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Trust-based Approach to Secure Low-Power and Lossy networks Routing Protocol 5

Trust-based Approach to Secure Low-Power and Lossy Networks ...

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5th IEEE international conference on Networking and Advanced systems, ICNAS 2021

作者： Abid, Meriem Bahloul, Sarah Nait Hamlili, Sara University of Mostaganem Dept. of Mathematics and Computer Science Algeria University of USTO-MB Dept. of Computer Science Oran Algeria

ISBN: (纸本)9781665428217

Resource-constrained things can now connect to the Internet through Wireless Personal Area networks (WPAN) and Low Power Wide Area networks (LPWAN). Since then, they are vulnerable to both attacks from inside the network and from the Internet as well. Many research has already investigated the security threats of such an environment. In this article, we focus on how securing the RPL routing protocol in such a constrained and vulnerable *** tackle these issues, we investigate how trust that is inspired by social human behavior can enhance RPL security. Our trust-based solution enables motes evaluating locally the threat level when interacting with their neighbors. However, evaluating the trust in such environment is a challenge due to various factors to take into account and the motes resource *** this end, we propose a distributed trust management approach based on machine learning to evaluate trust and detect untrustworthy behaviors. Machine learning-based approach have been widely investigated in IoT and *** evaluate the performance through significant metrics so as to obtain a good assessment of the effectiveness of our method. © 2021 IEEE.

关键词： Routing protocols

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An Efficient DDoS Attacks Detection and Mitigation Approach in SDN-IoT Network 6

An Efficient DDoS Attacks Detection and Mitigation Approach ...

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6th international conference on Futuristic Trends in networks and Computing Technologies, FTNCT 2024

作者： Singh, Chandrapal Jain, Ankit Kumar Department of Computer Engineering National Institute of Technology Kurukshetra India

The Internet of Things (IoT) has revolutionized modern life by introducing advanced solutions and enhancing processing power. However, managing IoT networks can be challenging due to the multitude of devices and communication protocols involved. Software-Defined Networking (SDN) offers a potential solution by simplifying network management, providing network abstraction, facilitating network development, and potentially addressing the complexities of IoT networks. Despite the numerous benefits, it is important to note that IoT systems still face significant security concerns. This study uses entropy as a metric on the SDN controller in an IoT network to assess the homogeneity of network traffic. And on the SDN controller network log data is present so to filter network traffic, the technique additionally incorporates machine learning (ML) classifiers. We tested the suggested methodology's effectiveness using real-world network traffic information. SDN-IoT networks and ML are used to improve the process of detection and mitigation. The proposed method has a detection rate between 98% and 100%, demonstrating its exceptional performance. Compared to existing methods, our approach not only detects distributed Denial of Service (DDoS) attacks with remarkable accuracy, but also enables early detection while maintaining a remarkable false-positive rate. This combination of high precision and early detection enables us to effectively identify and mitigate DDoS attacks, thereby enhancing security and minimizing the potential harm caused by malicious activities. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Internet of things

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Performance Comparison of Machine Learning Classifiers for DDOS Detection and Mitigation on Software Defined networks

Performance Comparison of Machine Learning Classifiers for D...

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IEEE international conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Babangida Isyaku Kamalrulnizam Bin Abu Bakar Muhammad Salisu Ali Muhammed Nura Yusuf Department of Computer Science Universiti Teknologi Malaysia Johor Malaysia Sule Lamido University K/Hausa P.M.B. 048 Kafin Hausa Jigawa State Nigeria Department of Cyber Security Federal University Dutse Ibrahim Aliyu By-Pass P.M.B. 7156 Dutse Jigawa State

Software Defined networks (SDN) is an emerging network with better network management through the separation of Control logic and data forwarding elements. Several emerging networks, including the Internet of Things, Wireless Body Area networks, and Blockchain, are incorporating SDN technology to improve resource management, thereby speeding up network innovation. The increasing number of internet-connected devices and the growing number of online applications pose various security concerns. SDN suffered various security threats due to centralized network architecture and limited memory space in the switch Flowtable. distributed Denial of Service (DDOS) attacks is among the severe security threats that flood the precious switch Flowtable with massive flows to hijack the network. Several machine-learning DDOS attack detection has been proposed to mitigate such threats. However, the choice of effective machine learning algorithms with high accuracy and short prediction and learning time is paramount. This study analyses the performance of eight machine-learning algorithms for DDOS detection and mitigation in SDN. On average, Decision Tree (DT) and Random Forest have the highest accuracy with 99.86%, respectively. Naive Bayes has a minimal prediction time of 144.511 seconds, while DT has the shortest learning time of 22229 seconds.

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Perception Poisoning Attacks in Federated Learning 3

Perception Poisoning Attacks in Federated Learning

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3rd EEE international conference on Trust, Privacy and security in Intelligent systems and Applications (TPS-ISA)

作者： Chow, Ka-Ho Liu, Ling Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA

ISBN: (纸本)9781665416238

Federated learning (FL) enables decentralized training of deep neural networks (DNNs) for object detection over a distributed population of clients. It allows edge clients to keep their data local and only share parameter updates with a federated server. However, the distributed nature of FL also opens doors to new threats. In this paper, we present targeted perception poisoning attacks against federated object detection learning in which a subset of malicious clients seeks to poison the federated training of a global object detection model by sharing perception-poisoned local model parameters. We first introduce three targeted perception poisoning attacks, which have severe adverse effects only on the objects under attack. We then analyze the attack feasibility, the impact of malicious client availability, and attack timing. To safeguard FL systems against such contagious threats, we introduce spatial signature analysis as a defense to separate benign local model parameters from poisoned local model contributions, identify malicious clients, and eliminate their impact on the federated training. Extensive experiments on object detection benchmark datasets validate that the defense-empowered federated object detection learning can improve the robustness against all three types of perception poisoning attacks. The source code is available at https://***/git-disl/Perception-Poisoning.

关键词： federated learning object detection data poisoning deep neural networks

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Workshop held at the 12th international conference on Model and Data Engineering, MEDI 2023

Workshop held at the 12th International Conference on Model ...

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Workshop held at the 12th international conference on Model and Data Engineering, MEDI 2023

ISBN: (纸本)9783031557286

The proceedings contain 18 papers. The special focus in this conference is on Model and Data Engineering. The topics include: Towards an Open Domain Arabic Question Answering System: Assessment of the Bert Approach;Multi-lingual Scene Text Detection Containing the Arabic Scripts Using an Optimal then Enhanced YOLO Model;transfer Learning Model for Cyberbullying Detection in Tunisian Social networks;node2Vec Stability: Preliminary Study to Ensure the Compatibility of Embeddings with Incremental Data Alignment;towards Enabling Domain-Specific Modeling Language Exchange Between Modeling Tools;the Power of Prognosis: Cox Model Prediction of Disease-Free Survival in Colon Cancer;transfer Learning in Segmenting Myocardium Perfusion Images;smart Saliency Detection for Prosthetic Vision;automatic Detection of Multiple Sclerosis Using Genomic Expression;distributed and Collaborative Learning Approach for Stroke Prediction;ioT Technologies for Smart Healthcare Buildings with distributed Deep Learning Techniques;How Does Blockchain Enhance Zero Trust security in IoMT?;HIPAA and GDPR Compliance in IoT Healthcare systems;a Secure IoT Architecture for Industry 4.0;From Functional Requirements to NoSQL Database Models: Application to IoT Geospatial Data;a Survey on Intrusion Detection systems for IoT networks Based on Long Short-Term Memory.

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LiteCrypt: Enhancing IoMT security with Optimized HE and Lightweight Dual-Authorization

LiteCrypt: Enhancing IoMT Security with Optimized HE and Lig...

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international conference on Parallel and distributed systems (ICPADS)

作者： Qipeng Xie Weizheng Wang Yongzhi Huang Mengyao Zheng Shuai Shang Linshan Jiang Salabat Khan Kaishun Wu IoT Thrust Hong Kong University of Science and Technology (Guang Zhou) Guang Zhou China Hong Kong University of Science and Technology Hong Kong China Computer Science Engineer City University of Hong Kong Hong Kong China DSA Thrust Information Hub Hong Kong University of Science and Technology (Guang Zhou) Guang Zhou China Harvard T.H. Chan School of Public Health Boston United States University of Electronic Science and Technology of China Chengdu Chengdu China Institute of Data Science National University of Singapore Singapore

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

The integration of 5G/6G networks with intelligent healthcare systems has enabled early disease detection through patient data monitoring. However, the Internet of Medical Things (IoMT) and remote healthcare services introduce significant privacy and security risks. In this paper, we propose LiteCrypt, which addresses these challenges by introducing an optimized Homomorphic Convolutional Neural networks (HCNN) structure for secure inference and a lightweight Threshold Signature Scheme (TSS) based dual-authorization mechanism. To enhance the practicality of Homomorphic Encryption (HE)-based secure inference in telemedicine applications, LiteCrypt presents an optimized HCNN framework that ensures efficient and adaptable operations across multiple datasets. A high-performance GPU-accelerated HE engine is developed to address the computational demands of HE operations, enabling real-time processing of encrypted patient data. Besides, LiteCrypt introduces a novel TSS-based dual-authorization protocol, requiring consent from both the patient and the hospital to access patient data, thereby mitigating unauthorized access risks. The system adapts to a flexible 2-out-of-3 authorization scheme for emergencies, ensuring timely data retrieval while maintaining security. To overcome the initial challenge of prolonged computation time due to compute-intensive operations, In LiteCrypt, we utilized the lightweight TSS protocol, based on Oblivious Transfer (OT), which is designed for resource-constrained IoMT devices, reducing computation time from 11.9 to 0.11 seconds. Empirical validation demonstrates LiteCrypt’s superior performance, achieving a 233-fold increase in processing speed, a $96 \%$ reduction in encrypted message size, and a 28-fold speed increase using GPUs.

关键词： Performance evaluation Privacy Protocols Telemedicine Memory management Performance gain Real-time systems Cryptography Homomorphic encryption Monitoring

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A Regression Model to Secure Cloud Datacenter Against DDoS Attack

A Regression Model to Secure Cloud Datacenter Against DDoS A...

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international Symposium on High Capacity Optical networks and Enabling Technologies, HONET

作者： Hamad Al-Thani Jarah Al-Otaibi Saad Al-Homaidan Shafi'i M. Abdulhamid Nadin Rana Abdullah H. Al-Ghushami Department of Information Technology Community College of Qatar Lusail Qatar Department of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Cloud computing is a major area for conventional IT executions since it offers insignificant expense and admittance to “pay-as-you-go” registering capacities and concerning requests by clients. The cloud offers systems, stages, and software through cloud climate models. There are numerous security-related issues in this domain. A distributed denial of service (DDoS) attack can be named a “ tsunami ” due to its devastating effect. DDoS attacks are a ceaseless danger to present-day organizations, causing serious business fiascos, client issues, and monetary misfortunes. This study utilizes a machine learning strategy named the Logistic Regression model to give security to servers working in a cloud climate against DDoS. This assists with giving security and is valuable in updating the strong advancement of associations. WEKA tool is used to develop a regression model. The data set used is NSL-KDD. The model helped categorize traffic as normal or anomaly with high accuracy and precision.

关键词： Cloud computing Smart cities Denial-of-service attack Tsunami Software security Servers computer crime Robots Meteorology

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security and Performance Comparison using QR Code through Blockchain Technology 4

Security and Performance Comparison using QR Code through Bl...

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4th international conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Dash, Sonali S Rajasekar, V. AMC Engineering College Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456487

Advances and transitions in healthcare system is constantly changing and acquiring fresh forms. The requirement of modern smart health systems is to protect sensitive patient data from attacks by potential adversaries. The secure data access mechanism ensures that only authorized personnel can access patient's health information. Therefore, in distributed method, it protects data in the healthcare system. Here it provides patients and doctors with an efficient and secure access mechanism based on blockchain data collection. In this health system the proposed system can also protect the privacy of patients. security analysis depends upon architecture to prevent from attacks and maintain integrity of system. In addition, the implementation of system based Ethereum used for feasibility test of proposed system. By hiding private information in the coverage environment in a way that a third party cannot discover this confidential information. In today's era, with the emergence of new technologies, QR codes have become one of the most popular codes. Provide mechanisms for protecting sensitive information QR codes are the most reliable method of hiding data because they do not use error correction levels and because it is difficult to change the encoded information. The process is divided into different categories, and various statistical parameters are used to evaluate its performance. © 2022 IEEE.

关键词： Blockchain

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An Efficient Multi-Layer Indexing Method on Blockchain for Multimodal Data Querying

An Efficient Multi-Layer Indexing Method on Blockchain for M...

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IEEE international conference on systems, Man and Cybernetics

作者： Haoyu Jia Xiaoming Wu Shanshan Liu Qile Yuan Bei Qi Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the digital society era, the generation frequency of multimodal data is rapidly increasing. Blockchain, recognized as a trusted distributed database technology, provides a new solution for the trustworthy storage and efficient management of multimodal data. However, blockchain systems support only query that use transaction hash values as keywords and cannot directly leverage the content features of multimodal data, leading to generally low query efficiency. To address this issue, this paper introduces an efficient multi-layer indexing method on blockchain for multimodal data querying. It establishes an effective mapping between on-chain and off-chain data through a verifiable on-chain and off-chain collaborative storage architecture. The paper also proposes Multi-layer Bitmap Block Index (MBBI) and Cuckoo Merkel Tree (C-MT) to optimize the querying process. Experimental results demonstrate that this method not only ensures the consistency and integrity of metadata across on-chain and off-chain but also significantly enhances the efficiency of multimodal data query. This offers a feasible solution for the storage and query needs of large-scale multimodal data.

关键词： Collaboration Metadata Data retrieval Throughput Data structures Blockchains systems support Cybernetics Indexing

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Adversarial Attack and Defense for Transductive Support Vector Machine

Adversarial Attack and Defense for Transductive Support Vect...

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international Joint conference on Neural networks (IJCNN)

作者： Li Liu Haiyan Chen Changchun Yin Liming Fang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial Defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.

关键词： Training Support vector machines Accuracy Machine learning algorithms Neural networks Robustness Vectors

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