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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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Automatic Smart Contract Generation Through LLMs: When The Stochastic Parrot Fails 6

Automatic Smart Contract Generation Through LLMs: When The S...

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6th Distributed Ledger Technology Workshop, DLT 2024

作者： Barbàra, Fadi Napoli, Emanuele A. Gatteschi, Valentina Schifanella, Claudio Department of Computer Science University of Turin Via Pessinetto 12 Turin10149 Italy Department of Computer and Control Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin10129 Italy

Blockchain and Artificial Intelligence (AI) have revolutionized the technology landscape, evolving predominantly along parallel trajectories. This research investigates the feasibility of integrating these two domains, specifically examining the potential of the latest advancements in Large Language Models (LLMs) to assist non-experts in the development of production-ready Solidity smart contracts. Utilizing a lease agreement as a case study, we employ GPT-4 to translate the document into smart contract code. To ensure consistency, we design several distinct prompts with analogous objectives, each employed multiple times. Our evaluation methodology encompasses both automated analysis and expert manual review. The findings indicate a clear limitation: the current iteration of GPT-4 is incapable of generating production-ready smart contracts, primarily due to undetected coding flaws and discrepancies between the prompts and the generated code. This study underscores the challenges and limitations inherent in leveraging LLMs for the autonomous generation of complex, real-world applicable smart contracts. © 2024 Copyright for this paper by its authors.

关键词： Smart contract

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CORMAND2--针对工业机器人的欺骗攻击

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engineering 2024年第1期32卷 186-201页

作者： Hongyi Pu Liang He Peng Cheng Jiming Chen Youxian Sun College of Control Science and Engineering Zhejiang UniversityHangzhou 310000China Department of Computer Science and Engineering University of Colorado Denver DenverCO 999039USA

Industrial robots are becoming increasingly vulnerable to cyber incidents and attacks,particularly with the dawn of the Industrial Internet-of-Things(IIoT).To gain a comprehensive understanding of these cyber risks,vulnerabilities of industrial robots were analyzed empirically,using more than three million communication packets collected with testbeds of two ABB IRB120 robots and five other robots from various original equipment manufacturers(OEMs).This analysis,guided by the confidentiality-integrity-availability(CIA)triad,uncovers robot vulnerabilities in three dimensions:confidentiality,integrity,and *** vulnerabilities were used to design Covering Robot Manipulation via Data Deception(CORMAND2),an automated cyber-physical attack against industrial ***2 manipulates robot operation while deceiving the Supervisory control and Data Acquisition(SCADA)system that the robot is operating normally by modifying the robot’s movement data and data ***2 and its capability of degrading the manufacturing was validated experimentally using the aforementioned seven robots from six different ***2 unveils the limitations of existing anomaly detection systems,more specifically the assumption of the authenticity of SCADA-received movement data,to which we propose mitigations for.

关键词： Industrial robots Vulnerability analysis Deception attacks Defenses

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction Systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

UPB at IberLEF-2023 AuTexTification: Detection of Machine-Ge...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Preda, Andrei-Alexandru Cercel, Dumitru-Clementin Rebedea, Traian Chiru, Costin-Gabriel Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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Deep-Learning-Based Onset Time Precision in Acoustic Emission Non-Destructive Testing

Deep-Learning-Based Onset Time Precision in Acoustic Emissio...

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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Melchiorre, Jonathan D'Amato, Leo Agostini, Federico Manuello, Amedeo Politecnico di Torino Department of Structural Geotechnical and Building Engineering Turin Italy Politecnico di Torino DAUIN Department of Control and Computer Engineering Turin Italy University of Padua Department of Physics and Astronomy Padua Italy

ISBN: (纸本)9798350385014

To investigate the actual health status and mechanical properties of structural materials, both direct and/or indirect investigation procedures can be used. The acoustic emission (AE) method is a non-destructive indirect structural health monitoring method based on the analysis of the elastic wave propagation inside the material under study induced during cracks and micro-cracks coalescence, opening, and formation process. To capture reliable ultrasonic elastic waveform data, piezoelectric sensors are typically employed which are directly and firmly fixed and attached to the specimen under study. For identifying the region of crack formation, thus the position of structural damage in its early stage, at least four sensors must be employed simultaneously. Furthermore, the identification of the onset time is crucial to accomplishing this task. In this study, the authors proposed a deep-learning-based solution based on a U-net architecture for identifying onset time with a method attempting to overcome the existing limitations of traditional threshold-based methods. The onset time precision obtained with this artificial intelligence-based (AI) paradigm is discussed on an acknowledged dataset available in the literature based on Pencil Lead Break (PLB) data, commonly used as a benchmark in the AE field. Finally, the method is tested on some real AE signals acquired during laboratory testing of reinforced concrete specimens. The results demonstrated the actual potential of the proposed AI-based method in future real-time monitoring real- world applications. © 2024 IEEE.

关键词： Structural health monitoring

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Understanding Deep RL agent decisions: a novel interpretable approach with trainable prototypes 4

Understanding Deep RL agent decisions: a novel interpretable...

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4th Italian Workshop on Explainable Artificial Intelligence, XAI.it 2023

作者： Borzillo, Caterina Ragno, Alessio Capobianco, Roberto Department of Computer Control and Management Engineering Sapienza University of Rome RM Rome00185 Italy Sony AI

Deep reinforcement learning (DRL) models have shown great promise in various applications, but their practical adoption in critical domains is limited due to their opaque decision-making processes. To address this challenge, explainable AI (XAI) techniques aim to enhance transparency and interpretability of black-box models. However, most current interpretable systems focus on supervised learning problems, leaving reinforcement learning relatively unexplored. This paper extends the work of PW-Net, an interpretable wrapper model for DRL agents inspired by image classification methodologies. We introduce Shared-PW-Net, an interpretable deep learning model that features a fully trainable prototype layer. Unlike PW-Net, Shared-PW-Net does not rely on pre-existing prototypes. Instead, it leverages the concept of ProtoPool to automatically learn general prototypes assigned to actions during training. Additionally, we propose a novel prototype initialization method that significantly improves the model's performance. Through extensive experimentation, we demonstrate that our Shared-PW-Net achieves the same reward performance as existing methods without requiring human intervention. Our model's fully trainable prototype layer, coupled with the innovative prototype initialization approach, contributes to a clearer and more interpretable decision-making process. The code for this work is publicly available for further exploration and applications. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Reinforcement learning

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Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing

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Digital Communications and Networks 2022年第4期8卷 516-530页

作者： Qi Zhang Yingjie Wang Zhipeng Cai Xiangrong Tong The School of Computer and Control Engineering Yantai UniversityYantai264005China Yantai Key Laboratory of High-end Ocean Engineering Equipment and Intelligent Technology Yantai264005China The Department of Computer Science Georgia State UniversityAtlantaGA30303USA

In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical *** a part of the IoT ecosystem,task assignment has become an important goal of the research *** task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment *** the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is *** is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task *** first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph *** second stage uses a bipartite graph to complete the online task assignment *** paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment *** encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’*** avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase ***,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.

关键词： Spatiotemporal crowdsourcing Cross-regional Edge cloud Offline prediction Oline task assignment

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