Security of information passing through the Internet is threatened by today's most advanced malware ranging from orchestrated botnets to simpler polymorphic worms. These threats, as examples of zero-day attacks, are able to change their behavior several times in the early phases of their existence to bypass the network intrusion detection systems (NIDS). In fact, even well-designed, and frequently-updated signature-based NIDS cannot detect the zero-day treats due to the lack of an adequate signature database, adaptive to intelligent attacks on the Internet. More importantly, having an NIDS, it should be tested on malicious traffic dataset that not only represents known attacks, but also can to some extent reflect the characteristics of unknown, zero-day attacks. Generating such traffic is identified in the literature as one of the main obstacles for evaluating the effectiveness of NIDS. To address these issues, we introduce RNNIDS that applies Recurrent Neural Networks (RNNs) to find complex patterns in attacks and generate similar ones. In this regard, for the first time, we demonstrate that RNNs are helpful to generate new, unseen mutants of attacks as well as synthetic signatures from the most advanced malware to improve the intrusion detection rate. Besides, to further enhance the design of an NIDS, RNNs can be employed to generate malicious datasets containing, e.g., unseen mutants of a malware. To evaluate the feasibility of our approaches, we conduct extensive experiments by incorporating publicly available datasets, where we show a considerable improvement in the detection rate of an off-the-shelf NIDS (up to 16.67%).
Due to the fourth industrial revolution, and the resulting increase in interconnectivity, industrial networks are more and more opened to publicly available networks. Apart from the huge benefit in manageability and flexibility, the openness also results in a larger attack surface for malicious adversaries. In comparison to office environments, industrial networks have very high volumes of data. In addition to that, every delay will most likely lead to loss of revenue. Hence, intrusion detection systems for industrial applications have different requirements than office-based intrusion detecti…
In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we devel…
AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent
School of Computer Science, University of Galway（戈尔韦大学计算机科学学院）；Faculty of Information Technology and Computer Science, University of Central Punjab（旁遮普中央大学信息技术与计算机科学学院）
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）
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）
This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES (Mobile- Cloud Intrusion Detection and Prevention System) combines a host-based IDS and network-based IDS using Machine Learning (ML) techniques. It applies dynamic analysis of both device resources and network traffic in order to detect ma…
The increase of cyber attacks in both the numbers and varieties in recent years demands to build a more sophisticated network intrusion detection system (NIDS). These NIDS perform better when they can monitor all the traffic traversing through the network like when being deployed on a Software-Defined Network (SDN). Because of the inability to detect zero-day attacks, signature-based NIDS which were traditionally used for detecting malicious traffic are beginning to get replaced by anomaly-based NIDS built on neural networks. However, recently it has been shown that such NIDS have their own dr…
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks
Abu Dhabi Polytechnic（阿布扎比理工学院）；College of Computer & Information Science（计算机与信息科学学院）；King Saud University（沙特国王大学）；Florida Atlantic University（佛罗里达大西洋大学）
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。
Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting
School of Information Technology, York University（约克大学信息科技学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；National Research Council of Canada（加拿大国家研究委员会）
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（电气工程与计算机科学学院（SEECS），国家 sciences and Technology（NUST））
Over the years, artificial neural networks have been applied successfully in many areas including IT security. Yet, neural networks can only process continuous input data. This is particularly challenging for security-related non-continuous data like system calls. This work focuses on four different options to preprocess sequences of system calls so that they can be processed by neural networks. These input options are based on one-hot encoding and learning word2vec or GloVe representations of system calls. As an additional option, we analyze if the mapping of system calls to their respective …
The number of cyber-attacks have substantially increased over the past decade resulting in huge organizational financial losses. Indeed, it is no longer a matter of "if" but "when" a security incident will take place. A Security Operations Center(SOC) adoption will help in the detection, identification, prevention, and resolution of issues before they end up causing extensive cyber-related damage. In this paper, our proposed framework is brought about to address the problem that current open-source SOC implementations are plagued with. These include lack of ability to be strengthened on the fl…
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）
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning
Rabah Rahal Networks and Systems Laboratory (LRS), Badji Mokhtar Annaba University, Algeria（拉比·拉尔实验室，巴吉·莫克塔尔安纳巴大学，阿尔及利亚）；Abdelaziz Amara korba Networks and Systems Laboratory (LRS), Badji Mokhtar Annaba University, Algeria（阿卜杜勒阿齐兹·阿马拉·科尔巴实验室，巴吉·莫克塔尔安纳巴大学，阿尔及利亚）；L3I, University of La Rochelle, France（L3I，拉罗歇尔大学，法国）
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）
