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Research on Building a Standardized Library for Auditing Heterogeneous Information Models in distributed Data 7

Research on Building a Standardized Library for Auditing Het...

引用

7th IEEE international conference on Information systems and computer Aided Education, ICISCAE 2024

作者： Heping, Huang Zhonglu, Zou Zhicai, Huang Dongguan Power Supply Bureau Guangdong Power Grid Corporation Guangdong Dongguan523000 China

ISBN: (纸本)9798350350760

With the advancement of smart grid transformation, the configuration audit of distribution network communication networks faces challenges such as diversified network devices, numerous manufacturers, and multi-source data management. Traditional methods exhibit significant deficiencies in processing efficiency and data consistency, failing to meet the demands of large-scale data management. This paper proposes a standard library construction method for configuration audit of distribution network data networks. By integrating knowledge graph technology with convolutional neural networks, this method establishes dynamic and static audit libraries, achieving accurate, rapid, and comprehensive management of audit data. The specific process includes the collection and preprocessing of multi-source data, construction of the knowledge graph, application of the inference mechanism, and intelligent retrieval and updating of the standard library. This method significantly improves the accuracy and completeness of data security configuration and optimizes the configuration audit process of distribution network communication networks. ©2024 IEEE.

关键词： Data consistency

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Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural networks 5

Deception Detection from Linguistic and Physiological Data S...

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5th international conference on Information Science, Parallel and distributed systems, ISPDS 2024

作者： Li, Panfeng Abouelenien, Mohamed Mihalcea, Rada Ding, Zhicheng Yang, Qikai Zhou, Yiming University of Michigan Department of Electrical and Computer Engineering Ann Arbor United States University of Michigan Department of Computer Science Ann Arbor United States Columbia University Fu Foundation School of Engineering and Applied Science New York United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Saarland University of Applied Science Department of Engineer Sciences Saarland Germany

ISBN: (纸本)9798350352719

Deception detection is gaining increasing interest due to ethical and security concerns. This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and one falsified response from each subject about each topic. In particular, we make three main contributions. First, we extract linguistic and physiological features from this data to train and construct the neural network models. Second, we propose a fused convolutional neural network model using both modalities in order to achieve an improved overall performance. Third, we compare our new approach with earlier methods designed for multimodal deception detection. We find that our system outperforms regular classification methods;our results indicate the feasibility of using neural networks for deception detection even in the presence of limited amounts of data. © 2024 IEEE.

关键词： Convolutional neural networks

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Generating Explanations for Model Incorrect Decisions via Hierarchical Optimization of Conceptual Sensitivity

Generating Explanations for Model Incorrect Decisions via Hi...

引用

international Joint conference on Neural networks (IJCNN)

作者： Gao, Lijun Sun, Zeyang Yan, Jiaxin Wang, Suran Zhang, Youzhi Jin, Xiao Shenyang Aerosp Univ Sch Comp Sci Shenyang Liaoning Peoples R China

ISBN: (纸本)9798350359329;9798350359312

The capacity to analyze the causes of poor decisions made by visual recognition models is becoming increasingly crucial as the security requirements of various real-world systems continue to escalate. However, the complex structure and blackbox nature within deep neural networks constrain the mining of their error causes. Based on this, we propose a conceptbased (e.g. a group of pixel blocks that contain leaves represents the concept of leaves) automated strong localization interpretation framework, called hierarchically optimized concept-sensitive interpretation (HOCS), to provide quantitative analysis of the semantics of wrong decisions in the classification network is provided from two directions of internal and external information interference of samples. HOCS was applied to models with spurious correlation and well-distributed data in the training set. The results showed that it provided concrete explanations in a way that was understandable to humans and demonstrated the significant advantages of HOCS in terms of efficiency and accuracy.

关键词： Interpretability Concept Activation Vector Conceptual Sensitivity computer Vision

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Securing the Digital Realm Data Engineering and Deep Learning in Cybersecurity Crime Prevention

Securing the Digital Realm Data Engineering and Deep Learnin...

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Jeyaram, A. Muthukumaravel, A. Bharath Institute of Higher Education and Research Department of Computer Science Chennai India Bharath Institute of Higher Education and Research Department of Arts and Science Chennai India

ISBN: (纸本)9798350375442

The digital world of today is always evolving;hence strong cybersecurity measures are more crucial than before. Conventional cybersecurity defences frequently fall short in identifying and countering sophisticated cyberattacks because their actions are based on predetermined rulesets. The study suggests a novel way to address these problems by enhancing cybercrime prevention through the integration of data engineering and deep learning techniques. Deep learning model selection, preprocessing, and extensive data collection are used by the proposed system to identify tiny signs of cyber threats that elude traditional methods. Results of comparing performance indicators demonstrate notable improvements in detection precision, recall, detection accuracy, and reaction times over existing systems. With a 95% detection accuracy, a 2% false positive rate, and an average reaction time of 5 seconds, the proposed system vastly outperforms the existing system. Because it can distinguish between benign and hostile behaviour, the proposed system is a major advancement in proactive cyber security that gives businesses more precision and resilience in safeguarding their digital assets. © 2024 IEEE.

关键词： crime prevention cybersecurity data engineering detection accuracy digital realm response times

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On-Device AI security Solutions: A Study on Lightweight Ensemble Learning Technique 17

On-Device AI Security Solutions: A Study on Lightweight Ense...

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17th international conference on COMmunication systems and networks, COMSNETS 2025

作者： Dong, Huiyao Kotenko, Igor ITMO University Faculty of Information Technology and Security Saint Petersburg Russia St. Petersburg Federal Research Center of the RAS Laboratory of Computer Security Problems Saint Petersburg Russia

ISBN: (纸本)9798331531195

IoT security poses a significant challenge due to the presence of extensive confidential data and vulnerable devices. Artificial intelligence (AI) is increasingly viewed as pivotal in cybersecurity, enhancing the efficacy of security measures including login mechanisms with fingerprinting, real-time phishing detection, and multi-stage attack identification. However, centralized AI models face issues such as high variance and over-fitting, as well as resource inefficiencies. This study explores the deployment of on-device AI security models in a distributed manner with lightweight yet effective ensemble deep learning techniques. The proposed methods involve lightweight ensemble techniques such as learnable weighted average voting (WAVGVoting) and concatenation-based stacking, integrated with three base learners with distinct structures. The experiments on Edge-IIoT, utilizing 5-fold cross-validation, show that our approaches are effective even in dynamic data and diverse settings. Specifically, the stacking model achieved an average accuracy of 99.8441% with a recall of 99.7762%, while WAVGVoting showed an average precision of 99.9116% and a false alarm rate of 0.0883%, certainly indicating its applicability. © 2025 IEEE.

关键词： deep learning ensemble learning Internet of Things (IoT) intrusion detection

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Realtime Intrusion Detection in Unmanned Aerial Vehicles Using Active Learning and Generative Adversarial networks 29

Realtime Intrusion Detection in Unmanned Aerial Vehicles Usi...

引用

29th IEEE international conference on Parallel and distributed systems, ICPADS 2023

作者： Zeng, Qingli Barnt, Kailynn Ragan, Luke Nait-Abdesselam, Farid University of Missouri Kansas City United States

ISBN: (纸本)9798350330717

This paper introduces a novel real-time intrusion detection approach for UAV networks, addressing current security challenges. We leverage Generative Adversarial networks (GANs) to generate synthetic data and implement a stream-based active learning process for prompt detection and response. Our method merges GANs with active learning, involving human experts to navigate the dynamic nature of UAV data and improve detection accuracy. Our results show that this system outperforms traditional intrusion detection methods, promising a new direction for future security frameworks and their application across various networked systems. © 2023 IEEE.

关键词： Active learning Generative Adversarial networks Intrusion detection

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Evaluating the security and Privacy of Blockchain Integration in Domain Name systems 7

Evaluating the Security and Privacy of Blockchain Integratio...

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7th IEEE international conference on computer and Communication Engineering Technology, CCET 2024

作者： Samonte, Mary Jane C. Abaleta, Raphael M. Cayabyab, Maverick D. Guerrero, Ladner Gilion D. School of Information Technology Mapúa University Makati City Philippines

ISBN: (纸本)9798350355673

Domain Name systems (DNS) are crucial components of any modern network, as they allow users to type in a domain name as opposed to complete IP addresses to visit web pages on the internet. The DNS resolution process typically involves a recursive server that fetches addresses from the root, top-level domain, and authoritative nameservers and sends the address to the client. Since this process can be slow, a caching system is implemented to save and access previous queries. However, this approach can result in security and privacy vulnerabilities. Blockchain is an emerging distributed ledger technology whose decentralized nature and data validation mechanisms help ensure data integrity, reliability, and authenticity. This study investigates the feasibility of integrating blockchain into DNS. It examines and reviews how DNS security is typically implemented, how blockchain can be implemented in other systems, like Internet of Things (IoT) systems, as a security measure, and how other technologies, like honeypots and artificial intelligence (AI), can be implemented to enhance system security in different systems, and potentially in DNS. The discussion results highlight blockchain's effectiveness in securing and improving the privacy of DNSs and their users. The study recommends that more studies be conducted on integrating blockchain in existing system architectures, like DNS, and on using other technologies, such as honeypots and AI, in conjunction with blockchain to improve security in computer networks and systems further. © 2024 IEEE.

关键词： Differential privacy

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An Innovative Markovian Environment for Intrusion Detection in IoT networks

An Innovative Markovian Environment for Intrusion Detection ...

引用

2024 international conference on computer and Applications, ICCA 2024

作者： Hesham, Eslam Hamdy, Abeer Nagaty, Khaled The British University in Egypt Cairo Egypt The British University in Egypt Cairo Egypt The British University in Egypt Cairo Egypt

ISBN: (纸本)9798350367560

Recently, cyberattacks on IoT (Internet of Things) and IIoT (Industrial Internet of Things) networks have become increasingly complicated and more frequent. These attacks have posed considerable threats such as distributed Denial of Service (DDoS) attacks, malware propagation, and unauthorized data access. These attacks threaten many applications, such as smart grids, manufacturing, and critical infrastructure systems. This paper proposes a novel approach to managing cyber-attacks in IoT and IIoT networks by developing a custom Markov Decision Process (MDP) environment and applying Deep Reinforcement Learning (DRL). The agent will be trained by the applied RL algorithm to create and control security policies autonomously. This study proposes a custom MDP environment suitable for the detection of intrusions in IoT and IIoT networks using DRL. To facilitate the training and evaluation of the DRL agent, the MDP environment is designed to mitigate realistic network conditions and various intrusion scenarios. © 2024 IEEE.

关键词： Network intrusion

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Cloud distributed Optimal Control of ESS-PV Residential Inverters 3

Cloud Distributed Optimal Control of ESS-PV Residential Inve...

引用

3rd international conference on Power systems and Electrical Technology, PSET 2024

作者： Kim, Byungchul Abed, Eyad H. University of Maryland Department of Electrical and Computer Engineering College ParkMD United States

ISBN: (纸本)9798350353204

Perfect power flow methods can be employed to choose which inverters will supply and determine their optimal real and reactive power settings based on financial goals. Utilizing advanced semidefinite relaxation make possible to work out with lessen calculation loads. Reflecting on the complex power inculcation originated by strong robustness and the high resilience of ESS-PV inverter, the steady state voltage security area (SSVSA) for inverter is created. The voltage expedition indicator for inverter is suggested to measure the voltage security limit between the security range and operation state. The suggested scheme balances beneficially to large distribution networks. © 2024 IEEE.

关键词： cloud system Energy storage system (ESS) photovoltaic power flow voltage security

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Finding of DDoS Attack in IoT-Based networks Using Ensemble Technique

Finding of DDoS Attack in IoT-Based Networks Using Ensemble ...

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2024 international conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Srivastava, Animesh Tiwari, Shweta Kumar, Deepak Garg, Navin Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science and Engineering University Institute of Engineering Punjab Mohali India Sgt University Department of Computer Science and Engineering Gurugram India Uttarakhand Dehradun India

ISBN: (纸本)9798350375237

Ensemble techniques could find DDoS attacks more quickly in IoT. A more dependable and precise detection system can be achieved by integrating the most advantageous attributes of XGBoost and Random Forest. This ensures that Internet of Things systems remain operational. Internet of Things (IoT) systems are significantly compromised regarding security and dependability by distributed Denial of Service (DDoS) assaults. Ensemble approaches, which utilize multiple machine learning models to generate predictions, appear to be a viable method for facilitating the detection of DDoS attacks in such contexts. Conventional defenses against these everevolving attacks prove inadequate when applied to a singular method. It emphasizes Random Forest and XGBoost. The accuracy of the proposed ensemble in detecting distributed denial of service attacks initiated by Internet of Things devices is 99%. The findings demonstrate how ensemble methodologies can enhance the precision and resilience of DDoS attack detection in Internet of Things systems against adversarial assaults. © 2024 IEEE.

关键词： Internet of things

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