Intrusion detection system (IDS) plays an essential role in computer networks protecting computing resources and data from outside attacks. Recent IDS faces challenges improving flexibility and efficiency of the IDS for unexpected and unpredictable attacks. Deep neural network (DNN) is considered popularly for complex systems to abstract features and learn as a machine learning technique. In this paper, we propose a deep learning approach for developing the efficient and flexible IDS using one-dimensional Convolutional Neural Network (1D-CNN). Two-dimensional CNN methods have shown remarkable performance in detecting objects of images in computer vision area. Meanwhile, the 1D-CNN can be used for supervised learning on time-series data. We establish a machine learning model based on the 1D-CNN by serializing Transmission Control Protocol/Internet Protocol (TCP/IP) packets in a predetermined time range as an invasion Internet traffic model for the IDS, where normal and abnormal network traffics are categorized and labeled for supervised learning in the 1D-CNN. We evaluated our model on UNSW\_NB15 IDS dataset to show the effectiveness of our method. For comparison study in performance, machine learning-based Random Forest (RF) and Support Vector Machine (SVM) models in addition to the 1D-CNN with various network parameters and architecture are exploited. In each experiment, the models are run up to 200 epochs with a learning rate in 0.0001 on imbalanced and balanced data. 1D-CNN and its variant architectures have outperformed compared to the classical machine learning classifiers. This is mainly due to the reason that CNN has the capability to extract high-level feature representations that represent the abstract form of low-level feature sets of network traffic connections.
nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection
Department of Computer Science（计算机科学系）；New Mexico State University（新墨西哥州立大学）；Graduate School of Informatics（信息科学研究生院）；Osaka Metropolitan University（大阪 Metropolitan 大学）
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
International University of Business Agriculture and Technology（国际商业农业技术大学）；Jagannath University（贾甘纳特大学）；Deakin University（迪肯大学）；University of New South Wales（新南威尔士大学）；Imam Mohammad Ibn Saud Islamic University（伊玛目穆罕默德·伊本·沙特伊斯兰大学）；The University of Queensland（昆士兰大学）
The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are converging together day by day to introduce a common platform on hybrid systems. Moreover, the combination of artificial intelligence (AI) with CPS creates a new dimension of technological advancement. All these connectivity and dependability are creating massive space for the attackers to launch cyber attacks. To defend against these attacks, intrusion detection system (IDS) has been widely used. However, emerging CPS technologies suffer from imbalanced and missing sample data, which makes the training of IDS …
Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are an essential security technology for detecting these attacks. Although numerous machine learning-based IDS have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) mo…
Generative adversarial networks have been able to generate striking results in various domains. This generation capability can be general while the networks gain deep understanding regarding the data distribution. In many domains, this data distribution consists of anomalies and normal data, with the anomalies commonly occurring relatively less, creating datasets that are imbalanced. The capabilities that generative adversarial networks offer can be leveraged to examine these anomalies and help alleviate the challenge that imbalanced datasets propose via creating synthetic anomalies. This anom…
Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN
Department of Control Science & Engineering, Tongji University（同济大学控制科学与工程系）；National Key Laboratory of Autonomous Intelligent Unmanned Systems（国家自主智能无人系统重点实验室）
In intrusion detection systems, classifiers still suffer from several drawbacks such as data dimensionality and dominance, different network feature types, and data impact on the classification. In this paper two significant enhancements are presented to solve these drawbacks. The first enhancement is an improved feature selection using sequential backward search and information gain. This, in turn, extracts valuable features that enhance positively the detection rate and reduce the false positive rate. The second enhancement is transferring nominal network features to numeric ones by exploiti…
Montclair State University（蒙特克莱尔州立大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Dalian University of Technology（大连理工大学）
Cyber attacks pose crucial threats to computer system security, and put digital treasuries at excessive risks. This leads to an urgent call for an effective intrusion detection system that can identify the intrusion attacks with high accuracy. It is challenging to classify the intrusion events due to the wide variety of attacks. Furthermore, in a normal network environment, a majority of the connections are initiated by benign behaviors. The class imbalance issue in intrusion detection forces the classifier to be biased toward the majority/benign class, thus leave many attack incidents undetec…
Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface
Bangladesh University of Business and Technology (BUBT)（孟加拉国商业与技术大学（BUBT））；Jagannath University（贾格纳特大学）；University of New South Wales (UNSW)（新南威尔士大学（UNSW））
University of Exeter（埃克塞特大学）；China University of Petroleum (East China)（中国石油大学（华东））
Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and threats in networking systems. As fundamental tools of IDSs, learning based classification methods have been widely employed. When it comes to detecting network intrusions in small sample sizes (e.g., emerging intrusions), the limited number and imbalanced proportion of training samples usually cause significant challenges in training supervised and semi-supervised classifiers. In this paper, we propose a general network intrusion detection framework to address the challenges of both \emph{data scarc…
Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks
Department of Computer Science and Technology, Bangladesh Sweden Polytechnic Institute（孟加拉瑞典理工学院计算机科学与技术系）；Department of Electrical Engineering, Bangladesh Sweden Polytechnic Institute（孟加拉瑞典理工学院电气工程系）
Machine Learning-based supervised approaches require highly customized and fine-tuned methodologies to deliver outstanding performance. This paper presents a dataset-driven design and performance evaluation of a machine learning classifier for the network intrusion dataset UNSW-NB15. Analysis of the dataset suggests that it suffers from class representation imbalance and class overlap in the feature space. We employed ensemble methods using Balanced Bagging (BB), eXtreme Gradient Boosting (XGBoost), and Random Forest empowered by Hellinger Distance Decision Tree (RF-HDDT). BB and XGBoost are t…
NIDSs identify malicious activities by analyzing network traffic. NIDSs are trained with the samples of benign and intrusive network traffic. Training samples belong to either majority or minority classes depending upon the number of available instances. Majority classes consist of abundant samples for the normal traffic as well as for recurrent intrusions. Whereas, minority classes include fewer samples for unknown events or infrequent intrusions. NIDSs trained on such imbalanced data tend to give biased predictions against minority attack classes, causing undetected or misclassified intrusio…
The internet has caused tremendous changes since its appearance in the 1980s, and now, the Internet of Things (IoT) seems to be doing the same. The potential of IoT has made it the center of attention for many people, but, where some see an opportunity to contribute, others may see IoT networks as a target to be exploited. The high number of IoT devices makes them the perfect setup for staging denial-of-service attacks (DoS) that can have devastating consequences. This renders the need for cybersecurity measures such as intrusion detection systems (IDSs) evident. The aim of this paper is to bu…
School of Computer Science and Technology, Changchun University of Science and Technology（长春理工大学计算机科学与技术学院）
One of the key challenges of machine learning (ML) based intrusion detection system (IDS) is the expensive computational complexity which is largely due to redundant, incomplete, and irrelevant features contain in the IDS datasets. To overcome such challenge and ensure building an efficient and more accurate IDS models, many researchers utilize preprocessing techniques such as normalization and feature selection in a hybrid modeling approach. In this work, we propose a hybrid IDS modeling approach with an algorithm for feature selection (FS) and another for building an IDS. The FS algorithm is…
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems
University of Technology Sydney（悉尼科技大学）；Le Quy Don Technical University（黎贵惇技术大学）；University of Engineering and Technology, Vietnam National University, Hanoi（河内越南国家大学工程技术大学）
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic
Center for Responsible Artificial Intelligence, University of Applied Sciences and Arts Coburg（科堡应用科学与艺术大学负责任人工智能中心）；Center for Artificial Intelligence and Data Science, University of Würzburg（维尔茨堡大学人工智能与数据科学中心）
With the rapid technological advancements, organizations need to rapidly scale up their information technology (IT) infrastructure viz. hardware, software, and services, at a low cost. However, the dynamic growth in the network services and applications creates security vulnerabilities and new risks that can be exploited by various attacks. For example, User to Root (U2R) and Remote to Local (R2L) attack categories can cause a significant damage and paralyze the entire network system. Such attacks are not easy to detect due to the high degree of similarity to normal traffic. While network anom…
Background: Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised machine learning algorithms. In such a paradigm, models need a large amount of labeled data to learn the useful relationships between selected features and the target class. However, such labeled data can be scarce and expensive to acquire. Goal: To help security practitioners train useful security classification models when few labeled training data and many unlabeled training data are available. Method: We propose an a…
Joint Stock "Research and production company "Kryptonite"（“氪石”科研生产联合股份公司）
Network Intrusion and Detection Systems (NIDS) are essential for malicious traffic and cyberattack detection in modern networks. Artificial intelligence-based NIDS are powerful tools that can learn complex data correlations for accurate attack prediction. Graph Neural Networks (GNNs) provide an opportunity to analyze network topology along with flow features which makes them particularly suitable for NIDS applications. However, successful application of such tool requires large amounts of carefully collected and labeled data for training and testing. In this paper we inspect different versions…
