL-KDD, and KDD Cup’99. The results confirm the adaptability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representations.

Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning

Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning