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共 5351 条记录，以下是71-80 订阅

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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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Advancements in Suspicious and Violent Activity Recognition for Intelligent Video Surveillance

Advancements in Suspicious and Violent Activity Recognition ...

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Disruptive Technologies (ICDT), international conference on

作者： Niyati Rana Deepak Parashar Nilesh Bahadure Hetal Jethani Sudipta Banerjee Kanhaiya Sharma Department of Computer Scinece & Engineering GSFC University Vadodara Gujarat Gujarat India Dept. of CSE Symbiosis Institute of Technology Pune Symbiosis International Univesity Pune Maharashtra India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Recognition of suspicious or violent activity in video surveillance has become increasingly important in terms of public safety and security. This synthesis examined state-of-the-art methodologies for detecting anomalous human behaviors-such as machine learning, deep learning, and hybrid approaches. By analyzing key contributions across recent studies, we identify advancements in feature extraction, model architectures such as CNNs, LSTMs, and Conv3D, and their applications to datasets like UCF101 and custom video repositories. The comparative analysis shows promising aspects in terms of performance and reliability, with some techniques such as Time-distributed CNN getting accuracy improvements of over 90%. The issues of practical applications in a real-world scenario, problems with dealing with advanced datasets, and future research directions such as real-time implementation and ethical considerations were discussed. The present study provides an inclusive overview of the current trends in intelligent surveillance systems and the roadmap for innovations in the near future.

关键词： Deep learning Ethics Adaptation models computer architecture Streaming media Video surveillance Feature extraction Real-time systems Public security Convolutional neural networks

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Integrating Blockchain and VANET for Privacy-Preserving and Authentication

Integrating Blockchain and VANET for Privacy-Preserving and ...

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international conference on ent Computing and Control systems (ICICCS)

作者： Sivagami A Sachin A Mohammad Arshath A Kiruthika N Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Apart from safeguarding the cars' identity privacy within Vehicular Ad-hoc networks (VANETs), privacy-preserving authentication is considered the primary protection against potential attacks. Unfortunately, the current authentication techniques have several shortcomings, including a significant overhead to validate messages and vehicle identifies where a large burden to revoke certificates, and non-transparency of the Trusted Authorities (TAs). Researchers present a Blockchain-based Privacy-Preserving Authentication technique. The semi-trusted authority's acts are transparent and verifiable which permanently and irreversibly stores all certificates and transactions in the blockchain. However, the practical driving scenarios (high speed or high message volume under congestion) remain a challenge when it comes to using such a blockchain for authentication. To offer distributed authentication method independent of revocation list, researchers have added the authentication data structure to the traditional blockchain topology. It allows a vehicle to employ numerous certificates to achieve conditional privacy. The linkability among certificates and real identity is preserved on the blockchain that can only be accessed in the event of a dispute. Scholars assess the efficacy and validity for every entity current systems, individual cars can handle the distributed authentication in less than 1 ms, satisfying the real-time requirement and needing a large reduction in processing time and storage.

关键词： Privacy Protocols Authentication Vehicular ad hoc networks Real-time systems Blockchains Topology security Automobiles Protection

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Assessing the Impact of Machine Learning-Based DDoS Attack Detection on SDN Network Performance

Assessing the Impact of Machine Learning-Based DDoS Attack D...

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international conference on Information Networking

作者： SmritiArora HariBabuK ShrinivasChoudhary CSIS BITS Pilani Rajasthan India

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

In recent years, the field of network security has seen significant progress. Researchers and developers have focused their efforts on designing innovative techniques to detect and counter various security threats and malicious activities. These advancements have strengthened the resilience of networks against potential attacks and have provided organizations with more effective tools to safeguard their digital assets. Within these groundbreaking developments, the application of AI technologies, notably machine learning (ML) and deep learning (DL), has proven to be remarkably successful in safeguarding software-defined networks (SDN) and bolstering the comprehensive internet security framework to defend against distributed denial-ofservice (DDoS) attacks. This research paper focuses on evaluating the performance of an SDN-based DDoS detection system, with a specific emphasis on measuring the time required to identify an ongoing attack. By conducting a thorough analysis of the system’s efficiency and responsiveness, this study aims to highlight the advantages of leveraging SDN and data plane programming in overcoming the shortcomings of traditional DDoS mitigation techniques. This study’s results are expected to support current initiatives aimed at strengthening cybersecurity measures and safeguarding essential systems against emerging digital threats. The research outcomes will provide valuable insights to help organizations and policymakers adapt their defense strategies in response to the constantly changing nature of cyber risks.

关键词： Current measurement Switches Organizations Denial-of-service attack Control systems Time measurement Delays Time factors computer crime Software defined networking

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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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Trust-Based Assured Sensor Fusion in distributed Aerial Autonomy 25

Trust-Based Assured Sensor Fusion in Distributed Aerial Auto...

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Proceedings of the ACM/IEEE 16th international conference on Cyber-Physical systems (with CPS-IoT Week 2025)

作者： R. Spencer Hallyburton Miroslav Pajic Department of Electrical and Computer Engineering Duke University Durham NC USA

ISBN: (纸本)9798400714986

Multi-agent collaboration enhances situational awareness in intelligence, surveillance, and reconnaissance (ISR) missions. Ad hoc networks of unmanned aerial vehicles (UAVs) allow for real-time data sharing, but they face security challenges due to their decentralized nature, making them vulnerable to cyber-physical attacks. This paper introduces a trust-based framework for assured sensor fusion in distributed multi-agent networks, utilizing a hidden Markov model (HMM)-based approach to estimate the trustworthiness of agents and their provided information in a decentralized fashion. Trust-informed data fusion prioritizes fusing data from reliable sources, enhancing resilience and accuracy in contested environments. To evaluate the assured sensor fusion under attacks on system/mission sensing, we present a novel multi-agent aerial dataset built from the Unreal Engine simulator. We demonstrate through case studies improved ISR performance and an ability to detect malicious actors in adversarial settings.

关键词： Autonomous Vehicles

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Research on computer Network Big Data security Defense System Based on Support Vector Machine and Deep Learning

Research on Computer Network Big Data Security Defense Syste...

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Electronics, Energy systems and Power Engineering (EESPE), IEEE international conference on

作者： Yi Wang Jikui Wang Gansu Province Natural Resources Information Center Lanzhou Gansu China Lanzhou University of Finance and Economics Lanzhou Gansu China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

Due to the swift progress of big data technology, traditional network security systems that are in use can hardly deal with complex and diverse network attacks. In the current paper, a network security system that is based on big data technology is proposed. By the means of limited mathematical computations, for example, genetic algorithm (GA), support vector machine (SVM) and deep learning (DL) a superior and smarter defense system is created. Above all, the system carries out extensive data collection, that is traffic data, user behavior and system log in real time, with the help of numerous distributed data processing technologies such as Hadoop and Spark. The genetic algorithm was then employed to create the features required for the intrusion detection system to optimize the feature set of the detection systems which in the turn manifolds the detection efficiency. Next, to classify the network traffic, the support vector machine is utilized, and after that, optimization of the hyperplane separation is performed to enhance the accuracy of the anomaly detection. Lastly, the deep learning technology was used to significantly analyze the network traffic and it automatically produced higher levels of features and thereby enabling the machine to extract complex attack behaviors. The experiment result shows that the detection accuracy of our proposed system is 95. 4%, 92.1% and 89.7% when it comes to DDoS, SQL injection and XSS attacks, respectively, while the response time is much better than that of traditional defense systems. This paper's analysis supplies conceptual underpinning and practical protection for the issue of safety within the network of big data, and also has a significant practical impact.

关键词： Support vector machines Deep learning Accuracy Intrusion detection Big Data Network security Feature extraction computer networks Time factors Genetic algorithms

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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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5th international conference on Computing Science, Communication and security, COMS2 2024

5th International Conference on Computing Science, Communica...

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5th international conference on Computing Science, Communication and security, COMS2 2024

ISBN: (纸本)9783031751691

The proceedings contain 31 papers. The special focus in this conference is on Computing Science, Communication and security. The topics include: Osmotic Computing-Based Task Offloading: A Fuzzy Logic-Based Approach;Traveler’s Demand Reactive Dynamic Online Bus Routing [TraDeR-DOBR] to Improvize Comfort Perception in Intelligent Public Transport System;High-Speed FSO System for Future Generation networks for Long Reach;Mitigating PAPR Challenges in Massive MIMO systems for CR-IoT networks: A Graeco-Latin Square Approach;performance Analysis of IoT Network over 5G Communication;ioT-Based Convolutional Neural networks in a Farm Pest Detection Using Transfer Learning;Performance Comparison of NOMA Vehicular Communications Under Shadow Fading;Detecting distributed Denial of Service (DDoS) Attacks in a Multi-controller SDN Environment Utilizing Machine Learning;Comprehensive Study of Short Channel Effects (SCEs) in MOSFET and FinFET Devices;A Machine Learning (ML)-Inspired Method for Intrusion Detection in IoT Devices networks;an Anomaly—Misuse Hybrid System for Efficient Intrusion Detection in Clustered Wireless Sensor Network Using Neural Network;detection and Prevention of Black Hole Attack and Sybil Attack in Vehicular Ad Hoc networks;cognitive Ad Hoc Trust Routing for Enhanced Quality of Service;routing in IoT Network Using NetSim Simulator;A Novel Approach of SHA-3-512bits Using Keccak Technique Based on Sponge Function Implementation on FPGA;cooperative Spectrum Sensing in Cognitive Radio Network Using Adaptive Walruses Optimization Algorithm;Catalan’s Conjecture and Elliptic Curve Cryptography (CCECC) Algorithm for Enhancing Data security During Data Transmission in MANET;monitoring the Concentration of Air Pollutants and Its Health Hazards Using Machine Learning Models;containment of Compromised Nodes in a distributed Environment;a Novel Approach to Solve Network security, Cryptography Problems Using Genetic Algorithm.

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Investigating a Comprehensive IoT security Solution Based on Blockchain 6th

Investigating a Comprehensive IoT Security Solution Based on...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Madhan, Ravinder Singh Singh, Randeep IEC University Himachal Pradesh Baddi174103 India

ISBN: (纸本)9783031840586

The term "Internet of Things" (IoT) refers to a network of interconnected objects capable of gathering data from their immediate surroundings and facilitating communication with one another over the Internet. "The proliferation of Internet of Things (IoT) devices has resulted in the generation of a significant amount of sensitive data, which is vulnerable to a range of security breaches. The growth of the IoT necessitates a significant emphasis on security measures, including aspects such as confidentiality, integrity, dependability, and several other issues. Due to the inherent limitations of hardware, the conventional security measures used in regular networks are not viable for implementation. Due to these factors, the decentralized record-based technique known as the blockchain (BC) approach is used instead of a centralized one. The main aim of this research is to conduct a complete analysis of the mechanism for blockchain-based securities in the context of the IoT. This study has specifically examined the application of blockchain technology in many businesses and categories. This approach yields superior outcomes concerning security considerations, including aspects such as security and privacy, when contrasted with the existing security systems. This study also examines the technology of Blockchain, its many uses, and the special issues it poses in the context of the IoT. The distributed and decentralized nature of blockchain technology offers hope for resolving these security issues in IoT networks. The integration of blockchain technology with the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm enhances the security of Internet of Things (IoT) networks. This integration facilitates more effective data management and ensures the integrity of transactions, hence promoting the durability and safeguarding of these networks. The objective of this article is to enhance the security of Internet of Things (IoT) networks. To do this, the paper proposes a

关键词： Information management

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