In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k- means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds. The proposed similarity measure satisfies all properties of a typical similarity measure.
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection
School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；Henan University（河南大学）；School of Intelligent Engineering, Zhengzhou University of Aeronautics（郑州航空工业管理学院智能工程学院）；School of Software, Henan University（河南大学软件学院）
ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
organization= Faculty of Information Technology；Electrical Engineering, University of Oulu , addressline= , city= Oulu , postcode= 90570 , state= , country= Finland
Joint Semantic Transfer Network for IoT Intrusion Detection
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Technology（北京理工大学）；University of Macau（澳门大学）
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning
School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院，哈尔滨工业大学）；School of Electronic and Information Engineering, Beijing Jiaotong University（电子与信息工程学院，北京交通大学）
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））
RES-DARE: Failure-Aware Expert Adaptation and Rollback-Safe Self-Repair for Intrusion Detection
Department of Computer Science（计算机科学系）；Jamia Hamdard（贾迈亚·哈姆达德）；Kalinga Institute of Industrial Technology（卡林加工业技术学院）；Department of Computer Science and Business Systems（计算机科学与商业系统系）；Gargi Memorial Institute of Technology（加里纪念技术学院）；Maulana Abul Kalam Azad University of Technology（穆拉纳·阿卜杜勒·卡里姆·阿扎德技术大学）；Homi Bhabha National Institute（霍米·巴哈瓦国家研究所）；Computer and Informatics Group（计算机与信息组）；Variable Energy Cyclotron Centre（变能回旋加速器中心）
SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks
Sir Syed CASE Institute of Technology（Sir Syed CASE 理工学院）；Habib University（哈比布大学）；Yeunganam University（延世南大学）；University of Nottingham Ningbo China（诺丁汉大学宁波分校）；Korea University（高丽大学）
PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT
Information Security Lab（信息安全部实验室）；University of Information Technology（信息技术大学）；Vietnam National University（越南国家大学）；VNU-HCM Information Security Center（VNU-HCM信息安全部中心）
Multi-Level Distributional Entropy from Flow Summary Statistics for Explainable Network Intrusion Detection
Centre for Intelligent Cloud Computing, CoE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University（智能云计算中心、先进云技术中心、信息科学与技术学院、多媒体大学）；Department of Communication Technology and Networking, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia（通信技术与网络系、计算机科学与信息技术学院、马来西亚国立大学）；Laboratory of Computational Science and Mathematical Physics, Institute for Mathematical Research, Universiti Putra Malaysia（计算科学与数学物理实验室、数学研究所、马来西亚国立大学）
