Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise seventeen unsupervised anomaly detection algorithms on eleven attack datasets. Results allow elaborating on a wide range of arguments, from the behavior of the individual algorithm to the suitability of the datasets to anomaly detection. We conclude that algorithms as Isolation Forests, One-Class Support Vector Machines and Self-Organizing Maps are more effective than their counterparts for intrusion detection, while clustering algorithms represent a good alternative due to their low computational complexity. Further, we detail how attacks with unstable, distributed or non-repeatable behavior as Fuzzing, Worms and Botnets are more difficult to detect. Ultimately, we digress on capabilities of algorithms in detecting anomalies generated by a wide pool of unknown attacks, showing that achieved metric scores do not vary with respect to identifying single attacks.
Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited s…
Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks
Dept. of Computer Engineering, The Hashemite University（计算机工程系，哈希米大学）；School of Computer Science and Technology, Algoma University（计算机科学与技术学院，阿尔戈马大学）；College of Computer Science and Engineering, University of Jeddah（计算机科学与工程学院，朱德赫大学）；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）
The extensive use of Information and Communication Technology in critical infrastructures such as Industrial Control Systems make them vulnerable to cyber-attacks. One particular class of cyber-attacks is advanced persistent threats where highly skilled attackers can steal user authentication information's and move in the network from host to host until a valuable target is reached. The detection of the attacker should occur as soon as possible in order to take appropriate response, otherwise the attacker will have enough time to reach sensitive assets. When facing intelligent threats, intelli…
Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
University of New Mexico（新墨西哥大学）；Florida International University（佛罗里达国际大学）；Florida Atlantic University（佛罗里达大西洋大学）；University of Washington（华盛顿大学）；Islamic Azad University（伊斯兰阿扎德大学）
Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection
A Risk Manager for Intrusion Tolerant Systems: Enhancing HAL 9000 with New Scoring and Data Sources
Department of Computer Science（计算机科学系）；Faculty of Sciences of University of Porto（葡萄牙波尔图大学科学学院）；Centre Advanced Computing Systems（先进计算系统中心）；Institute for Systems and Computer Engineering, Technology and Science（系统与计算机工程、科技与科学研究所）；UMBC（美国马里兰大学巴尔的摩县分校）
本文通过引入定制爬虫持续挖掘安全公告、研究论坛和实时漏洞利用概念验证等多样化威胁来源，扩展了入侵容忍系统风险管理器 HAL 9000 的情报基础，使其能更早检测和评估未验证漏洞，并验证了该集成显著提升了对新兴威胁的应对能力。
Mawlana Bhashani Science and Technology University（莫拉纳·巴沙尼科技大学）；Deakin University（迪肯大学）；RMIT University（皇家墨尔本理工大学）；University of Saskatchewan（萨斯喀彻温大学）；Charles Sturt University（查尔斯史都华大学）
Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experience…
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Politehnica University of Timisoara（蒂米什瓦拉理工大学）
The Controller Area Network (CAN) is used for communication between in-vehicle devices. The CAN bus has been shown to be vulnerable to remote attacks. To harden vehicles against such attacks, vehicle manufacturers have divided in-vehicle networks into sub-networks, logically isolating critical devices. However, attackers may still have physical access to various sub-networks where they can connect a malicious device. This threat has not been adequately addressed, as methods proposed to determine physical intrusion points have shown weak results, emphasizing the need to develop more advanced te…
HybridGuard: Enhancing Minority-Class Intrusion Detection in Dew-Enabled Edge-of-Things Networks
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology（计算机科学与信息工程系，台湾科技大学）；Department of Computer Science and Engineering, Digital University Kerala（计算机科学与工程系，数字大学喀拉拉）
University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）
Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties - violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communicat…
The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The utilisation of Machine Learning (ML) capabilities in the defence against IoT cyber attacks has many potential benefits. However, the currently proposed frameworks do not consider data privacy, secure architectures, and/or scalable deployments of IoT ecosystems. In this paper, we propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion dete…
DNS Tunneling: Threat Landscape and Improved Detection Solutions
Cyber Security and Privacy Research Lab., SPFLab（网络安全与隐私研究实验室，SPFLab）；Faculty of Computer and Informatics Engineering（计算机与信息工程学院）；Istanbul Technical University（伊斯坦布尔技术大学）
Zhejiang Normal University（浙江师范大学）；NingboTech University（浙大宁波理工学院）
Intrusion detection has been a key topic in the field of cyber security, and the common network threats nowadays have the characteristics of varieties and variation. Considering the serious imbalance of intrusion detection datasets will result in low classification performance on attack behaviors of small sample size and difficulty to detect network attacks accurately and efficiently, using Adaptive Synthetic Sampling (ADASYN) method to balance datasets was proposed in this paper. In addition, Random Forest algorithm was used to train intrusion detection classifiers. Through the comparative ex…
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
As the number of cyber-attacks is increasing, cybersecurity is evolving to a key concern for any business. Artificial Intelligence (AI) and Machine Learning (ML) (in particular Deep Learning - DL) can be leveraged as key enabling technologies for cyber-defense, since they can contribute in threat detection and can even provide recommended actions to cyber analysts. A partnership of industry, academia, and government on a global scale is necessary in order to advance the adoption of AI/ML to cybersecurity and create efficient cyber defense systems. In this paper, we are concerned with the inves…
Adversarial attacks have been widely studied in the field of computer vision but their impact on network security applications remains an area of open research. As IoT, 5G and AI continue to converge to realize the promise of the fourth industrial revolution (Industry 4.0), security incidents and events on IoT networks have increased. Deep learning techniques are being applied to detect and mitigate many of such security threats against IoT networks. Feedforward Neural Networks (FNN) have been widely used for classifying intrusion attacks in IoT networks. In this paper, we consider a variant o…
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…
Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset
Computer Science and Engineering, Wright State University, Ohio, USA（计算机科学与工程系，怀特州立大学，俄亥俄州，美国）；Computer Science and Engineering, Azad University, Ahvaz, Iran（计算机科学与工程系，阿扎德大学，阿瓦兹，伊朗）
The University of Hong Kong（香港大学）；University of Ottawa（渥太华大学）
The high volume of increasingly sophisticated cyber threats is drawing growing attention to cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection, new algorithms that are more robust, effective, and able to use more information are needed. Moreover, the intrusion detection task faces a serious challenge associated with the extreme class imbalance between normal and malicious traffics. Recently, graph-neural network (GNN) achieved state-of-the-art performance to model the network topology in cybersecurity tasks. However, only a few works exist using GNNs to tac…
Tennessee Tech University（田纳西理工大学）；Benha University（本哈大学）；North Carolina A&T State University（北卡罗来纳农工州立大学）
Purveyors of malicious network attacks continue to increase the complexity and the sophistication of their techniques, and their ability to evade detection continues to improve as well. Hence, intrusion detection systems must also evolve to meet these increasingly challenging threats. Machine learning is often used to support this needed improvement. However, training a good prediction model can require a large set of labelled training data. Such datasets are difficult to obtain because privacy concerns prevent the majority of intrusion detection agencies from sharing their sensitive data. In …
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）
A significant increase in the number of interconnected devices and data communication through wireless networks has given rise to various threats, risks and security concerns. Internet of Things (IoT) applications is deployed in almost every field of daily life, including sensitive environments. The edge computing paradigm has complemented IoT applications by moving the computational processing near the data sources. Among various security models, Machine Learning (ML) based intrusion detection is the most conceivable defense mechanism to combat the anomalous behavior in edge-enabled IoT netwo…
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…
Western University（韦仕敦大学）；S2E Technologies（S2E科技公司）
Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) approaches. To accurately detect various types of attacks in IoV networks, we propose…
University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））
Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Internet of Things devices. Current approaches rely on powerful centralized servers that receive data from all their parties -- violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overhe…
King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Fayoum University（法尤姆大学）
Network intrusions have become a significant threat in recent years as a result of the increased demand of computer networks for critical systems. Intrusion detection system (IDS) has been widely deployed as a defense measure for computer networks. Features extracted from network traffic can be used as sign to detect anomalies. However with the huge amount of network traffic, collected data contains irrelevant and redundant features that affect the detection rate of the IDS, consumes high amount of system resources, and slowdown the training and testing process of the IDS. In this paper, a new…
Birla Institute of Technology, Mesra, Ranchi（比尔拉理工学院，梅斯拉，兰契）
Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations varying from smaller to larger enterprises. It is true that organizations are aware of the possible threats and attacks so they always prepare for the safer side but due to some loopholes attackers are able to make attacks. Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms for detecting intrusions. …
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…
Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is propos…
Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；NVIDIA（英伟达公司）；Penn State University（宾夕法尼亚州立大学）；Columbia University（哥伦比亚大学）；University of Missouri-Kansas City（密苏里大学堪萨斯分校）；Florida State University（佛罗里达州立大学）；Auburn University（奥本大学）
Cisco Systems, Inc.（思科系统公司）；Czech Technical University in Prague（布拉格捷克理工大学）；Catalyzer（4Catalyzer）
This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building blocks of the multi-level detection architecture. The detectors identify malicious communication exploiting encrypted URL strings and domains generated by a Domain Generation Algorithm (DGA) which are frequently used in Command and Control (C&C), phishing, and click fraud. Surprisingl…
