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Employing Deep Neural networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity systems

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Aadam Quraishi Maher Ali Rusho Anurag Prasad Ismail Keshta Richard Rivera Mohammed Wasim Bhatt M.D. Research Intervention Treatment Institute Houston Texas US Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado IIT Kharagpur (Rajiv Gandhi School Of Intellectual Property Law) Gurugram India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity systems.

关键词： Visualization Transfer learning Circuits Real-time systems Smart grids Problem-solving computer security

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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Two-Stage Optimal Operation of SOPs in Active Three-Phase Distribution systems 3

Two-Stage Optimal Operation of SOPs in Active Three-Phase Di...

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3rd IEEE international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Sharma, Abhimanyu Paul, Subho Padhy, Narayana Prasad Indian Institute of Technology Roorkee Department of Electrical Engineering Roorkee247667 India Washington State University School of Electrical Engineering and Computer Science PullmanWA99163 United States Indian Institute of Technology Roorkee Mnit Jaipur Department of Electrical Engineering Roorkee247667 India

ISBN: (纸本)9798350304732

The asymmetric integration of distributed energy resources exacerbates the unbalanced conditions in three-phase distribution networks. The increased unbalanced conditions pose severe threats to the system security, cause inefficient system resource utilization, and bring additional computational challenges to the system operation. Soft open point (SOP) is a flexible power electronic facility that can accurately control the (kW + j kVAr) line flows to balance the three-phase power flows. This paper proposes a two-stage optimal operation of SOPs to minimize the kW power injection in the active three-phase distribution network. The second-order cone (SOC) programming is advocated to alleviate the non-convexity of the branch flow model (BFM). The inexactness of the SOC programming in the unbalanced distribution system is removed using the novel solution recovery and successive bound tightening algorithms. Simulations conducted on the modified IEEE 34-bus distribution system verify the effectiveness of the proposed solution strategy. The results analyses validated the proposed approach, and the results are thoroughly verified using OpenDSS. © 2023 IEEE.

关键词： Convex relaxation Optimal Power Flow SOC SOP Unbalanced Distribution System

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Neural Network-based Cyber Threats Detection and Mitigation Framework in Aerial Communication systems Underlying 6G

Neural Network-based Cyber Threats Detection and Mitigation ...

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international conference on Parallel distributed and Grid Computing (PDGC)

作者： Harsh Parekh Aparna Kumari Dilip Yadav Sudeep Tanwar Prasun Kumar Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Computer Science and Engineering SCSET Bennett University Greater Noida India Department of Biotechnology SSET Sharda University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This study aims to enhance the security of communication between aeroplanes and ground control from potential risks. We introduce a novel framework, i.e., NeuroSkyWatch to protect aeroplanes from interference attacks like jamming and distributed Denial of Service (DDoS). NeuroSkyWatch utilizes Bidirectional Long Short-Term Memory (Bi-LSTM), a neural network (NN), to detect and prevent threats proactively. Operating as an intelligent system, NeuroSkyWatch acts as a smart radar, continuously analyzing digital communication patterns in aerial systems using the Bi-LSTM model. Moreover, the Bi-LSTM’s ability to encompass past and future contextual information enables proactive threat detection. Additionally, NeuroSkyWatch leverages 6G technology to strengthen the security of airborne platforms in real time. This approach supports terabit-level data rates, ultra-low delay in the microsecond range, and extensive sensor/actuator data support. By combining NN algorithms with 6G networks, NeuroSkyWatch strengthens defences against evolving cyber threats in aerial environments. NeuroSkyWatch demonstrates its effectiveness across diverse scenarios, achieving a prediction accuracy of 95%, a communication delay of 245 milliseconds, and a validation loss of 0.31, surpassing existing approaches in security and performance.

关键词： 6G mobile communication Technological innovation Atmospheric modeling Prevention and mitigation Radar Threat assessment Real-time systems Delays Testing Resilience

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Autonomous Cyber Defense using Graph Attention Network Enhanced Reinforcement Learning

Autonomous Cyber Defense using Graph Attention Network Enhan...

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international conference on Parallel and distributed systems (ICPADS)

作者： Yihang Shi Huajun Zhang Lin Shi Shoukun Xu School of Computer Science and Artificial Intelligence Changzhou University Changzhou China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Ubiquitous networked hosts and Internet of Things (IoT) devices have enabled critical network applications in both enterprise and industrial environments. However, network threats have proliferated, constantly challenging normal network operations and data security of IoT devices. In facing these challenges, defenders have increasingly adopted Deep Reinforcement Learning (DRL) approaches, aiming to leverage their self-learning and adaptability to bolster network security. Despite these efforts, existing approaches often exhibit significant performance bottlenecks when navigating the complexities of network scenarios. This paper introduces a novel algorithm, the Normalized Graph-Attention Proximal Policy Optimization (NGA-PPO), which synergistically integrates Graph Attention networks (GAT) with the existing Proximal Policy Optimization (PPO) framework. By harnessing a weighted mechanism to amalgamate intricate network connectivity data, NGA-PPO empowers intelligent defense agents to dynamically decipher dependencies and interactions within complex network architectures, thereby facilitating precise and prompt defense actions. Comprehensive experiments within the Yawning Titan network security simulation platform reveal that NGA-PPO outperforms other methods, delivering substantial improvements in performance and exhibiting distinct advantages in robustness and generalization capabilities. Specifically, as network scenarios become more complex, existing algorithms face significant performance limitations, whereas NGA-PPO consistently demonstrates high performance, thereby affirming its efficacy and viability in mitigating complex network threats.

关键词： Performance evaluation Navigation Heuristic algorithms Complex networks Network security Deep reinforcement learning Robustness Internet of Things Optimization Faces

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distributed Attacks Classification Based on Radical Basis Function and Particle Swarm Optimization In Hypervisor Layer

Distributed Attacks Classification Based on Radical Basis Fu...

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international conference on Information systems and computer networks (ISCON)

作者： Aditya Kumar Shukla Ashish Sharma Department of Computer Engineering & Applications GLA University Mathura (UP) India

The term ‘cloud computing’ refers to a concept for on-demand provisioning of computer resources (including hardware and software) through the Internet. Cloud computing’s primary benefits lie in the time and money savings it may provide to IT departments. While cloud computing offers many benefits It also raises many safety considerations. These include data privacy, data availability, data placement, data security credential, license, access management, network security, web data protection, virtual devices protection, etc. Recently, machine learning has shown to be an effective tool for safeguarding the cloud. In order to automate the process of identifying cloud assaults with better accuracy than any other technology, machine learning algorithms are trained on numerous legitimate datasets to develop models. To mitigate the effects of DDoS invasions, our research suggests implementing a detection method at the hypervisor layer. As a means of detecting and categorizing DDoS attacks in the communication between virtual computers, the suggested detection method combines the particle swarm optimization (PSO) with radial basis function (RBF). High classification accuracy against DDoS attacks is the focus of our investigation and recommended method.

关键词： Machine learning algorithms Virtual machine monitors Cloud computing security Data protection Machine learning Denial-of-service attack Safety

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A Cross Data Center Access Control Model by Constructing GAS on Blockchain

A Cross Data Center Access Control Model by Constructing GAS...

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international conference on Parallel and distributed systems (ICPADS)

作者： Shujiang Xu Chaoyang Zhang Lianhai Wang Shuhui Zhang Wei Shao Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

With the rapid development of cloud computing, big data, and mobile Internet technology, data sharing across different data centers is increasingly urgent. Meanwhile, data security risks such as unauthorized access and data abuse bring serious challenges to data security sharing. Due to its poor scalability, traditional access control technology cannot play its role in the distributed computing paradigms with massive data and dynamic network environment. Attribute-based access control (ABAC) model is a potential candidate for data sharing cross data centers. But it still faces some challenges, such as different attribute semantics among data centers, and slow query speed of attributes and policies. This paper proposes an architecture for access control across data centers by constructing a global attribute set (GAS) on the blockchain to manage attributes and policies. To achieve a high efficiency, the scheme utilizes world state rather than block for query of data attributes and policies, and employs trust degree to pre authorization. security analysis and experimental results show that the model is secure and practicable.

关键词： Access control Data centers Analytical models Computational modeling Data security Semantics Smart contracts

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Machine Learning Techniques to Detect a DDoS Attack in Flying AD-HOC Network

Machine Learning Techniques to Detect a DDoS Attack in Flyin...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Khushbu Jaiswal Sudesh Kumar Neeraj Kumar Rathore Department of Computer Science Indira Gandhi National Tribal University Amarkantak Madhya Pradesh India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Flying Ad-hoc Network (FANET) consists of aerial devices, such as Unmanned Aerial Vehicles (UAVs), which work together to perform specific tasks. The system stands out because of its unique coverage, speed, and mobility capabilities. However, these features also make it vulnerable to various security threats, particularly in routing. One of the major threats to the availability of FANET is the distributed Denial of Service (DDoS) flooding attack. These threats generate excessive traffic on network node, which causes frequent delays, packet loss, connectivity issues, and failure in operation. In this paper, we delve into detecting DDoS attacks by monitoring traffic patterns. We simulate a DDoS flooding attack scenario and explore different Machine Learning (ML) techniques to enhance detection accuracy.

关键词： Support vector machines Performance evaluation Accuracy Computational modeling Denial-of-service attack Picture archiving and communication systems Ad hoc networks security Floods computer crime

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Quantum Long Sort-Term Memory-based Identification of distributed Denial of Service Attacks

Quantum Long Sort-Term Memory-based Identification of Distri...

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AI in Cybersecurity (ICAIC), international conference on

作者： Sarvapriya Tripathi Himanshu Upadhyay Jayesh Soni Electrical and Computer Engineering Florida International University Miami USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

distributed Denial of Service (DDoS) is defined as an attack targeting a server, where the server is overwhelmed by an overflow of traffic, leading to a computer system being compromised. To prevent these attacks from happening, thus allowing the computer systems to maintain their normal behavior, a long short-term memory (LSTM) Time-Series model can recognize sequential characteristics in the data in order to predict whether a DDoS attack is occurring. Our approach leverages various aspects of network traffic such as Flow ID, Source IP, Destination IP, and Destination Port, and analyzes the accuracy and reliability using LSTMs to improve DDoS attack prediction accuracy. In this study we analyzed the relative efficiency of Quantum LSTM (QLSTM) over that of Classical LSTM, and found that both Quantum and Classical LSTM models train quickly and within a few epochs the accuracy increases to 0.99 and above. The validation of the models proved that QLSTM achieved better Recall, Accuracy, Precision, and F1 scores as compared to classical LSTM, making it an ideal candidate for DDoS attack predictions. This study proves the potential of applying deep learning techniques to strengthen network security, which has become increasingly important with the rise of complex cybersecurity threats.

关键词： Accuracy Quantum computing Telecommunication traffic Predictive models Denial-of-service attack Servers IP networks Reliability computer crime Long short term memory

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Training of cyber defense agents in tactical inter-organizational networks by using federated learning 22

Training of cyber defense agents in tactical inter-organizat...

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22nd international conference on Military Communication and Information systems, ICMCIS 2022

作者： Aurisch, Thorsten Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE Bonn Germany

Malware used in advanced persistent threats often establish a covert channel. By means of such a channel, the malware can communicate with external Command & Control servers or send classified data to a server controlled by the attacker. In military networks, these covert transmissions are a security problem with significant impact on the success of a military operation. Mobile agents in combination with machine learning are particularly suitable to detect and mitigate these type of attacks. However, a mobile agent needs to be trained before its deployment. In this paper, a concept is defined how federated learning can be used to train the mobile agents in tactical networks if the training data is distributed on the nodes of a tactical network. Furthermore, we propose an algorithm to select the nodes that are involved in the training. The proposed concepts and methods are evaluated regarding their efficiency. The evaluation considers in particular federated learning in the inter-organizational tactical networks because multinational armed forces are often involved in modern military operations. © 2022 Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE.

关键词： Malware

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