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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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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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Enhancing Intrusion Detection in Smart Grids: A Machine Learning-based Defense Against DDoS Attacks with Imbalance-Aware Optimization

Enhancing Intrusion Detection in Smart Grids: A Machine Lear...

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Electronics and Renewable systems (ICEARS), international conference on

作者： Mahaveerakannan R T. Rajesh Kumar Balamanigandan R Parnandi Srinu Vasarao Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Swarnandhra College of Engineering and Technology Narsapur Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The presence of numerous bidirectional communication devices connecting customers to the grid makes smart grid networks particularly vulnerable to network attacks. As an instance of a network attack that could impact the smart grid, a distributed denial of service (DDoS) attack occurs when a compromised grid communiqué devices or nodes send a flood of false data or requests to the smart grid network. This can cause problems with smart meters, data servers, and the state estimator, which in turn affects the services that end users receive. The problem of protecting the network from distributed denial of service (DDoS) attacks is best addressed by methods that rely on machine learning. One major drawback of using machine learning-based approaches is that models need to be retrained every time new types of attacks appear. Despite the fact that DDoS attack detection has long relied on Intrusion Detection systems (IDS), previous research has been limited due to a number of factors. Actually, it is highly discouraged to interfere with the regular functioning of the smart grid. to suggest using reconstructive deep learning methods to identify DDoS attacks efficiently and with minimal downtime in order to tackle this difficulty. In order to identify typical attacks on a communication network, this research introduces a shallow neural network with just 110 artificial neurones triggered by ReLU. to suggest a better attack-sharing loss function to handle unbalanced learning, which will allow for the training of such tiny neural networks. A Lungs Performance-based Optimisation (LPO) was devised using the suggested model to hone the parameters. In smart grid settings, our suggested approach improves the scalability and dependability of IDS and provides useful insights for building strong IDS based on machine learning. This enables organisations to strengthen their defences against harmful cyber-attacks and proactively reduce security risks.

关键词： Training Deep learning Lungs Intrusion detection Denial-of-service attack Smart grids security Reliability Optimization Business

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A Blockchain-Based Intelligent IoT Communication System for Enhanced security, Reliability, and Efficiency

A Blockchain-Based Intelligent IoT Communication System for ...

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Advanced Computing Technologies (ICoACT), international conference on

作者： Dinesh Kumar K J. Gobinath Jayavadivel Ravi S. Saravanakumar Senthil P B. Prabhu Shankar Department of Computer Science and Engineering Vel Tech University Chennai India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering Chennai India School of Computer Science and Engineering Presidency University Bengaluru India Department of Information Technology Paavai Engineering College Paachal India Department of Artificial Intelligence and Data Science T.J.S.Engineering college Chennai India Department of Artificial Intelligence and Machine Learning Vel Tech University Chennai India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

This research proposes a new approach for developing and implementing a smart communication system of IoT through developmental blockchain technology. Regarding the proposed solution, it is suggested that a distributed ledger can improve security, reliability, and efficiency of communication of IoT devices. They tackle issues such as data integrity, privacy and security in IoT networks as well as the problem of trust in such networks. The solution provides a decentralised and secure IoT ecosystem communication hence allowing monitoring, controlling and administration of the IoT build ecosystems in real time. Blockchain technology, however, when combined with nextgeneration communication interfaces and modern data analysis techniques, can offer various superior characteristics such as device identification, origin of information, etc. to IoT applications. It also enhances the dependability and capacity of IoT systems regarding capability and efficiency. Analysis and performance comparisons substantiate the performance benefits of the proposed intelligent communication system based on blockchain technology In addition these theoretical and performance analyses and experimental evaluation confirm the applicability and potential of the blockchain-based intelligent communication system to transform IoT communication models and facilitate the new generation of eIOT applications in a number of fields. The key to the solution to incorporate blockchain fundamental infrastructure to augment the communication channel of IoT devices. Using cryptography and consensus methodologies with the decentralized framework, integrity and confidentiality of the data is properly maintained because of the barriers of malicious interception or manipulation of the data. The communication system also boosts trust within the network participants because it offers a decentralized and immutable ledger of data transactions on the blockchain. Instead, one of the compelling benefits of this solution

关键词： Performance evaluation computers Communication systems Ecosystems Transforms Real-time systems Blockchains Internet of Things Reliability Monitoring

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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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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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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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Enhancing Cyber security Against DDoS Attacks: A Comprehensive Review and Future Directions

Enhancing Cyber Security Against DDoS Attacks: A Comprehensi...

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AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Smruti Jagtap Vikrant Kavitkar Mahek Jain Anup Ingle Jayashree P. Tamkhade Ketki Kshirsagar Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

distributed Denial of Service (DDoS) attacks pose significant threats to network infrastructures, particularly in critical sectors such as smart grid systems. This paper presents a comprehensive review of DDoS attack detection and prevention strategies based on recent research findings. It encompasses various methodologies ranging from machine learning-based detection approaches to hybrid models integrating router-based client puzzles and pushback techniques. Key themes explored include the importance of cybersecurity in smart grid applications, communication standard vulnerabilities, and the evolving landscape of cyber threats. Additionally, the paper discusses practical implications such as enhanced security, reduced economic losses, and improved resilience in the face of DDoS attacks. Through a synthesis of diverse perspectives, this review underscores the critical need for unified defense mechanisms and hierarchical structures to effectively combat DDoS threats and ensure the integrity and reliability of network operations.

关键词： Accuracy Machine learning algorithms Reviews Biological system modeling Prevention and mitigation Neural networks Denial-of-service attack Smart grids computer crime Random forests

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