The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication and encryption, that are made necessary by this development. Thus, industrial IT-security is needed. In this work, emulated industrial network data is transformed into a time series and analysed with three different algorithms. The data contains labeled attacks, so the performance can be evaluated. Matrix Profiles perform well with almost no parameterisation needed. Seasonal Autoregressive Integrated Moving Average performs well in the presence of noise, requiring parameterisation effort. Long Short Term Memory-based neural networks perform mediocre while requiring a high training- and parameterisation effort.
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations
DHA Suffa University（DHA Suffa大学）；Karachi Institute of Economics and Technology（卡拉奇经济技术学院）；DNNae Inc.（DNNae公司）；Singapore University of Technology and Design（新加坡科技设计大学）
Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection
School of Computer Science, University of Nottingham, Ningbo, China（宁波大学计算机科学学院）；School of Computer Science, University of Nottingham, Nottingham, United Kingdom（诺丁汉大学计算机科学学院）
A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks
Department of Computer Science and Engineering, Jahangirnagar University（贾杭吉纳大学计算机科学与工程系）；Department of Computer Science and Engineering, Bangladesh Army University of Science & Technology（孟加拉国陆军科学技术大学计算机科学与工程系）；State University of Bangladesh, Department of Computer Science and Engineering（孟加拉国国立大学计算机科学与工程系）；Skill Morph Research Lab., Skill Morph（Skill Morph研究实验室）
CST-AFNet: A dual attention-based deep learning framework for intrusion detection in IoT networks
Skill Morph；Bangladesh Army University of Science & Technology（孟加拉国陆军科技大学）；Daffodil International University（达福迪尔国际大学）；State University of Bangladesh（孟加拉国州立大学）
Deep Neural Networks based Meta-Learning for Network Intrusion Detection
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学学院）；Air University（空军大学）；Ghulam Ishaq Khan Institute of Engineering Sciences and Technology（古拉姆·伊沙克·汗工程科学与技术研究所）；Majmaah University（迈季迈阿大学）
Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL
Faculty of Sciences, Lebanese University（科学学院，黎巴嫩大学）；DITEN, University of Genova（DITEN，热那亚大学）；IMT Atlantique, Lab-STICC UMR CNRS 6285（IMT Atlantique，Lab-STICC UMR CNRS 6285）；School of Arts and Sciences, Lebanese American University（艺术与科学学院，黎巴嫩美国大学）
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness
Mepco Schlenk Engineering College（梅普科施伦克工程学院）；Fordham University（福特汉姆大学）；University of South Florida（南佛罗里达大学）；Hung Yen University of Technology and Education（兴安工业大学）；Royal Military College of Canada（加拿大皇家军事学院）
Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset
Center for Fundamental Physics, School of Artificial Intelligence, Hubei Polytechnic University, Huangshi 435003, China（基础物理中心、人工智能学院、湖北 polytechnic 大学）；The European Higher Education Institute, St. Julian's STJ 3141, Malta（欧洲高等教育研究所）
Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights
Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；School of Cybersecurity and Privacy, Georgia Institute of Technology（佐治亚理工学院网络安全与隐私学院）；H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院H·米尔顿·斯图尔特工业与系统工程学院）
NBcoded: network attack classifiers based on Encoder and Naive Bayes model for resource limited devices
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（Vicomtech基金会，巴斯克研究与技术联盟）；Institute of Smart Cities, Public University of Navarre（纳瓦拉公立大学智慧城市研究所）
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition
King’s Communications, Learning & Information Processing (KCLIP) lab within the Centre for Intelligent Information Processing Systems (CIIPS), Department of Engineering, King’s College London（伦敦国王学院工程系智能信息处理系统中心（CIIPS）通信、学习与信息处理实验室（KCLIP））
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach
Centre for Cyber Resilience and Trust (CREST), Deakin University（网络弹性与信任中心（CREST），迪肯大学）；Department of Computer Science & IT, Latrobe University（计算机科学与信息技术系，拉筹伯大学）
