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IoT DDoS Attack Detection System Using Machine Learning Classification Techniques

IoT DDoS Attack Detection System Using Machine Learning Clas...

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Student conference on Research and Development (SCOReD)

作者： Aiman Saftwan Bin Sa’Idi Ameerah Muhsinah Binti Jamil Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

The Internet of Things (IoT) has experienced substantial expansion, facilitating the connection of a multitude of devices to the Internet and enhancing the level of automation in several aspects of our daily routines. Nevertheless, the expansion has also brought to light several weaknesses, most notably the occurrence of Distributed Denial of Service (DDoS) attacks. IoT devices, which frequently lack comprehensive security measures, are vulnerable to such attacks. The objective of this work is to explore the feasibility of employing machine learning (ML) classification techniques to identify DDoS attacks on IoT networks. This study focuses on three commonly employed machine learning classification techniques: (i) Decision Tree (DT), (ii) Random Forest (RF), and (iii) Naïve Bayes (NB). The models will undergo training using a dataset specifically designed for detecting and classifying DDoS attacks. The TON IoT (UNSWIoT20) dataset sourced from the School of Engineering at the University of New South Wales Canberra is included in the study as the selected dataset. Among the several datasets accessible from the source, the IoT Network dataset with a specific emphasis on DDoS attacks was deliberately selected. To determine the efficacy of the suggested solution, a comprehensive assessment of the dataset is conducted. The testing results are then compared and analyzed to determine the model with the utmost level of precision in detecting DDoS attacks. This paper is a valuable contribution to the subject of IoT security by offering possible efficient and resilient approaches to address DDoS attacks targeting IoT devices and networks. The detection system, which utilizes machine learning, provides a proactive approach to protection, thereby guaranteeing the security and dependability of Internet of Things (IoT) services. Additionally, it facilitates the sustained growth of the IoT ecosystem.

关键词： Radio frequency Biological system modeling Denial-of-service attack Classification algorithms Bayes methods Internet of Things Decision trees

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Designing and Analyzing Secure SoC Architecture for IoT devices

Designing and Analyzing Secure SoC Architecture for IoT Devi...

引用

International conference on Circuit, Power and computing Technologies (ICCPCT)

作者： D.Ferlin Deva Shahila Dr.L.Padma Suresh P Aruna Jeyanthy Valantina Stephen Anushia R M Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Baselios Mathews II College of Engineering Kollam Chennai India Department of EEE Kalasalingam Academy of Research and Education Tamil Nadu India Department of EEE Saveetha School of Engineering Thandalam Chennai India Department of CSE DMI College of Engineering Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

As the Internet of Things (IoT) grows, it is more important to guarantee the security bbesides the integrity of data relayed between gadgets in the Internet of Things. SHA-256 algorithm implementation and the inclusion of six General Purpose Input/output (GPIO) pins will be emphasised in particular. Confidentiality is critical in the context of the Industrial Network (IoT), and a System-on-Chip (SOC) structure was developed to satisfy this prerequisite. Because of its robust cryptographic characteristics, the SHA-256 algorithm is a wise strategic choice that guarantees a high degree of data security and integrity in Internet of Things contexts architecture is painstakingly designed to balance power efficiency, effectiveness, and security. Sensitive data transfer is protected by end-to-end encryption thanks to the effortless incorporation of the SHA-256 algorithm. To improve security, the suggested architecture incorporates a SHA-256 encryption block alongside the use of a Zynq Ultra Scale and MPSoC board. The framework integrates a SHA-256 Verilog code for security, uses GPIO for input, and uses I2C to interact with a camera. SRAM, which connects to the SHA-256 module, is connected to registers and an ALU. UART is used to transfer the output for additional processing and analysis. The findings show that the suggested architecture is very compatible with implementation in various Internet of Things applications since it not only offers strong security features but also provides remarkable performance and power economy. The presence of six GPIO pins, boosting the adaptability and usability of the IoT devices, is a unique aspect of the suggested architecture. These GPIO pins have risen functionality, enabling versatility and applicability in a range of environments. It also entails a careful examination of the SOC architecture that has been created, determining how well it strengthens the world of the web of Things' security posture. By fusing the SHA-256 algorithm’s p

关键词： Performance evaluation Software algorithms Computer architecture Software Pins Encryption System-on-chip

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Assessing the Impact of Emerging Technologies on Cybersecurity with a Special Emphasis on Artificial Intelligence, the Internet of Things, and Blockchain Innovations

Assessing the Impact of Emerging Technologies on Cybersecuri...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Eliph Mazher Mankhi Akula Rajitha V Revathi H Pal Thethi Dinesh Kumar Yadav K. Surya Kanthi College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq Institute of Aeronautical Engineering Dundigal Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh AP Dept. of IT St. Martin’s Engineering College Secunderabad Telangana India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article proposes a safety strategy that addresses the complex concerns raised by blockchain, AI, and the Internet of Things. Use Threat Intelligence Integration (Tii), Dynamic Risk Assessment (DRA), Blockchain Integrity Verification (BIV), AI Adversarial Robustness Assessment (AARA), and IoT Security Compliance Assessment (ISCA). Each program is part of a larger, more linked defense system for sophisticated cyberthreats. The program uses Threat Intelligence Integration. Combining historical data with realtime hazard sources predicts assaults and their outcomes. The Tii enabled new algorithms like DRA. The algorithms discover assets, assess weaknesses, and prioritize threats. Blockchain Integrity Verification (BIV) checks for issues and performs complicated hash and weight computations to secure the blockchain. AI Adversarial Robustness Assessment (AARA) evaluates AI models in BIV tests and other adversarial tasks. The ISCA ensures IoT devices fulfill AARA safety and security criteria. Each algorithm prioritizes tracking, updating, and assessing to keep up with the ever-changing risk situation. The recommended solution is more accurate, finds more threats, reduces false positives, is scalable, and is simpler to set up than current defense solutions.

关键词： Technological innovation Heuristic algorithms Computational modeling Prediction algorithms Robustness Blockchains Internet of Things

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A Configurable Pruning Gaussian Image Filter for Energy-Efficient Edge Detection

A Configurable Pruning Gaussian Image Filter for Energy-Effi...

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IEEE International conference on Electronics, Circuits and Systems

作者： Leonardo Bandeira Soares Eduardo Antonio Cesar da Costa Sergio Bampi Federal Institute of Technology of Rio Grande do Sul (IFRS) Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil Graduate Program on Microelectronics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This paper presents a new configurable pruning Gaussian image filter CMOS architecture to address energy efficiency requirements regarding edge detection applications. Low-energy consumption is key for Internet of Things (IoT) devices. Many emerging IoT applications rely on cameras to extract video or image features by running power-hungry computer vision algorithms. The Gaussian image filter is one of the most compute intensive tasks for pre-processing edge detection techniques which are widely adopted in the computer vision domain. Therefore, our proposed 2D Gaussian filter architecture enables: i) a low power and low area overhead runtime configuration scheme based on clock gating technique to prune the Gaussian filter (GF) window size, and ii) run-time capability to balance the tradeoff between edge detection quality and energy efficiency. Our proposed configurable architecture is synthesized and mapped onto 45 nm technology for an ASIC implementation. Results show that for 6 different run-time profiles our proposed configurable architecture provides power dissipation reduction of up to 64% with multiple levels of edge detection quality, which is assessed by considering the performance conformance metric.

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Real-time Settings and Their Transformation through Distributed Intelligence Communication Technologies

Real-time Settings and Their Transformation through Distribu...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Manjunatha M Hari Krishna Vijay Kumar Rai Navdeep Singh Ashwani Kumar Mohammed I. Habelalmateen Department of Mechanical Engineering New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computer Science & Engineering IES College of Technology Bhopal M. P India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh The Islamic university Najaf Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Modern real-time settings and dispersed intelligence communication technologies require new solutions to improve efficiency, flexibility, and scalability. This research introduces dynamic edge-cloud orchestration for real-time intelligence. The approach uses the Dynamic Task Allocation Algorithm (DTAA), the Predictive Resource Scaling Algorithm (PRSA), and the Adaptive Communication Routing Algorithm. DTAA distributes work between edge devices and the cloud based on priority, processing time, and resources. PRSA estimates future resource needs, making edge and cloud scaling easier. ACRA adjusts communication routes based on latency, job priority, and network circumstances. We use numbers to compare the new strategy to six popular ones. These include SDA, TFA, RBI, FBA, and PRT. The proposed option outperforms the others in latency, scalability, security, reliability, freedom, and resource utilization. Due to its comprehensive framework for smart task assignment, resource management, and communication enhancement, DECO-RTI is a major advance in real-time settings. The approach can adapt to changing situations and automatically scale up resources, making it ahead of the curve for distributed intelligent communication systems.

关键词： Heuristic algorithms Scalability Computer network reliability Prediction algorithms Routing Dynamic scheduling Real-time systems

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Time Slicing in Mobile TV Broadcast Networks with Arbitrary Channel Bit Rates

Time Slicing in Mobile TV Broadcast Networks with Arbitrary ...

引用

IEEE Annual Joint conference: INFOCOM, IEEE Computer and Communications Societies

作者： C.-H. Hsu M. Hefeeda School of Computing Science Simon Fraser University Surrey BC Canada

ISBN: (纸本)9781424435128

Mobile TV networks have received significant attention from the industry and academia, as they have already been deployed in several countries and their expected market potential is huge. In such networks, a base station broadcasts TV channels in bursts with bit rates much higher than the encoding bit rates of the videos. This enables mobile receivers to receive a burst of traffic and then turn off their receiving circuit till the next burst to conserve energy. The base station needs to construct a transmission schedule for all bursts of different TV channels. Constructing optimal (in terms of energy saving) transmission schedules has been shown to be an NP-complete problem when the TV channels are encoded at arbitrary bit rates. In this paper, we propose a near-optimal approximation algorithm to solve this problem. We prove the correctness of the proposed algorithm and derive its approximation factor. We also conduct extensive evaluation of our algorithm using real implementation in a mobile TV testbed and simulations. Our experimental and simulation results show that the proposed algorithm: (i) is practical and produces correct burst schedules, (ii) achieves near-optimal energy saving for mobile devices, and (iii) runs efficiently in real time.

关键词： Mobile TV TV broadcasting Bit rate Job shop scheduling Base stations Approximation algorithms Videos Telecommunication traffic Circuits NP-complete problem

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Distributed Relay Selection in Presence of Dynamic Obstacles in Millimeter Wave D2D Communication

Distributed Relay Selection in Presence of Dynamic Obstacles...

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IEEE International conference on Communications (ICC)

作者： Durgesh Singh Arpan Chattopadhyay Sasthi C. Ghosh Advanced Computing& Microelectronics Unit Indian Statistical Institute Kolkata India Department of Electrical Engineering Indian Institute of Technology Delhi

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses and requires almost a line of sight (LOS) communication path. D2D channel condition is local to devices/user equipments (UEs) and hence is not directly visible to the base station (BS). Thus quality of the D2D channel needs to be propagated to BS by UEs which may incur some delay. Hence the solution provided by BS to UEs using this gathered channel information might become less useful to establish communication due to moving obstacles. These types of obstacles might not be known in advance and hence may cause unpredictable fluctuations to the D2D channel quality. Hence we seek to learn the D2D channels using the finite horizon partially observable Markov decision process (POMDP) framework to model the uncertainty in such kind of network environments with dynamic obstacles. The objective is to minimize delay when channel quality deteriorates, by making UEs choose locally the best possible decision between i) to continue on the current relay link on which communication is taking place or ii) to switch to another good relay by exploring other possible UEs in its locality. We derive an optimal threshold policy which tells the UE to take appropriate decision locally. Later, we give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgement failures, based on which UE make appropriate decision locally. Through extensive simulation, we demonstrate that our approach outperforms recent algorithms.

关键词： Relays Device-to-device communication Packet loss Delays Uncertainty Switches

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Exploring the Integration of Wearable Sensor Technologies in Professional Sports for Enhanced Athletic Performance and Injury Prevention

Exploring the Integration of Wearable Sensor Technologies in...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Kilaru Aswini Deepak Patidar Uma Reddy Amandeep Nagpal Mohamed I. Habelalmateen Praveen Institute of Aeronautical Engineering Dundigal Hyderabad Department of Electronics & Communication Engineering IES College Of Technology Bhopal M. P. India Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India The Islamic university Najaf Iraq Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The escalating demand for wearable technologies coupled with the pressing need for environmentally conscious solutions has spurred innovative methodologies. This study delves into the symbiotic relationship between biomimetic designs and advanced deep learning, envisioning a paradigm shift in the landscape of next-generation wearables. This project meticulously integrates biomimetic design with cutting-edge deep learning. Biomimicry improves wearable technology by taking ideas from nature. Strong deep learning algorithms based on artificial neural networks provide predictive analytics and user adaptation. To explore biomimetic setups and user activities in a controlled setting, many datasets were collected. Across several success metrics, the proposed method performs better. The accuracy increases greatly, demonstrating how effectively biomimetic concepts and deep learning techniques work together to improve accuracy and reliability. As smart technology evolves, this study ensures that new technologies are cutting-edge and environmentally friendly. Biomimetic designs and deep learning have revolutionized sustainable technologies. It promotes a more compassionate and eco-friendly future.

关键词： Deep learning Training Technological innovation Accuracy Biomimetics Wearable devices Intelligent sensors

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Versatile Bezier Bends for Silicon photonics

Versatile Bezier Bends for Silicon Photonics

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conference on Lasers and Electro-Optics Pacific Rim

作者： Thomas Y. L. Ang Jun Rong Ong Soon Thor Lim Ching Eng Png X. Guo Hong Wang Electronics and Photonics Department Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore Optic2Connect Pte. Ltd. 79 Ayer Rajah Crescent #01-14 Ayer Rajah Industrial Estate Singapore 139955 Singapore Silicon Technologies – Centre of Excellence Nanyang Technological University 50 Nanyang Avenue S1-B2b-15 Singapore 639798

ISBN: (纸本)9781509062904

We report on the design and fabrication of silicon Bezier bends for the communication wavelength of 1550 nm. By varying the Bezier parameter B, the bend losses can be controlled for different bend radius. At a bend radius of 2 μm, bend losses of 0.0652 to 0.269 dB per 180° bend is experimentally demonstrated for the TE polarization using silicon channel waveguide with width 500nm, and height 220nm for B = 0.2-0.45.

关键词： Bezier bends Silicon photonics

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