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machine learning for Intricate Detection in 6G Networks 2

Machine Learning for Intricate Detection in 6G Networks

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2nd IEEE international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Tiwari, Annapurna Nand Revathi, N. Almusawi, Muntather Prabha, Sakthi Karpagam, J. Hafeena, Md. IES College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Technology T.M Department of Electronics and Communication Engineering Tamil Nadu Palayam India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Academy of Higher Education Department of Electrical and Electronics Engineering Karpagam TN India CMR Technical Campus Medchal Hyderabad India

ISBN: (纸本)9798350368109

In the world of 6G networks, the merging of micro-services-oriented designing and intelligent network management demonstrates the central role of neural networks (AI), machine learning (ML), and supervised learning (DL). This study looks into strengthening the security of 6G communications networks through the development of a new anomaly recognition System for 6G Connections (AD6GNs) utilizing ensemble learning (EL). The EL-ADCN workflow comprises prior processing, feature selection utilizing the hybrid methodology CFS-RF, and intruder detection applying hybrid EL methods. The system shows impressive performance on datasets with names like NSL-KDD, UNSW-NB2015, CIC-IDS2017, when CICDDOS2019, getting accuracy of 99.5%, 99.9%, 99.8%, while 99.95426%, respectively, with low false alarm rates. The combination of assistance vector machines (SVM) particularly random forest computing (RF) as EL techniques further improves the efficiency of packing and adaboosting in guaranteeing network that self-sustainability and preventive threat prevention in the envisioned 6G model. © 2024 IEEE.

关键词： Network intrusion

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Performance Analysis of Ensemble Feature Selection based Network Intrusion Detection System using Ensemble machine learning 4

Performance Analysis of Ensemble Feature Selection based Net...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Upadhyay, Lav Tripathi, Meenakshi Grover, Jyoti MNIT Department of CSE Jaipur India

ISBN: (纸本)9798350353266

The proliferation of internet-connected devices, soft-ware, and protocols results in extremely heterogeneous data, and both the amount of traffic and the disparity between different types of data are only going to get worse. The vast amount of data flow necessitates a detection method that can distinguish between different types of attacks. In a dynamic world, the attack detection system would work reliably even when data distribution is imbalanced. Ensemble feature selection was used to handle high-dimensional data. This work proposes an ensemble feature selection method and an ensemble method for training the data to improve intrusion detection on unbalanced data. Combining multiple classification algorithms, such as GNB, LR, DT, SVC, and RF, provides the ideal detection methodology based on ten selected features. Since the CICIDS-2017 dataset has been employed and is commonly used in IDS research, it was chosen for this investigation and performed well with an accuracy of 88%. © 2024 IEEE.

关键词： Network intrusion

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Design of a Secure machine learning-Based Malware Detection and Analysis Scheme

Design of a Secure Machine Learning-Based Malware Detection ...

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2023 IEEE international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2023

作者： Kumar, Pankaj Obaidat, Mohammad S. Pandey, Piyush Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computing Srm University Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Algorithmic Research International Institute of Information Technology Center for Security Theory Hyderabad India

ISBN: (纸本)9798350302561

Today's world is rapidly growing towards the advanced concept of technology. Technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score. © 2023 IEEE.

关键词： machine learning

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Performance Analysis of a Stacking Ensemble machine learning Model in Forecasting University-industry Collaboration 7

Performance Analysis of a Stacking Ensemble Machine Learning...

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7th international conference on Data Science and Information Technology, DSIT 2024

作者： Hange, Uzapi Nwanesi, Ezenwa Chike Bah, Monhesea Obrey Patrick Wuhan University of Technology School of Management Wuhan China Yanshan University School of Economics and Management Qinhuangdao China

ISBN: (纸本)9798350384093

University-industry collaboration has become a pivotal strategy for fostering innovation and economic growth. However, these partnerships are often fraught with high failure rates. Early forecasting of university-industry collaboration performance informs national strategies aimed at improving their success. Recently, standalone machine learning algorithms have been gaining attention for predicting the performance of these collaborations. However, their efficacy in predicting university-industry collaboration performance varies. In this paper, a new approach to forecasting university-industry collaboration using a stacking ensemble model is proposed. Experiments were conducted based on four different machine learning algorithms;random forest, support vector machines, multilayer-perceptron network, and linear regression. These algorithms were used to train the ensemble model for forecasting university-industry performance based on human capital factors. The study utilized a dataset extracted from the global innovation index from 2013 to 2017, containing 639 instances, and 14 attributes. A comparative analysis between the standalone models and the stacking ensemble model revealed that the stacking ensemble model achieved the most promising results with the highest correlation coefficient of 90% and coefficient of determination 81%. The stacking model also obtained the lowest mean absolute error of 4.73 and root mean square error of 6.35. Therefore, we conclude that the proposed approach is superior in predicting university-industry collaboration performance compared to standalone models. The efficacy of stacking ensemble modeling in real-life university-industry collaboration prediction is therefore justified. Drawing upon feature selection, the study also offers valuable insights aimed at improving decision making. Thus, fostering successful university-industry collaborations in many nations. © 2024 IEEE.

关键词： Prediction models

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A Novel Deep learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks 10

A Novel Deep Learning based Model to Defend Network Intrusio...

引用

17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Roshan, Khushnaseeb Zafar, Aasim Ul Haque, Shiekh Burhan Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9789380544472

Network Intrusion Detection System (NIDS) is an essential tool in securing cyberspace from a variety of security risks and unknown cyberattacks. A number of solutions have been implemented for machine learning (ML), and Deep learning (DL) based NIDS. However, all these solutions are vulnerable to adversarial attacks, in which the malicious actor tries to evade or fool the model by injecting adversarial perturbed examples into the system. The main aim of this research work is to study powerful adversarial attack algorithms and their defence method on DL-based NIDS. Fast Gradient Sign Method (FGSM), Jacobian Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini & Wagner (C&W) are four powerful adversarial attack methods implemented against the NIDS. As a defence method, Adversarial Training is used to increase the robustness of the NIDS model. The results are summarized in three phases, i.e., 1) before the adversarial attack, 2) after the adversarial attack, and 3) after the adversarial defence. The Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset is used for evaluation purposes with various performance measurements like fl-score, accuracy etc. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Deep neural networks

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Design and Implementation of a machine learning-Based Network Intrusion Detection System 24

Design and Implementation of a Machine Learning-Based Networ...

引用

2024 Asia Pacific conference on computing Technologies, Communications and Networking, CTCNet 2024

作者： Lisong, Shao Peng, Wang Weitao, Wang Lei, Lei Jiale, Chang Beijing KeDong Electric Power Control System Co. Ltd China China Electric Power Research Institute Beijing China Nari Group Corporation State Grid Electric Power Research Institute China

ISBN: (纸本)9798400709609

In today’s highly interconnected world, network security is a critical challenge for enterprises when storing and transmitting sensitive information. Traditional security measures are increasingly inadequate against the ever-growing complexity of network attacks. This paper aims to design and implement a machine learning-based Network Intrusion Detection System (IDS) to enhance the accuracy and defense capability against network attacks. Using the CICIDS2017 dataset, the study employs Stacked Autoencoders (SAE) for data dimensionality reduction and feature extraction, followed by intrusion detection utilizing the Random Forest (RF) classification algorithm. Experimental results demonstrate that the system excels in detecting various types of network attacks, particularly DDoS, Brute Force, and Botnet attacks, achieving an accuracy of 98.5%, with precision and recall rates both exceeding 97%. The system incorporates a feedback mechanism, enabling adaptive adjustments to the ever-changing network environment, thereby providing robust security assurance. The findings validate that the proposed method significantly improves the accuracy and robustness of intrusion detection. © 2024 Copyright held by the owner/author(s).

关键词： Network intrusion

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learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels 59

Learning for Semantic Knowledge Base-Guided Online Feature T...

引用

59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Gao, Xiangyu Sun, Yaping Wei, Dongyu Xu, Xiaodong Chen, Hao Yin, Hao Cui, Shuguang Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA Peng Cheng Lab Dept Broadband Commun Shenzhen 518000 Peoples R China Chinese Univ Hong Kong Shenzhen Future Network Intelligent Inst FNii Shenzhen 518172 Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn SSE Shenzhen 518172 Peoples R China

ISBN: (纸本)9781728190549

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.

关键词： Semantic knowledge base online optimization deep reinforcement learning remote object recognition

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machine learning-Based Framework for Cybersecurity of Robotic System 6th

Machine Learning-Based Framework for Cybersecurity of Roboti...

引用

6th international conference on Recent Innovations in computing, ICRIC 2023

作者： Verma, Narinder Kumar, Neerendra Salotra, Harish Department of Computer Science and Information Technology Central University of Jammu Jammu181143 India

ISBN: (纸本)9789819778614

Robotic systems are increasingly integrated into sectors ranging from manufacturing to health care, presenting significant cybersecurity challenges. Ensuring the security and integrity of these systems is critical to prevent malicious attacks that could compromise functionality and safety. This study introduces a machine learning-based framework designed to enhance the cybersecurity of industrial robotic systems. Leveraging the CICIDS 2017 dataset, which provides a comprehensive collection of network traffic data representing modern cyber threats, the framework demonstrates high efficacy in detecting and mitigating such threats. Experimental results show that the K-Nearest Neighbors and Decision Tree algorithms achieve accuracy rates of 99.01% and 99.83%, respectively, underscoring the potential of these techniques for real-time intrusion detection. These findings highlight the framework's effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. The findings highlight the framework effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyber attacks

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Blockchain and machine learning Security for the Internet of Things: A Bibliometric Analysis 3

Blockchain and Machine Learning Security for the Internet of...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Mudhar, Jaideep Kaur Malhotra, Jyoteesh Singh, Ajay Kumar Anand, Monika Guru Nanak Dev University Computer Engineering Amritsar India Guru Nanak Dev University Electronics Engineering Regional Campus Jalandhar India Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9798350322842

The Internet of Things has developed as a catalyst for innovation across numerous industries, from home automation to smart cities to 4IR primarily Industry 4.0. Through the internet, IoT devices interact and exchange information. Beyond its potential for development, IoT devices are divergent, ubiquitous, memory-and computational-power-constrained, making them vulnerable to a range of security and privacy risks. Blockchain, a decentralised and tamper-proof technology, has recently been introduced in the BIoT domain for securing data handling activities and inculcating trusted transactions. In anticipation of IoT System vulnerabilities, machine learning (ML) algorithms yield accurate findings for large and complicated databases. This study conducts a bibliometric study from the Web of Science (WoS) database from 2017 to 2022 in the area of ML-based BIoT security using Prisma guidelines. Rankings of the most productive and influential writers, articles, and sources are based on total publications and citations. Text data content analysis, Author keywords, index keywords are utilized to highlight research hotspots and future trends in the ML-Based BIoT security domain. © 2023 IEEE.

关键词： Blockchain

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Detection of XSS Attack and Defense of REST Web Service machine learning Perspective 21

Detection of XSS Attack and Defense of REST Web Service Mach...

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5th international conference on machine learning and soft computing, icmlsc 2021

作者： Ivanova, Malinka Rozeva, Anna Technical University of Sofia College of Energy and Electronics Bulgaria

ISBN: (纸本)9781450387613

The paper presents a machine learning approach for detection of stored XSS attack and for defense of REST web service. For this purpose, a XML-based REST web service is developed in JAVA, which is tested and attacked in specially created test-bed simulation environment, consisting of IntelliJ IDEA environment, Postman and web browser. The obtained data sets are processed resulting in the selection of 30 out of 171 features for further treatment. Supervised machine learning classifiers: Random Forest, Random Tree, Decision Tree and Gradient Boosted Tree are used for the detection of known attacks and clustering algorithm k-Means for the identification of unknown threats. The efficiency of implementing machine learning algorithms is evaluated and the results confirm their high accuracy. In addition fuzzy sets and fuzzy logic theory is utilized for solving multi-criteria task in support of decision making for web service defense. © 2021 ACM.

关键词： Web services

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