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检索条件"任意字段=Conference on Internet Performance and Control of Network Systems II"

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Research on IoT Device Fingerprint Recognition in Grid Power System Based on Few-Shot Learning 5

Research on IoT Device Fingerprint Recognition in Grid Power...

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2023 5th International conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Zhang, Liang Ge, Minhui Zou, Futai East Branch of State Grid Corporation of China Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350323313

IoT smart devices play a pivotal role in power grid systems, contributing to efficient monitoring and control. However, the diverse range of IoT device types and the scarcity of samples pose challenges for accurate device recognition. In this context, we propose a method tailored to small-sample scenarios, addressing the vital need for robust detection. Our approach leverages the Triplet-loss concept to construct a Triplet-CNN feature extractor, harnessing the power of CNN networks for enhanced feature extraction and achieving the construction of a privacy recognition method for IoT devices under small sample conditions. Our model demonstrates exceptional performance, achieving an impressive 84% detection accuracy with a minimal sample size of just 20, significantly surpassing traditional deep learning algorithms. © 2023 IEEE.

关键词： internet of things

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Ethernet Device Authentication via Physical Layer Fingerprinting

Ethernet Device Authentication via Physical Layer Fingerprin...

引用

IEEE SoutheastCon conference

作者： Suski, William Card, Christopher Few, Brian Appl Engn Concepts Inc Eldersburg MD 21784 USA

ISBN: (纸本)9781665476119

Device authentication is an important element of any strong defense-in-depth strategy for securing cyber-physical, industrial control, and other critical infrastructure systems. However, the current stable of solutions available to perform device authentication are not suitable for deployment on many operational technology networks due to the power and processing limitations of legacy and state-of-the-art internet-of-Things devices. In this paper, we propose a machine learning method for passively and non-invasively authenticating or fingerprinting Ethernet devices, at the physical layer (PHY), using their transmitted signals. This technique exploits the unique, intrinsic physical features of a device that are created by the operational characteristics of its discrete physical components. These characteristics are imprinted on a device's communication signal, can be externally monitored to authenticate/authorize registered devices, and can quickly detect the introduction of unknown and/or unauthorized devices. We assess the performance of our proposed technique based on the discrimination power in a device classification scenario.

关键词： authentication intrusion detection network access control

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Advanced Persistent Threats Detection based on Deep Learning Approach 6

Advanced Persistent Threats Detection based on Deep Learning...

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IEEE 6th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Eke, Hope Nkiruka Petrovski, Andrei Robert Gordon Univ Sch Comp Aberdeen Scotland Natl Subsea Ctr Sch Comp Aberdeen Scotland

ISBN: (纸本)9798350311259

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses "low-and-slow" approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed "APT(DASAC)" to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and pre-processing, (ii) Data analysis layer;applies the core process of "APT(DASAC)" to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer;the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.

关键词： Advanced Persistent Threats Cyber-Physical systems Critical Infrastructures Deep Learning Industrial control systems Supervisory control and Data Acquisition

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Hierarchical Blockchain-based Resource Access control Architecture and Scheme for IoT Devices 96

Hierarchical Blockchain-based Resource Access Control Archit...

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IEEE 96th Vehicular Technology conference (VTC-Fall)

作者： Chai, Rong Jiang, Wenhang Yang, Xizheng Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

ISBN: (纸本)9781665454681

Traditional access control schemes in internet of things (IoT) systems have long been faced with security problems such as single point of failure, data tampering and cross-domain access. In this paper, we study the access control problem in an IoT system composed of multiple domains. Applying blockchain technology, we propose a hierarchical blockchain-based access control architecture which consists of two layers of blockchain networks, i.e., global blockchain network and local blockchain network. Based on the proposed architecture, the processes of inter-domain and intra-domain access control are examined, respectively. In order to achieve the secure access of user data in the proposed architecture, we further present a trust-value based access control strategy. To examine the performance of proposed architecture and strategy, we set up an access control simulation platform based on Hyperledger Fabric and verify the feasibility of the proposed architecture and strategy.

关键词： Blockchain

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CP-ABE encryption over MQTT for an IoT system with Raspberry Pi 56

CP-ABE encryption over MQTT for an IoT system with Raspberry...

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56th Annual conference on Information Sciences and systems (CISS)

作者： Mendoza-Cardenas, Fredy Stiv Leon-Aguilar, Rai Luis Quiroz-Arroyo, Jose Univ Nacl Ingn Inst Nacl Invest & Capacitac Telecomunicac Lima Peru

ISBN: (纸本)9781665417969

Nowadays, the increasing number of devices deployed in IoT systems implementation and the requirement of preserving the integrity of data transported over the internet, demand the use of data encryption schemes. This paper aims to show the performance evaluation of CP-ABE (Ciphertext-Policy Attribute Based Encryption) type of encryption over MQTT (Message Queue Transport Telemetry) that focuses on execution time for an IoT system with Raspberry Pi. For the implementation, two Raspberry Pi 4 Computer Model B are used for both the publisher and the subscriber, and a computer with Ubuntu 20.04 LTS operating system is used for the Broker and the Key Authority. The result of the present work provides relevant information on the execution times required in the CP-ABE encryption scheme to provide data integrity and fine-grained access control policy in an IoT system. The work demonstrates that the CP-ABE encryption scheme is suitable for IoT systems.

关键词： internet of Things (IoT) Message Queue Telemetry Transport (MQTT) Ciphertext-Policy Attribute Based Encryption (CP-ABE) Publish/Subscribe

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Power System Transient Stability Assessment Based on Graph Neural network with Interpretable Attribution Analysis 4

Power System Transient Stability Assessment Based on Graph N...

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4th International conference on Smart Power and internet Energy systems (SPIES)

作者： Gu, Sili Qiao, Ji Zhao, Zixuan Zhu, Qiongfeng Han, Fujia China Elect Power Res Inst Dept Artificial Intelligence Applicat Beijing Peoples R China

ISBN: (纸本)9781665489577

The power transient stability analysis is one of the basis for determining the control strategy of power system security and stability. Considering the influence of power grid topology on the transient stability of power system, the transient stability evaluation model is constructed based on the graph attention neural network. The electrical components and their transient operation data are mapped to the graph data with the spatial topology characteristics of power system for model training, so as to improve the topological generalization performance of the model. The marginal contribution of input characteristics to the output of transient power angle stability evaluation model is quantitatively calculated based on Shapley additive explanation (SHAP), so as to improve the interpretability of data-driven method for transient power angle stability evaluation. The effectiveness of the proposed method is verified by the IEEE 39-bus system.

关键词： transient stability assessment graph neural network Shapley additive explanation

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Managing IoT Cyber-Security Using Programmable Telemetry and Machine Learning

Managing IoT Cyber-Security Using Programmable Telemetry and...

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2023 International conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Sutariya, Kamal Pramanik, Kundan Kumar Parul University Piet Department of Cse India Arka Jain University Department of Engg. & It India

ISBN: (纸本)9798350322309

internet of Things (IoT) devices that are purchased from a variety of suppliers & installed in significant amounts are subject to increasing cybersecurity vulnerabilities. Consequently, it has become more crucial for telecom companies to control such devices. Current monitoring systems for communication analyze data through specialized speed on networking devices or thorough protocol analysis in programs, but these methods can be difficult, costly, rigid, and unable to scale. Using the SDN architecture together with ML, we harness the advantages of configurable motion-relied telemetry with adaptable information driven designs to control IoT gadgets depending on their system behavior in this article. Our 3 results are as follows: (1) Over the course of a half year period, we examine the network traffic patterns of 17 individual customer IoT devices in the laboratory as well as recognize a collection of traffic patterns (for every device) for whom the period characteristics tabulated at numerous timeframes (from a minute to an hour) characterize the connectivity behavior of different IoT gadget kinds, as well as their functioning stages (such as bootup, engaging with the subscriber effectively, or becoming inactive);(2) We create a multiple-level model of inferences which autonomously detect Iot systems from those which aren't IoTs, categories specific IoT device categories, & determine their operational statuses. (3) Then calculate the trade-off among expense & performance of our method and show the manner in which our tracking strategy can be utilized in practice for identifying behavioral changes. We teach the designs & test its effectiveness employing actual traffic patterns (firmware updates or cyberattacks). © 2023 IEEE.

关键词： internet of things

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Secure internet-of-Things Communications: Joint Precoding and Power control

Secure Internet-of-Things Communications: Joint Precoding an...

引用

IEEE International conference on Communications (ICC)

作者： Oh, Mintaek Park, Jeonghun Choi, Jinseok Ulsan Natl Inst Sci & Technol Elect Engn Ulsan 44919 South Korea Kyungpook Natl Univ Coll IT Engn Daegu 41566 South Korea

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

In this paper, we consider a downlink internet-of-things (IoT) multiple-input multiple-output (MIMO) network wherein an access point (AP), multiple IoT users, and a single eavesdropper coexist. The eavesdropper attempts to wiretap confidential messages of the IoT users. In the considered system, we solve a sum secrecy rate maximization problem in the finite blocklength (FBL) regime. Due to the FBL, the secrecy rate has a back-off factor with respect to blocklength, decoding error probability, and information leakage, which makes the problem more challenging. The main challenges are: i) the problem is not tractable because of the back-off factor, ii) an objective function is inherently non-convex, and iii) information leakage by the eavesdropper needs to be considered. To address these difficulties, we first obtain a lower bound of the secrecy rate and transform the problem into a product of Rayleigh quotients form. Then, we derive a first-order Karush-Kuhn-Tucker (KKT) condition to find a local optimal solution and interpret the condition as a generalized eigenvalue problem. Consequently, we develop a low-complexity algorithm by adopting a generalized power iteration-based (GPI) method. Via simulations, we validate the secrecy rate performance of the proposed method for the short-packet IoT communication systems.

关键词： internet-of-things physical layer security short-packet communications finite blocklength secrecy rate

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A Secure Deep Q-Reinforcement Learning Framework for network Intrusion Detection in IoT-Fog systems

A Secure Deep Q-Reinforcement Learning Framework for Network...

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Bebortta, Sujit Tripathy, Subhranshu Sekhar Sharma, Vandana Behera, Jnana Ranjan Nayak, Aishwarya Ravenshaw University Department of Computer Science Cuttack753003 India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar751024 India Department of Computational Sciences Delhi NCR Campus India Kiit Deemed to Be University School of Electronics Bhubaneswar751024 India Driems University Department of Computer Sceince & Engineering Cuttack74022 India

ISBN: (纸本)9798350373783

IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks © 2024 IEEE.

关键词： network intrusion

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Switching in the Rain: Predictive Wireless x-haul network Reconfiguration 23

Switching in the Rain: Predictive Wireless x-haul Network Re...

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2023 ACM SIGMETRICS International conference on Measurement and Modeling of Computer systems, SIGMETRICS 2023

作者： Kadota, Igor Jacoby, Dror Messer, Hagit Zussman, Gil Ostrometzky, Jonatan Columbia University New York United States Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9798400700743

4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism;and (ii) a Multi-Step network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds;and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances. The full paper associated with this abstract can be found at https://***/10.1145/3570616. © 2023 Owner/Author.

关键词： Wireless networks

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