Advancements in computer networks and communication technologies like software defined networks (SDN), Internet of things (IoT), microservices architecture, cloud computing and network function virtualization (NFV) have opened new fronts and challenges for security experts to combat against modern cyberattacks. Relying on perimeter defense and signature-based network security solutions like Intrusion Detection and Prevention Systems (IDS/IPS) have failed to deliver adequate level of security against new attack vectors such as advance persistent threats, zero days, ransomware, botnets and other forms of targeted attacks. Recent developments in machine learning and cognitive computing have shown great potential to detect unknown and new intrusion events where legacy misuse and anomaly based intrusion detection systems usually fail. In this research study we applied state of the art machine learning algorithms on UNSW-NB15 dataset for potential applicability to detect new attacks. We also proposed a novel wrapper based feature selection technique TS-RF using metaheuristic Tabu Search (TS) algorithm and Random Forest (RF) ensemble classifier. Results obtained by applying proposed feature selection technique i.e. TS-RF on UNSW-NB15 dataset show improvement in overall intrusion detection accuracy while it reduces computation complexity as it removes more than 60% features.
In this paper, we analyze existing feature selection methods to identify the key elements of network traffic data that allow intrusion detection. In addition, we propose a new feature selection method that addresses the challenge of considering continuous input features and discrete target values. We show that the proposed method performs well against the benchmark selection methods. We use our findings to develop a highly effective machine learning-based detection systems that achieves 99.9% accuracy in distinguishing between DDoS and benign signals. We believe that our results can be useful …
Due to the size and nature of intrusion detection datasets, intrusion detection systems (IDS) typically take high computational complexity to examine features of data and identify intrusive patterns. Data preprocessing techniques such as feature selection can be used to reduce such complexity by eliminating irrelevant and redundant features in the dataset. The objective of this study is to analyze the efficiency and effectiveness of some feature selection approaches namely, wrapper-based and filter-based modeling approaches. To achieve that, a hybrid of feature selection algorithm in combinati…
In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.
Characteristics and way of behavior of attacks and infiltrators on computer networks are usually very difficult and need an expert In addition; the advancement of computer networks, the number of attacks and infiltrations are also increasing. In fact, the knowledge coming from an expert will lose its value over time and must be updated and made available to the system and this makes the need for the expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such a…
Despite the great developments in information technology, particularly the Internet, computer networks, global information exchange, and its positive impact in all areas of daily life, it has also contributed to the development of penetration and intrusion which forms a high risk to the security of information organizations, government agencies, and causes large economic losses. There are many techniques designed for protection such as firewall and intrusion detection systems (IDS). IDS is a set of software and/or hardware techniques used to detect hacker's activities in computer systems. Two …
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental learning of support vector machines in order to rank the features in real time within a streaming model for network data. Some illustrative numerical experiments with two popular benchmark datasets show that our approach allows to adapt to the changes in normal network behaviour an…
Recently, the Distributed Denial of Service (DDOS) attacks has been used for different aspects to denial the number of services for the end-users. Therefore, there is an urgent need to design an effective detection method against this type of attack. A fuzzy inference system offers the results in a more readable and understandable form. This paper introduces an anomaly-based Intrusion Detection (IDS) system using fuzzy logic. The fuzzy logic inference system implemented as a detection method for Distributed Denial of Service (DDOS) attacks. The suggested method was applied to an open-source DD…
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 Intrusion Detection: Feature Selection versus Feature Extraction
MobiFone Research and Development Center, MobiFone Corporation（MobiFone公司MobiFone研发中心）；Hanoi University of Science and Technology（河内科技大学）；Phenikaa University（菲尼卡大学）
Moghadas Ardabili Institute of Higher Education（莫格达斯·阿尔达比利高等教育学院）；National Industrial Group（国家工业集团）
Preventing and detecting intrusions and attacks on wireless networks has become an important and serious challenge. On the other hand, due to the limited resources of wireless nodes, the use of monitoring nodes for permanent monitoring in wireless sensor networks in order to prevent and detect intrusion and attacks in this type of network is practically non-existent. Therefore, the solution to overcome this problem today is the discussion of remote-control systems and has become one of the topics of interest in various fields. Remote monitoring of node performance and behavior in wireless sens…
The effectiveness of machine learning models is significantly affected by the size of the dataset and the quality of features as redundant and irrelevant features can radically degrade the performance. This paper proposes IGRF-RFE: a hybrid feature selection method tasked for multi-class network anomalies using a Multilayer perceptron (MLP) network. IGRF-RFE can be considered as a feature reduction technique based on both the filter feature selection method and the wrapper feature selection method. In our proposed method, we use the filter feature selection method, which is the combination of …
Southeast University（东南大学）；National Key Laboratory of Science and Technology on Information System Security（信息系统安全技术国家重点实验室）
Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform …
University of Salerno（萨莱诺大学）；Amazon AWS（亚马逊AWS）；University of Calabria（卡拉布里亚大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）
Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: i) the presence of so many features leads to lengthy training processes (part…
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…
Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System
Faculty of Business and Information Technology, Ontario Tech University（业务与信息科技学院，安大略技术大学）；Department of Electrical and Computer Engineering, Western University（电气与计算机工程系，西部大学）
University of Lahore（拉合尔大学）；NUCES（巴基斯坦国家计算机与新兴科学大学）
Network security has become the biggest concern in the area of cyber security because of the exponential growth in computer networks and applications. Intrusion detection plays an important role in the security of information systems or networks devices. The purpose of an intrusion detection system (IDS) is to detect malicious activities and then generate an alarm against these activities. Having a large amount of data is one of the key problems in detecting attacks. Most of the intrusion detection systems use all features of datasets to evaluate the models and result in is, low detection rate…
BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in 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）
Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms of privacy violations which may threaten end-user privacy. Therefore, to mitigate risk and preserve a balance between security and privacy, it is imperative to protect user privacy with respect to intrusion data. Moreover, cost is a driver of a machine learning-based NIDS because such systems are increasingly being deployed on resource-limited edge …
North Carolina Agricultural and Technical State University（北卡罗来纳农业技术州立大学）
In the realm of cybersecurity, intrusion detection systems (IDS) detect and prevent attacks based on collected computer and network data. In recent research, IDS models have been constructed using machine learning (ML) and deep learning (DL) methods such as Random Forest (RF) and deep neural networks (DNN). Feature selection (FS) can be used to construct faster, more interpretable, and more accurate models. We look at three different FS techniques; RF information gain (RF-IG), correlation feature selection using the Bat Algorithm (CFS-BA), and CFS using the Aquila Optimizer (CFS-AO). Our resul…
Software-Defined Networking (SDN) is the next generation to change the architecture of traditional networks. SDN is one of the promising solutions to change the architecture of internet networks. Attacks become more common due to the centralized nature of SDN architecture. It is vital to provide security for the SDN. In this study, we propose a Network Intrusion Detection System-Deep Learning module (NIDS-DL) approach in the context of SDN. Our suggested method combines Network Intrusion Detection Systems (NIDS) with many types of deep learning algorithms. Our approach employs 12 features extr…
Efficient Network Traffic Feature Sets for IoT Intrusion Detection
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院智能工程与先进创新计算研究组）
本文组合多种特征选择方法，在多个 IoT 网络数据集上筛选高效流量特征，以在保持分类泛化性能的同时降低 ML 模型训练开销。
Internet of Things (IoT) networks have become an increasingly attractive target of cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to implement network intrusion detection systems to protect IoT networks. For the successful training of such ML models, selecting the right data features is crucial, maximising the detection accuracy and computational efficiency. This paper comprehensively analyses feature sets' importance and predictive power for detecting network attacks. Three feature selection algorithms: chi-square, information gain and correlation, have been ut…
The rapid expansion of the Internet of Things and the emergence of edge computing-based applications has led to a new wave of cyber-attacks, with intensity and complexity that has never been seen before. Historically most research has focused on Intrusion Detection Systems (IDS), however due to the volume and speed of this new generation of cyber-attacks it is no longer sufficient to solely detect attacks and leave the response to security analysts. Consequently, research into Intrusion Response Systems (IRS) is accelerating rapidly. As such, new intrusion response approaches, methods and syst…
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems
School of Electrical and Data Engineering, University of Technology Sydney (UTS)（电气与数据工程学院，悉尼大学技术学院）；Department of Electrical and Computer Engineering, University of Arizona（电气与计算机工程系，亚利桑那大学）；School of Electrical and Data Engineering, the University of Technology Sydney（电气与数据工程学院，悉尼大学技术学院）；Computer Science Department, Faculty of Information Technology, Le Quy Don Technical University（信息科技学院计算机科学系，吕文技术大学）；Vietnam National University, Hanoi, Vietnam（越南国家大学，河内，越南）
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…
Western University（西部大学）；National Research Council (NRC)（国家研究委员会）
The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detecti…
Due to an exponential increase in the number of cyber-attacks, the need for improved Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning (ML) techniques are playing a pivotal role in the early classification of the attacks in case of intrusion detection within the system. However, due to a large number of algorithms available, the selection of the right method is a challenging task. To resolve this issue, this paper analyses some of the current state-of-the-art intrusion detection methods and discusses their pros and cons. Further, a review of different ML…
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
Department of Computer Sciences, University of Liverpool（计算机科学系，利物浦大学）；Centre for Doctoral Training (CDT) in Distributed Algorithm, University of Liverpool（分布式算法博士培训中心，利物浦大学）；Department of Electrical Engineering and Electronics, University of Liverpool（电子工程与电子系，利物浦大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）
With the ubiquitous nature of information technology solutions that facilitate communication in the modern world, cyber attacks are increasing in volume and becoming more sophisticated in nature. From classic network-based Denial of Service (DoS) attacks to the more recent concerns of privacy compromises, Intrusion Detection Systems (IDS) are becoming an urgent need to safeguard the modern information technology landscape. The development of these IDS relies on training and evaluation datasets that must evolve with time and represent the contemporary threat landscape. The purpose of this analy…
College of Information Science, University of Arizona, Tucson, AZ, USA（信息科学学院，亚利桑那大学，图森，亚利桑那州，美国）
In this paper, we propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and multi-head attention mechanisms to significantly enhance cybersecurity intrusion detection capabilities. By leveraging the comprehensive UNSW-NB15 dataset containing diverse network traffic patterns, our approach effectively captures both spatial dependencies through graph structural relationships and temporal dynamics through sequential analysis of network events. The integrated attention mechanism provides dual benefits of impro…
University of Rome Tor Vergata（罗马第二大学）；Sapienza University of Rome（罗马第一大学）；Roma Tre University（罗马第三大学）；Mississippi State University（密西西比州立大学）
Intrusion Response is a relatively new field of research. Recent approaches for the creation of Intrusion Response Systems (IRSs) use Reinforcement Learning (RL) as a primary technique for the optimal or near-optimal selection of the proper countermeasure to take in order to stop or mitigate an ongoing attack. However, most of them do not consider the fact that systems can change over time or, in other words, that systems exhibit a non-stationary behavior. Furthermore, stateful approaches, such as those based on RL, suffer the curse of dimensionality, due to a state space growing exponentially…
