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intelligent Service Oriented Architecture (SOA) for State-of-the-Art IoT-DDoS Defense and Research Challenges

Intelligent Service Oriented Architecture (SOA) for State-of...

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2023 international conference on IoT, Communication and automation technology, ICICAT 2023

作者： Snehi, Manish Verma, Jyoti Kansal, Isha Popli, Renu Kumar, Rajeev Saini, Gurpreet Singh Punjabi University Department of Computer Science and Engineering Punjab Patiala India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Electronics and Electrical Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350302820

The Internet of Things (IoT) is rapidly surfacing that spans all spheres of life. However, IoT devices are less secure due to limited computational capabilities, making them easy for malware for various attacks. Distributed Denial of Service (DDoS) attacks focused on the Internet of Things are among the most devastating attacks that have gained attention as the number of intelligent devices is ever increasing. Several security protocols and defensive solutions have been developed to improve the security scenario in IoT networks, the frameworks are tightly coupled. The heterogeneity of network traffic is frequently increasing, making it challenging to keep the defense solutions updated for zero-day attacks. Additionally, the tight coupling makes scaling, upgrading, and migrating defense systems challenging. The state-of-the-art solutions cannot address the increased need for on-demand services and security concerns inherent in today's Internet. This article presented a novel service-oriented architecture (SOA) for security frameworks that can be supplied to tenants and customers of tenants to mitigate these attacks. The objective of service-oriented architecture is to organise application components into a system of loosely connected services in order to build adaptable, dynamical operational procedures and adaptable applications that transcends enterprises and computing systems. The proposed Security conceptual paradigm SOA employs an SDN-based security mechanism to detect and mitigate DDoS attacks that occur in IoT computer networks. As interfaces to services for defensive systems, programming networks enable the implementation of security. © 2023 ieee.

关键词： Denial-of-service attack

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Deep Reinforcement Learning in Mixture of Experts control System for Blind Wheeled-Legged Quadrupedal Locomotion

Deep Reinforcement Learning in Mixture of Experts Control Sy...

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2024 international conference on Advanced Robotics and intelligent systems, ARIS 2024

作者： Zhang, William Wang, Ke Harvard University School of Engineering and Applied Sciences AllstonMA02134 United States Dyson Technology Ltd MalmesburySN160RP United Kingdom

ISBN: (纸本)9798350362572

The advances in wheeled-legged robots in recent years have led to their proliferation in human environments. However, these robots still face significant challenges regarding non-flat terrain. Stair climbing is particularly difficult for these robots and restricts their functionality in human facilities. We implemented a goal conditioned deep reinforcement learning algorithm to develop a position-based controller that allows Wheely, a wheeled-legged quadrupedal robot, to blindly climb stairs up to a record breaking 10 cm in height or 33% of Wheely's maximum body height. By training the reinforcement learning algorithm on a single environment each time, we create specialized policies that excel at their own specific terrain type. Through the independent training, we create a controller with a mixture of experts architecture that does not need to compromise between different environments, leading to a more optimal policy for each scenario. To maintain robustness, a selector neural network selects the policy based on past observations, allowing the robot to function independently in dynamic environments. © 2024 ieee.

关键词： Deep reinforcement learning

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PrivacySphere: Privacy-Preserving Smart Spaces 6

PrivacySphere: Privacy-Preserving Smart Spaces

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6th ieee international conference on Trust, Privacy and Security in intelligent systems, and Applications, TPS-ISA 2024

作者： Farrukh, Habiba Lahjouji, Nada Mehrotra, Sharad Nawab, Faisal Rousseau, Julie Sharma, Shantanu Venkatasubramanian, Nalini Yus, Roberto University of California Irvine United States New Jersey Institute of Technology United States University of Maryland United States

ISBN: (纸本)9798350386745

In smart spaces, data flows from sensors through data processing pipelines that interpret and enrich it to realize the needs of diverse applications. Smart space data may also be stored for future analysis and processing to implement new functionalities and learn correlations that can help improve deployed applications. Data processing may be performed at the edge (on sensors or at trusted local servers) or may be relegated to the (possibly untrusted) public cloud. This paper presents PrivacySphere, our vision towards a plug-n-play framework to integrate and test a variety of Privacy Enhancing Technologies (PETs) in smart spaces. PrivacySphere will support mechanisms (with appropriate APIs) to control when data is collected and from which sensors;in what format the data is exposed to devices/machines;and to whom (i.e., individuals/entities). Using PrivacySphere, flow of data may be intercepted at any point of the execution to apply PETs (e.g., differential privacy, encryption, policy-based sharing). © 2024 ieee.

关键词： data privacy regulations privacy smart spaces

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Detection of Breast Cancer from Histopathological Images using Image Processing and Deep-Learning 3

Detection of Breast Cancer from Histopathological Images usi...

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3rd international conference on intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Thomas, Anusha Maria Adithya, G. Arunselvan, A.S. Karthik, R. Vellore Institute of Technology Electronics and Computer Engineering Chennai India Vellore Institute of Technology Centre for Cyber Physical Systems School of Electronics Engineering Chennai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Breast cancer is the most commonly occurring cancer in women. Cancer patients frequently develop metastasis, which is responsible for more than 90% of their deaths. The mortality rate will be significantly reduced if it is identified and treated in an early phase. The categorization of cancer cells is critical for medical diagnosis, tailored therapy, and disease prevention. Classifying various types of these cells with great precision has remained a difficult issue. Deep learning has emerged as a significant tool for such challenging tasks in the fields of biology and medicine. In this research, we propose a novel model that throws light on image processing and deep learning for breast cancer classification from histopathological images. The proposed Vision transformer model outperforms the state-of-the-art convolution neural networks in classifying the breast cancer cell with an accuracy of 96%. © 2022 ieee.

关键词： Diseases

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Walmart Sales Forecasting Using Time Series Analysis

Walmart Sales Forecasting Using Time Series Analysis

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2024 international conference on Advances in Data Engineering and intelligent Computing systems, ADICS 2024

作者： Shrieenidhi, A.M. Ruba Roshini, S. Ranjana, P. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The field of retail analytics concentrates on Walmart's challenging issue of forecasting sales by utilizing advanced time series analysis. Walmart is attempting to leverage time series models because it understands how important precise sales projections are for revenue optimization, inventory control, and customer pleasure. By the usage of EDA and Feature Engineering, which is capable of capturing underlying trends, temporal patterns, seasonality (Holiday, Weekly Sales, CPI), the impact of external factors (Prominent Holidays) and aiming to improve sales, optimize budgeting and resource allocation, inventory management, business planning is achieved. The principal aim is to provide Walmart a future forecasted sales using traditional time series model such ARIMA & SARIMA. Statistical metrics such as MAE, MSE, RMSE, WMAE have been adopted to evaluate the quality of the model. By refining sales forecasting through advanced time series models and meticulous evaluation, Walmart anticipates not only enhanced operational efficiency but also an elevated customer experience, reinforcing its position as a retail industry leader. © 2024 ieee.

关键词： Sales

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A Comprehensive Study of Artificial Neural Networks in Power System Stability 21

A Comprehensive Study of Artificial Neural Networks in Power...

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21st international Multi-conference on systems, Signals and Devices, SSD 2024

作者： Alhalim, Shaimaa Shukri Abd. Bahloul, Wissem Chtourou, Mohamed Derbel, Nabil University of Technology Department of Electrical Engineering Baghdad Iraq Sfax Tunisia

ISBN: (纸本)9798350374131

The stability of the power system is important for ensuring the power grid's reliable operation. Traditional stability analysis and stabilization methods have limitations in handling the complexity and nonlinearity of modern power systems. The combination of artificial neural network (ANN)based approaches holds particular promise for power system stability and addressing stabilization challenges. This paper aims to investigate the use of neural methods to improve the analysis and stabilization of complex power systems. Research will focus on neural network architectures and algorithms that can accurately predict and ensure the stability of power systems under various operating conditions. By using neural networks, the research aims to overcome the limitations of traditional methods and provide intelligent solutions to ensure the stability and the robustness of power systems in the face of challenges in the ongoing process. This comprehensive review provides in-depth insights into the applications, techniques, and advancements of ANN in power system stability analysis and control. The study includes ANN basic principles and appropriateness to model complex nonlinear relationships in dynamical systems and evaluate them, including feedforward. This study looks into how ANNs can be used to improve system stability and response to disturbances in areas like fault detection, dynamic modeling, control strategies, and stability assessment. © 2024 ieee.

关键词： Neural networks

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Insider Threat Detection Using Bidirectional Encoder Representations from Transformers and Optimized AdaBoost Classifier

Insider Threat Detection Using Bidirectional Encoder Represe...

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2024 international conference on Circuit, systems and Communication, ICCSC 2024

作者： Kumpf, Katarina Protic, Mihajlo Jovanovic, Luka Cajic, Miroslav Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia University 'Union Nikola Tesla' Faculty of Information Technology and Engineering Belgrade Serbia

ISBN: (纸本)9798350365306

cybersecurity plays an increasingly vital role in contemporary times, particularly as large companies face escalating threats and substantial losses, with ransomware attacks standing out as one of the more sophisticated forms of intrusion. In such attacks, hackers infiltrate systems, encrypt crucial company data, and then demand a ransom for data decryption or the restoration of control over the system. The application of natural language processing (NLP) emerges as a promising avenue, offering the potential to analyze attack patterns and recognize malicious intentions before actual attempts occur. Given the inherent specificity of each system, fine-tuning AI algorithms with special settings becomes imperative for optimal efficiency. This research delves into the synergy of NLP and robust classification algorithms to detect cyber attacks through website content analysis. Recognizing that classifier performance hinges on the judicious selection of hyperparameters, we explore a modified version of the recently introduced red fox algorithm (RFO). This adaptation is combined with bidirectional encoder representations from transformers to identify malicious intent in email content. The proposed approach undergoes testing on a real-world dataset, with the best models demonstrating an accuracy of 0.975822 suggesting viability. © 2024 ieee.

关键词： Natural language processing systems

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Quantization Tracking control Based on Disturbance Observer for the Unmanned Aerial Vehicles

Quantization Tracking Control Based on Disturbance Observer ...

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2023 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2023

作者： Chen, Xingru He, Xiuyu Wang, Jingyuan Li, Guang He, Wei School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education Beijing100083 China University of Manchester Manchester Department of Electrical and Electronic Engineering LondonM13 9PL United Kingdom

ISBN: (纸本)9798350309003

In this paper, we propose a quantized tracking control scheme for the Unmanned Aerial Vehicles with the external disturbances. A disturbance observer is proposed to handle the disturbances and a commanded filter backstepping technique is adopted to reduce the computational complexity. The error compensation signals are used to reduce the influence of filtering errors. The hysteresis uniform switching bounded quantization mechanism is introduced in the control channel to reduce the data transmission burden and resource usage as well. Finally, the effectiveness of the proposed method is verified by stability proof and numerical simulation. © 2023 ieee.

关键词： Error compensation

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Novel Approach for intelligent Fault Diagnosis in Electromechanical Drive systems Using an Attention-Based Residual 1D Convolutional Neural Network 4

Novel Approach for Intelligent Fault Diagnosis in Electromec...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Okonda, Jonathan Xu, Danping Kilundu, Bovic Shanghai University School of Mechatronic Engineering and Automation Shanghai China Department of Mechanical Engineering Brussels Belgium

ISBN: (纸本)9798331531225

Numerous existing deep learning-based fault diagnosis models, have been implemented for intelligent fault diagnosis in electromechanical drive systems (EMDS). However, these models often are designed to detect and diagnose single fault types at specific operating conditions. These models are frequently susceptible to overfitting, suffer from the vanishing gradient problem, and struggle with long-range feature dependencies. Such limitations significantly hinder their accuracy and reliability in diagnosing complex fault scenarios for EMDS under different operating conditions using noisy data. To address these limitations, we propose a novel deep learning-based fault diagnosis model named Attention-based Residual 1D Convolutional Neural Network (AR1D-CNN). This model is designed to effectively diagnose complex faults in EMDS across various load conditions using noisy and imbalanced vibration data. Experimental results demonstrate exceptional classification accuracies of 99.98% and 100.00% on the KAIST dataset for 0 and 2 Nm load conditions, respectively, and 99.31% on the AETWPSU broken rotor bar dataset. Notably, the AR1D-CNN outperforms existing state-of-the-art methods by approximately 4% on the AETWPSU dataset, highlighting its robustness and reliability. This research significantly enhances predictive maintenance strategies by enabling early and precise fault detection, ultimately improving the operational efficiency of EMDS. © 2024 ieee.

关键词： Convolutional neural networks

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Developing an Electroencephalogram Based Robotic Motion control System Using Brainwaves Enabled Signal Processing Technique

Developing an Electroencephalogram Based Robotic Motion Cont...

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2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Govindaraj, R. Ranjith, S. Deepthi, K. Babu, K. Kishore Bodkhe, Rajesh G. Amer, Ayman Saveetha School of Engineering Dept of Ece Chennai India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of Aimlds Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Energy Engineer zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

This work presents the development of an Electroencephalogram (EEG)-based robotic motion control system utilizing brainwave-enabled signal processing techniques. The system is designed to translate mental commands into real-time robotic actions, offering a non-invasive brain-computer interface (BCI) for users. The system involves EEG signal acquisition, preprocessing, feature extraction, and classification using machine learning algorithms, followed by robotic control via a real-time interface. Data was collected from 10 participants performing four mental tasks: move forward, turn left, turn right, and stop. The classification accuracy across these tasks ranged from 84% to 94%, with an average accuracy of 88%. Response times for translating mental commands into robotic actions varied between 205 milliseconds and 280 milliseconds, ensuring near real-time control. Testing under different environmental conditions (normal, noisy, fatigued) showed the system's robustness, although there was a slight drop in accuracy under noisy (80-85%) and fatigued (81-88%) conditions. User satisfaction scores indicated a positive overall experience with high ratings for ease of use and perceived accuracy. The results demonstrate the system's potential for applications in assistive technologies and rehabilitation robotics. © 2024 ieee.

关键词： Assistive technology

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