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）
Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Khondokar Fida Hasan, Selina Sharmin, Salem A. Alyami, Mohammad Ali Moni
备注：
Accepted in Journal of Big Data (Q1, IF: 8.1, SCIE) on Jan 19, 2024
Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for detecting dynamic cyber threats, identifying abnormalities, and identifying malicious conduct within the network. However, as the number of data grows, dimension reduction becomes an increasingly difficult task when training ML models. Addressing this, our paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding based on clustering results, as well as Principal Component Analysis (PCA) for dimension reduction and is specifically designed for large and imbalanced datasets. This model's performance is carefully evaluated using three cutting-edge benchmark datasets: UNSW-NB15, CIC-IDS-2017, and CIC-IDS-2018. On the UNSW-NB15 dataset, our trials show that the RF and ET models achieve accuracy rates of 99.59% and 99.95%, respectively. Furthermore, using the CIC-IDS2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS2018. These performance results continuously outperform the state-of-art, indicating significant progress in the field of network intrusion detection. This achievement demonstrates the efficacy of the suggested methodology, which can be used practically to accurately monitor and identify network traffic intrusions, thereby blocking possible threats.
With massive data being generated daily and the ever-increasing interconnectivity of the world's Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in …
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction
School of Information Science and Technology, Southwest Jiaotong University（西南交通大学信息科学与技术学院）；School of Integrated Circuits, Peking University（北京大学集成电路学院）
Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）
The real-time crash likelihood prediction has been an important research topic. Various classifiers, such as support vector machine (SVM) and tree-based boosting algorithms, have been proposed in traffic safety studies. However, few research focuses on the missing data imputation in real-time crash likelihood prediction, although missing values are commonly observed due to breakdown of sensors or external interference. Besides, classifying imbalanced data is also a difficult problem in real-time crash likelihood prediction, since it is hard to distinguish crash-prone cases from non-crash cases…
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）
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 …
A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data
School of Electrical Engineering and Computer Science University of Ottawa（电气工程与计算机科学学院，渥太华大学）；Department of Computer Engineering K.N.Toosi University（计算机工程系，K.N.Toosi大学）
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…
Amazon（亚马逊）；New York University（纽约大学）；Infosys（印孚瑟斯）；Samsung（三星）；Veermata Jijabai Technological Institute（韦尔马塔·吉贾拜理工学院）
Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDD~CUP~99 dataset as a benchmark. Several studies question its usability while constructing a contemporary NIDS, due to the skewed response distribution, non-stationarity, and failure to incorporate modern attacks. In this paper, we compare the performance for KDD-99 alternatives when trained using classification models commonly found in literature: Neural Network, Support Vector Machine, Decision Tree, Rando…
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 …
KU Leuven（鲁汶大学）；Applied Statistics GmbH（应用统计有限公司）；University of Antwerp（安特卫普大学）
A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. In most data sets, fraud occurs in typically less than 0.5% of the cases. Detecting fraud in such a highly imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected. We discuss some popular oversampling techniques that solve the problem of imbalanced data by creating synthetic samples that mimic the minority class. A frequent problem when analyzing real data is the presence of anom…
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…
University of Western Ontario（西安大略大学）；University of Sharjah（沙迦大学）
Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities. Several previous machine learning (ML)-based network intrusion detection systems (NIDSs) have been developed to protect against malicious online behavior. This paper proposes a novel multi-stage optimized ML-based NIDS framework that reduces computational complexity while maintaining its detection performance. This work studies the impact of oversampling techniques on the models' training samp…
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））
Background: Machine learning techniques have been widely used and demonstrate promising performance in many software security tasks such as software vulnerability prediction. However, the class ratio within software vulnerability datasets is often highly imbalanced (since the percentage of observed vulnerability is usually very low). Goal: To help security practitioners address software security data class imbalanced issues and further help build better prediction models with resampled datasets. Method: We introduce an approach called Dazzle which is an optimized version of conditional Wassers…
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…
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…
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）
