Network Anomaly Detection Based On Late Fusion Of Several Machine Learning Algorithms
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Network Anomaly Detection Based on Late Fusion of Several Machine Learning Algorithms
Today's Internet and enterprise networks are so popular as they can easily provide multimedia and ecommerce services to millions of users over the Internet in our daily lives. Since then, security has been a challenging problem in the Internet's world. That issue is called Cyberwar, in which attackers can aim or raise Distributed Denial of Service (DDoS) to others to take down the operation of enterprises Intranet. Therefore, the need of applying an Intrusion Detection System (IDS) is very important to enterprise networks. In this paper, we propose a smarter solution to detect network anomalies in Cyberwar using Stacking techniques in which we apply three popular machine learning models: k-nearest neighbor algorithm (KNN), Adaptive Boosting (AdaBoost), and Random Decision Forests (RandomForest). Our proposed scheme uses the Logistic Regression method to automatically search for better parameters to the Stacking model. We do the performance evaluation of our proposed scheme on the latest data set NSLKDD 2019 dataset. We also compare the achieved results with individual machine learning models to show that our proposed model achieves much higher accuracy than previous works.
Proceedings of International Conference on Communication and Computational Technologies
This book gathers selected papers presented at 6th International Conference on Communication and Computational Technologies (ICCCT 2024), jointly organized by Soft Computing Research Society (SCRS) and Rajasthan Institute of Engineering & Technology (RIET), Jaipur, during January 8–9, 2024. The book is a collection of state-of-the art research work in the cutting-edge technologies related to the communication and intelligent systems. The topics covered are algorithms and applications of intelligent systems, informatics and applications, and communication and control systems.
AI-Driven Policing and Urban Security in Smart Cities
Author: Abdelmottlep, Mamdooh Abdelhameed
language: en
Publisher: IGI Global
Release Date: 2025-06-24
As cities increasingly adopt smart technologies to enhance public services, AI has emerged as a transformative force in urban security. AI systems like predictive analytics and real-time surveillance have reshaped how law enforcement monitors and responds to crime. This integration of AI into policing practices promises improved efficiency, faster response times, and data-informed decision-making. However, it also raises important ethical, legal, and privacy concerns that must be addressed to ensure equitable and transparent implementation. AI-Driven Policing and Urban Security in Smart Cities explores the way AI has transformed the way police have monitored crime. This book explores research with AI systems in law and government. Covering topics such as AI, policing, and government, this book is an excellent resource for law enforcement, city planners, policymakers, researchers, academicians, and more.