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Exploring Artificial Intelligence’s Potential in Developing Advanced distributed Denial of Service Defense Strategies

Exploring Artificial Intelligence’s Potential in Developing...

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international conference on Computing and Machine Learning, CML 2024

作者： Alhindi, Ahmad Computer Science and Artificial Intelligence Department College of Computer and Information Systems Makkah Saudi Arabia

ISBN: (纸本)9789819778386

Facing the growing menace of distributed denial of service (DDoS) attacks, there’s a critical demand for enhanced detection mechanisms. This necessitates a shift from traditional methodologies to more advanced, dynamic detection techniques. The significance of advanced computational methods in pinpointing DDoS threats is increasingly recognized. This paper delves into the crucial role these techniques play in detecting DDoS activities, with a focus on the adaptability of network-based algorithms and the proficiency of advanced models like Convolutional Neural networks (CNNs), Recurrent Neural networks (RNNs), and autoencoders in identifying complex attack patterns that traditional methods may overlook. Our findings reveal that while advanced computational strategies offer considerable benefits over conventional methods in terms of detection accuracy and adaptability to evolving threats, they also present unique challenges, including higher computational demands and the need for continuous model training to keep pace with new attack vectors. The comparative analysis underscores the operational advantages and potential limitations of these approaches, providing a nuanced understanding of their application in real-world scenarios. This investigation aims to present a current view of advancements in digital security techniques and to serve as a basis for future research and the development of innovative, practical solutions. It suggests that enhancing the resilience of digital infrastructures against DDoS attacks will require not only technological innovation but also a comprehensive strategy that includes ongoing research, collaboration among stakeholders, and the adoption of adaptive, scalable detection systems capable of responding to the dynamic nature of cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Novel distributed Tree-Based One-Hot Encoding (DTOHE) Technique for Intrusion Detection 6th

A Novel Distributed Tree-Based One-Hot Encoding (DTOHE) Tech...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Abirami, K. Samraj, Jasmine Chennai India

ISBN: (纸本)9789819728381

In the realm of cloud security, intrusion detection systems (IDSs) play a pivotal role as they are instrumental in identifying vulnerable activities occurring across networks and computer systems. Developing a novel IDS for Internet of Things (IoT)-enabled networks is a complex undertaking beset by numerous challenges, primarily stemming from difficulties in data access. Moreover, the specific constraints imposed by IoT devices further complicate the task of safeguarding the network against external threats and analyzing these threats effectively. In this current study, a range of existing methodologies has been explored to enhance the efficiency of intrusion detection systems, despite facing certain limitations and resource constraints. The principal objective of this research paper is to investigate the efficacy of existing intrusion detection systems integrated with genetic algorithm decision-making and coding techniques. By incorporating these techniques, the aim is to bolster the performance of intrusion detection systems and fortify them against external intrusion attempts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Enhancing Building Management systems with distributed Intelligence 13

Enhancing Building Management Systems with Distributed Intel...

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13th IEEE international conference on Communication systems and Network Technologies, CSNT 2024

作者： Rajalakshmi, B. Reddy, V Venkat Malviya, Bhupendra Thethi, H Pal Praveen Adnan, Myasar Mundher New Horizon College of Engineering Department of Computer Science Bangalore India Institute of Aeronautical Engineering Hyderabad India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350305463

There has been a significant change in the management and operation of buildings as a result of the incorporation of distributed Intelligence into Building Management systems. This paper analyzes the revolutionary potential of this integration, concentrating on the suggested technique, which amalgamates Decentralized Sensor networks, Federated Learning, and Predictive Maintenance algorithms. The approach uses real-time data, machine learning, and decentralized decision-making to improve building operations. The new technique is shown to perform better on all of the most important performance criteria when compared to the six original approaches. The suggested technique greatly improves upon conventional methods with regards to energy efficiency, occupant comfort, system responsiveness, maintenance cost reduction, security effectiveness, and flexibility. It is much easier to understand the comparative analysis and how the suggested solution outperforms the separate methods by using visual representations. © 2024 IEEE.

关键词： Energy efficiency

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Vehicle Identification Using RESNET-50: CNN approach 3

Vehicle Identification Using RESNET-50: CNN approach

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3rd IEEE international conference on Blockchain and distributed systems security, ICBDS 2024

作者： Singh, Kartikey Patil, Netra Mohite, Sagar Ganapati Jadhav, Swati Mohite, Snehal Gayakwad, Milind Joshi, Rahul College of Engineering Department of Computer Engineering Pune India BARTC's Vishwakarma Institute of Technology Department of Computer Engineering Pune India College of Engineering Cse Department Pune India College of Engineering Department of It Pune India Symbiosis Institute of Technology Cse Department Pune India

ISBN: (纸本)9798350354348

Vehicle This paper proposes a method for vehicle brand identification using a Convolution Neural Network (CNN) with improved Accuracy. The proposed method utilizes static image datasets and live CCTV surveillance data for training and real-time vehicle detection and recognition. This system can detect moving vehicles and recognize their brands, count them, and verify permits. Leveraging the advancements in deep learning, the proposed model builds upon classical CNNs with specific enhancements. This improved model is trained on a benchmark vehicle detection dataset and demonstrates superior performance in identifying vehicles on the road. The primary objective of this study is to develop a cost-effective and efficient solution for identifying vehicle brands across various applications. A deep learning-based classification technique is employed to achieve this goal. The model utilizes the popular ResNet-50 architecture combined with transfer learning techniques. Initial training yielded an accuracy of 34%. However, with further training, the model improved significantly, reaching an accuracy of 95.31%. A training-to-testing data split ratio of 43:7 was used. © 2024 IEEE.

关键词： Convolutional neural networks

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international conference on Current Problems of Applied Mathematics and computer systems, CPAMCS 2023

International Conference on Current Problems of Applied Math...

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international conference on Current Problems of Applied Mathematics and computer systems, CPAMCS 2023

ISBN: (纸本)9783031640094

The proceedings contain 59 papers. The special focus in this conference is on Current Problems of Applied Mathematics and computer systems. The topics include: Forced Longitudinal Oscillations of Vertical Rods with Concentrated Mass;method of Guaranteed Collision Avoidance with Multiple Unpredictable Target-Vessels;computer-Oriented Lyapunov Stability Criteria for systems of Nonlinear Ordinary Differential Equations;asymptotic Estimates of the Horizontal Wind Retrieval Accuracy from Lidar Remote Sensing Data;effect of Wind Field Deformation on the Wind Components Retrieval Accuracy from Lidar Measurements;on Vibration of Multi-span Continuous Beam in View of Rotational Inertia;load Balancing Methods for distributed Data Storage: Challenges and Opportunities;method for Detecting and Correcting Errors in Arithmetic Operations Based on Rank of a Number;toward Understanding Uncertainty in Fog-Cloud Computing for Big Data Storage and Processing;simulation of a Coaxial Resonator of a Frequency-Selective Device for Communication systems, Radio Navigation and Radar;analytical Review of Orchestration Methods for distributed Computing systems Under Conditions of Dynamic Scaling;modeling of Density Properties of Subsiding Soils Under Explosive Impact;comparative Analysis of Error Correction Devices in Modular Redundant Codes;estimation of the Solution of a Spatial Parabolic Equation Describing Anisotropic Geological systems;development of a Mathematical Model of the Software Solution «Bioeconomical Diagnostics of Health Protection»;fault Tolerant System for Data Storage, Transmission and Processing in Fog Computing Using Artificial Neural networks;A 17ps Two Channel TDC System Based on XILINX ZYNQ 7000 SoC;investigation of the Rank of Numbers Represented in the Residue Number System;power Consumption Modelling for Symmetric Block Encryption Algorithms;methodology for Assessing the security Risks of Cyber-Physical systems.

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LiteCrypt: Enhancing IoMT security with Optimized HE and Lightweight Dual-Authorization 30

LiteCrypt: Enhancing IoMT Security with Optimized HE and Lig...

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30th IEEE international conference on Parallel and distributed systems, ICPADS 2024

作者： Xie, Qipeng Wang, Weizheng Huang, Yongzhi Zheng, Mengyao Shang, Shuai Jiang, Linshan Khan, Salabat Wu, Kaishun IoT Thrust Guang Zhou China Hong Kong University of Science and Technology Hong Kong City University of Hong Kong Computer Science Engineer Hong Kong Dsa Thrust Information Hub Guang Zhou China Harvard T.H. Chan School of Public Health Boston United States University of Electronic Science and Technology of China Chengdu Chengdu China National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798331515966

The integration of 5G/6G networks with intelligent healthcare systems has enabled early disease detection through patient data monitoring. However, the Internet of Medical Things (IoMT) and remote healthcare services introduce significant privacy and security risks. In this paper, we propose LiteCrypt, which addresses these challenges by introducing an optimized Homomorphic Convolutional Neural networks (HCNN) structure for secure inference and a lightweight Threshold Signature Scheme (TSS) based dual-authorization mechanism. To enhance the practicality of Homomorphic Encryption (HE)-based secure inference in telemedicine applications, LiteCrypt presents an optimized HCNN framework that ensures efficient and adaptable operations across multiple datasets. A high-performance GPU-accelerated HE engine is developed to address the computational demands of HE operations, enabling real-time processing of encrypted patient data. Besides, LiteCrypt introduces a novel TSS-based dual-authorization protocol, requiring consent from both the patient and the hospital to access patient data, thereby mitigating unauthorized access risks. The system adapts to a flexible 2-out-of-3 authorization scheme for emergencies, ensuring timely data retrieval while maintaining security. To overcome the initial challenge of prolonged computation time due to compute-intensive operations, In LiteCrypt, we utilized the lightweight TSS protocol, based on Oblivious Transfer (OT), which is designed for resource-constrained IoMT devices, reducing computation time from 11.9 to 0.11 seconds. Empirical validation demonstrates LiteCrypt's superior performance, achieving a 233-fold increase in processing speed, a 96% reduction in encrypted message size, and a 28-fold speed increase using GPUs. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on Whale Optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

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An Energy Efficient Clustering for Underwater Acoustic Sensor networks using 6G Technology 5

An Energy Efficient Clustering for Underwater Acoustic Senso...

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5th IEEE international conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Siddiqui, Shams Tabrez Hashmi, Syed Ghyasuddin Ali, Mohammed Maqsood Syed, Ziauddin Qidwai, Khalid Ali Salim, Abu Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia Applied College Jazan University Department of Business Administration Jazan Saudi Arabia

ISBN: (纸本)9798350351378

These underwater Acoustic Sensor networks (UWSNs) present unique challenges due to the harsh and dynamic underwater environment, making efficient communication protocols essential for their operation. This paper introduces an innovative Energy Efficient Clustering Routing Protocol (EECRP) specifically designed for UWSNs, leveraging the advancements of 6G technology. EECRP employs a distributed clustering mechanism and energy-efficient routing strategy to optimize energy consumption, prolong network lifetime, and enhance communication reliability. The protocol integrates with 6G technologies, such as terahertz communication and quantum communication security, to further enhance performance and security in UWSNs. Through simulation and comparative analysis, EECRP demonstrates superior performance compared to existing routing protocols in terms of energy efficiency, throughput, and scalability. EECRP's contributions lie in its novel approach to routing in UWSNs, emphasizing energy efficiency, sustainability, and integration with advanced technologies. This research paves the way for the adoption and deployment of EECRP in various underwater applications, facilitating scientific research, environmental monitoring, and technological innovation in underwater communication systems. © 2024 IEEE.

关键词： Scalability

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Detection and Evaluation of Cybersecurity Threats in MANET Based on AI 4

Detection and Evaluation of Cybersecurity Threats in MANET B...

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4th international conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Mohandas, R. Vaigandla, Karthik Kumar Sivapriya, N. Kirubasankar, K. Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India Balaji Institute of Technology and Science Department of Electronics & Communication Engineering Telangana Warangal India Cauvery College for Women Department of Computer Applications Tamil Nadu Trichy India

ISBN: (纸本)9798331529635

This research focuses on the use of AI to enhance the security of Mobile Ad-hoc networks (MANETs), which aredecentralized and mobile wireless networks characterized by their dynamic nature. The work aims to assess the risks associated with cyber activities and identify various sorts of cyber threats, such as distributed Denial of Service (DDoS) attacks, malware invasions, data leaks and unauthorized access attempts. This will be achieved by using Artificial Intelligence (AI)-powered algorithms and models. The objective is to enhance the accuracy in identifying and categorizing these threats, hence increasing the security and dependability of MANET networks. Anomaly detection is a supplementary protection mechanism that specifically targets the hardware and network traffic of MANETs. Implementing a monitoring strategy is necessary in order to detect any anomalous activity that might potentially indicate security vulnerabilities or attacks in the MANET setups. The primary objective is to identify and address the unique characteristics of timely detection, which may enhance the security capabilities of MANET against cyber attacks. Machine learning (ML) algorithms often demonstrate impressive performance in efficiently identifying and accurately classifying various cyber threats, including DDoS attacks, malware infiltrations, and attempted unauthorized access. This anomaly detection technology effectively identifies robot abnormalities and malicious actions in network data, while also proactively avoiding system vulnerabilities or threats. In addition, this research discovered very effective AI-based cybersecurity solutions for dynamic decentralized MANET systems. These systems were specifically designed for tasks such as street-view switching, route finding, self healing, and self configuration. © 2024 IEEE.

关键词： Cyber attacks

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Malware Detection Method Based on TPE Optimized Stacking 3

Malware Detection Method Based on TPE Optimized Stacking

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3rd international conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Zhao, Dongbo Yuan, Zesong Xie, Nannan Jilin Province Key Laboratory of Network and Information Security School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Fast detection and categorization of malware are increasingly important for securing hosts and networks. Although many Machine Learning models have been utilized to detect malware, single-model detection may not remain efficient in the face of diverse datasets. The continuously increasing size of malware need the distributed storage systems and distributed computing. The distributed methods and ensemble learning are introduced into malware detection, and an optimal combination of base classifiers suitable for stacking are designed. A hyperparameter optimization method based on the Tree-structured Parzen Estimator (TPE) approach to enhance malware detection. The proposed method is implemented on Apache Spark and Hadoop distributed File System(HDFS). Experiments conducted on four independent datasets, including Android and Windows, demonstrate that the proposed method can achieve 99.41% accuracy on the Android dataset and 96.96% accuracy on the Windows dataset. © 2024 SPIE.

关键词： Learning systems

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