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Real-Time Adaptive Intrusion Detection System [RTPIDS] for Internet of Things Using Federated Learning and Blockchain

Real-Time Adaptive Intrusion Detection System [RTPIDS] for I...

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Data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： Sunil Raj Y E. Helen Parimala V. S Jayakumar Paul Bosco Adarsh Samuel Raj J P. J. Rosario Vasantha Kumar Vinothkumar Kolluru Department of Data Science St. Xavier’s College (Autonomous) India Department of Computer Science Gitam Deemed to be University Bangalore India Department of BCA Sacred Heart College (Autonomous) Tirupattur India San Jose State University San Jose California USA Librarian St. Xavier’s College (Autonomous) India Department of Data Science Stevens Institute of Technology NJ USA

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

In the growing field of the Internet of Things (IoT), ensuring security and privacy has become a critical concern, particularly in sensitive domains such as healthcare. This paper presents a Real-Time Adaptive Intrusion Detection System (IDS) specifically designed for Internet of Medical Things (IoMT) networks. The proposed system leverages Federated Learning (FL) and Blockchain technologies to address key challenges, including handling non-independent and identically distributed (non-IID) data, ensuring secure model updates, and providing real-time detection capabilities. By employing FL, the system enables IoMT devices to collaboratively train models without sharing sensitive data, thereby preserving privacy and improving scalability. Blockchain integration further enhances the system by ensuring the integrity and security of model updates, mitigating risks of tampering. The system demonstrates strong performance in detecting simpler intrusions in real-time, though additional enhancements are required to manage more complex attacks and large-scale IoT deployments. This work contributes a robust and scalable IDS framework, particularly valuable for protecting critical IoT environments such as healthcare networks.

关键词： Performance evaluation Privacy Adaptive systems Federated learning Scalability Intrusion detection Real-time systems Data models Blockchains security

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Securing Smart City networks - Intelligent Detection of DDoS Cyber Attacks 5

Securing Smart City Networks - Intelligent Detection of DDoS...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Anitha, D. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9798350398267

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed. © 2022 IEEE.

关键词： Network security

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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international conference on Parallel and distributed systems (ICPADS)

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity.

关键词： Accuracy Computational modeling Memory management Neural networks Feature extraction Transformers Sensors Data mining Computational complexity Remote sensing

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Enhanced DDoS Defense in SDN: Double-Layered Strategy with Blockchain Integration

Enhanced DDoS Defense in SDN: Double-Layered Strategy with B...

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international conference on Communications, Circuits and systems (ICCCAS)

作者： Jialin Tian Zhaogang Shu Shuwu Chen Haihui Xie Xiaolong Liu Caiyu Qiu The Computer and Information College Fujian Agriculture And Forestry University Fuzhou China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288

With the development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) has emerged as a novel network architecture in today's Internet era. It can separate the control plane from the data plane, allowing rapid packet forwarding in the Internet through a centralized controller. However, SDN environments are vulnerable to traditional distributed Denial of Service (DDoS) attacks. This paper proposes a new dual layer strategy to try to mitigate the question. First, by using blockchain technology and smart contract in the northbound interface to store the flow tables required for SDN networks, security is increased. Then, we use the Token Bucket algorithm and Time Window algorithm to build the first-tier strategy to defend against obvious DDoS attacks. To detect unobvious DDoS attacks, we design the second-tier strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm to perform binary classification on data, thereby identifying abnormal traffic. We use the currently publicly available DDoS dataset CIC-DDoS2019 for experimental verification. The results show that using this strategy in SDN networks results in an average deviation of data Round-Rip Time (RTT) approximately 38.86% lower than in the original SDN networks without this strategy. Additionally, the accuracy of DDoS attack identification reaches 91.29%. This means that with the implementation of this strategy, DDoS attacks can be effectively identified without compromising the stability of data transmission in SDN network environments.

关键词： Machine learning algorithms Accuracy Smart contracts Network security Denial-of-service attack Feature extraction Classification algorithms

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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization

A Parallelizable Counterfactual Generation Method Based on G...

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international conference on Parallel and distributed systems (ICPADS)

作者： Haorun Ding Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models.

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EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle via Edge Devices

EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle...

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international conference on Computing and Networking Technology (ICCNT)

作者： V Arunodhai Leena P Susan Jinesh M. Kannimoola Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Internet of Things (IoT) revolutionizes technology interaction by enabling seamless connectivity and automation, but it introduces significant security challenges. A distributed ledger technology (DLT) called IOTA Tangle, a utilizing a Directed Acyclic Graph (DAG), enables secure and feeless transactions among IoT devices. Despite its advantages, the IOTA Tangle faces threats such as replay attacks and double spending, which highlight vulnerabilities in distributed ledger technologies. To address these security concerns, we initially developed EdgeGuard, a rule-based system for spam detection within the IOTA ecosystem. However, its reliance on predetermined rulesets limited its adaptability. We have now enhanced our approach with EdgeShield, incorporating a neural network for superior threat *** enhances adaptability over EdgeGuard in threat detection for the IOTA Tangle by integrating machine learning. EdgeShield analyzes network packets to identify threats, leveraging machine learning to adapt to evolving risks. Trained using the MQTTSet dataset, it detects network-based threats such as denial-of-service (DoS) attacks and packet flooding. Tested with simulated IoT data, EdgeShield demonstrates effectiveness across various domains, particularly in healthcare IoT. Its decentralized processing on edge devices like the Raspberry Pi 4 enables immediate network traffic analysis, reducing reliance on centralized servers and improving system resilience. Additionally, real-time alerts empower administrators to proactively secure IoT deployments and maintain transaction integrity within the IOTA Tangle. This paper traces our research evolution from EdgeGuard to EdgeShield, highlighting advancements and demonstrating the enhanced security of IoT transactions on the IOTA Tangle.

关键词： distributed ledger Image edge detection Neural networks Machine learning Telecommunication traffic Real-time systems Threat assessment Internet of Things security Servers

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Depth Analysis on Recognition and Prevention of DDoS Attacks via Machine Learning and Deep Learning Strategies in SDN

Depth Analysis on Recognition and Prevention of DDoS Attacks...

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Self Sustainable Artificial Intelligence systems (ICSSAS), international conference on

作者： Pranay Yadav Nishchol Mishra Sanjeev Sharma School of Information Technology (SoIT) Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal M.P INDIA

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Recent years have witnessed the exponential growth of global digitization. Similarly, the rise in distributed Denial of Service (DDoS) attacks continues unabated. DDoS is a significant threat to network security in the flexible environments of software-defined networks (SDN). This research discusses the study of the advanced characteristics of machine learning and deep learning technologies to detect and prevent DDoS attacks in SDNs. In this article, we closely examine the design of SDNs and the unique challenges they face during DDoS attacks. This research study analyzes various deep learning and machine learning models designed to respond to complex DDoS attack patterns and respond instantly to attacks. Different datasets related to attacks in SDN and IoT are discussed in the research article. Additionally, a comparative study of these methods are presented.

关键词： Deep learning Surveys Adaptation models Technological innovation Scalability Switches Denial-of-service attack Data models computer crime Software defined networking

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Shared-Dining: Broadcasting Secret Shares Using Dining-Cryptographers Groups 21st

Shared-Dining: Broadcasting Secret Shares Using Dining-Crypt...

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21st IFIP WG 6.1 international conference on distributed Applications and Interoperable systems (DAIS) Held as Part of the 16th international Federated conference on distributed Computing Techniques (DisCoTec)

作者： Moedinger, David Dispan, Juri Hauck, Franz J. Ulm Univ Inst Distributed Syst D-89081 Ulm Germany

ISBN: (纸本)9783030781972;9783030781989

We introduce a combination of Shamir's secret sharing and dining-cryptographers networks, which provides (n - vertical bar attackers vertical bar)-anonymity for up to k - 1 attackers and has manageable performance impact on dissemination. A k-1 anonymous broadcast can be implemented using a small group of dining cryptographers to first share the message, followed by a flooding phase started by group members. Members have little incentive to forward the message in a timely manner, as forwarding incurs costs, or they may even profit from keeping the message. In worst case, this leaves the true originator as the only sender, rendering the dining-cryptographers phase useless and compromising their privacy. We present a novel approach using a modified dining-cryptographers protocol to distributed shares of an (n, k)-Shamir's secret sharing scheme. All group members broadcast their received share through the network, allowing any recipient of k shares to reconstruct the message, enforcing anonymity. If less than k group members broadcast their shares, the message cannot be decoded thus preventing privacy breaches for the originator. We demonstrate the privacy and performance results in a security analysis and performance evaluation based on a proof-of-concept prototype. Throughput rates between 10 and 100 kB/s are enough for many real applications with high privacy requirements, e.g., financial blockchain system.

关键词： Network protocol Privacy protocol Dining cryptographers Secret sharing Peer-to-Peer networking

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OpenAuth: Human Body-Based User Authentication Using mmWave Signals in Open-World Scenarios

OpenAuth: Human Body-Based User Authentication Using mmWave ...

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international conference on distributed Computing systems

作者： Junlin Yang Jiadi Yu Linghe Kong Yanmin Zhu Hong-Ning Dai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science Hong Kong Baptist University Hong Kong China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

User authentication is evolving with expanded application scenarios and innovative techniques. New authentication approaches utilize RF signals to sense specific human behaviors and characteristics, such as faces, specific gestures, etc., offering a contactless and nonintrusive solution. However, these RF signal-based methods struggle with challenges in open-world scenarios, i.e., dynamic environments, daily behaviors with unrestricted postures, and identification of unauthorized users with security threats. In this paper, we present an open-world user authentication system, OpenAuth, which leverages a commercial off-the-shelf (COTS) mmWave radar to sense unrestricted human postures and behaviors for identifying individuals. First, OpenAuth utilizes a MUSIC-based neural network imaging model to eliminate environmental clutter and generates environment-independent human silhouette images. Then, the human silhouette images are normalized to consistent topological structures of human postures, ensuring robustness against unrestricted human postures. Based on the environment-independent and posture-independent human silhouette images, OpenAuth further extracts fine-grained body features through a metric learning model for user authentication. To eliminate potential security threats that arise from frequent accesses by unauthorized users, OpenAuth synthesizes data placeholders for enhancing the applicability of unauthorized user identification. Finally, a k-NN-based authentication model is constructed based on the extracted body features to authenticate users' identities. Experiments in real environments show that the proposed OpenAuth achieves an average authentication accuracy of 93.4 % and false acceptance rate (FAR) of 1.8% in open-world scenarios.

关键词： Radio frequency Neural networks Authentication Imaging Radar imaging Feature extraction Robustness

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Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment 7

Effective DDoS Attack Detection using Deep Generative Radial...

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7th international conference on Communication and Electronics systems, ICCES 2022

作者： Kiruthiga, G. Saraswathi, P. Rajkumar, S. Suresh, S. Dhiyanesh, B. Radha, R. Cse Ies College of Engineering Thrissur India It Velammal College of Engineering and Technology Chennai India It Sona College of Technology Salem India Cse P.A. College of Engineering and Technology Pollachi India Hindusthan College of Engineering and Technology Coimbatore India Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity. © 2022 IEEE.

关键词： Network security

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