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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning 6

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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6th international conference on Next Generation data-Driven Networks, NGDN 2024

作者： Yang, Ruiyu Tang, Qi Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined Networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance. © 2024 ieee.

关键词： Reinforcement learning

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cognitive Detection of Anomalies in Autonomous In-Vehicle Network Communication

Cognitive Detection of Anomalies in Autonomous In-Vehicle Ne...

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2024 international conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Firasta, Suzen Srivastava, Yash Rahul Rao, Vidya Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798350363289

The research addresses the critical issue of detecting anomalies in autonomous in-vehicle network communication, focusing on the vulnerability of infotainment systems to denial-of-service attacks. The aim is to explore and compare traditional Machine Learning (ML) and advanced Deep Learning (DL) models for the detection of DoS attacks within Controller Area Network (CAN) communications. By evaluating the performance of various ML and DL algorithms, including Decision Tree, Logistic Regression, RandomForest, Feed Forward Neural Networks, and LSTM architectures, the study aims to enhance intrusion detection capabilities in vehicular communication systems. The significance of the research lies in identifying the advantages of DL methods in capturing complex patterns and temporal dependencies within the CAN network, thus contributing to fortifying the security of automotive networks against evolving threats. The results demonstrate the efficacy of the proposed models in detecting and mitigating DoS attacks, with DL methods showing promise in automatic learning and adaptability to intricate network structures. This study provides valuable insights for future research endeavors aimed at enhancing the cybersecurity of autonomous vehicles. © 2024 ieee.

关键词： Network intrusion

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Multi-Task Learning Network Optimization Based on Weight Adaptive 11

Multi-Task Learning Network Optimization Based on Weight Ada...

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11th ieee international conference on Behavioural and Social computing, BESC 2024

作者： Cao, Zizhuo Li, Yidong Ding, Chuntao Zhang, Zhuxi School of Computer Science and Technology Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing100044 China General Logistics Information Center Beijing100036 China

ISBN: (纸本)9798331531904

Multi-task learning has emerged as a significant topic in artificial intelligence research, where a singular network model performs numerous tasks. This approach simultaneously processes multiple related tasks and shared knowledge, enhancing model generalization while increasing efficiency. This methodology provides innovative solutions to complex real-world problems. However, the single-model-based approach for multi-task learning suffers from inter-task interference in practice. Therefore, the exploration of more efficient multi-task learning strategies, aimed at balancing the synergy and conflict among tasks and minimizing the dependence on computational resources, is pivotal for the field's future progress. We propose a strategy that autonomously adjusts both the parameters and structures of the model to alleviate gradient interference in multi-task learning. This method includes integrating a lightweight, weight-adaptive module that enhances the network's ability to process tasks by optimally balancing parameter sharing and isolation. This adaptation enables the model to share common features across tasks while allocating distinct spaces for each task, thereby reducing interference. Our extensive experimental validation indicates that our framework surpasses other multi-task learning approaches, achieving joint optimization of tasks more effectively. This enhancement not only bolsters performance but also maintains an equilibrium between accuracy and inference speed. © 2024 ieee.

关键词： Multi-task learning

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A Novel Scalable Cloud-enabled Spectrum Sensing Architecture 17

A Novel Scalable Cloud-enabled Spectrum Sensing Architecture

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17th international conference on Development and Application Systems, DAS 2024

作者： Mutescu, Partemie-Marian Calinciuc, Alin Popa, Valentin Schipor, Ovidiu-Andrei Interdisciplinary Cloud and Big Data Center Ștefan cel Mare University of Suceava Suceava Romania ASSIST Software Suceava Romania Computers Electronics and Automation Department Stefan Cel Mare University of Suceava Suceava Romania

ISBN: (纸本)9798350349290

Spectrum Sensing techniques have evolved significantly, transitioning from traditional approaches to more complex Machine Learning and Deep Learning based approaches. This evolution has led to enhanced performance capabilities but has also introduced substantial computational demands, which in turn have constrained the scalability of large-scale deployments due to associated costs. Nevertheless, the advent of Cloud computing offers a promising solution by enabling the centralized processing of data at the cloud level, thereby reducing the computational load on field-level sensors. This shift facilitates cost-effective deployment and enhances the scalability of spectrum sensing applications. In this study, we introduce and evaluate a Cloud-Enabled Spectrum Sensing architecture, with a specific focus on the computational and data transmission requirements from the perspective of the sensing node. We explore and compare two data formats for radio signal processing: I/Q samples and FFT series. Our evaluation covers the computational demands for processing and transmitting these data formats to a Cloud data Fusion Center across a range of radio spectrum bandwidths. Moreover, we examine the latencies involved in data transfer. Our analysis reveals that the FFT series data format offers considerable advantages for this architecture, achieving an optimal balance between computational requirements and network load. Specifically, for the recording and transmission of a 30 MHz wide radio band using the TCP protocol, the FFT format requires only 0.3 GFLOPS for processing and 6.7 MB/s for data transfer. These findings underscore the potential of the FFT series as a highly efficient radio signal representation format for Cloud-Enabled Spectrum Sensing architectures, promising significant improvements in scalability and operational efficiency. ©2024 ieee.

关键词： Cloud computing

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Deep reinforcement learning applied to ORIS-aided Downlink MU-MIMO VLC Systems

Deep reinforcement learning applied to ORIS-aided Downlink M...

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2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Jbari, Mohamed El Moussaoui, Mohamed de Figueiredo, Felipe A.P. Chakkor, Saad Azroumahli, Chaimae Bouazizi, Omar LabTIC ENSAT of Tangier Abdelmalek Essaadi University Tangier Morocco SIC Dept. LabTIC ENSA of Tangier Abdelmalek Essaadi University Tangier Morocco Santa Rita do Sapucaí Minas Gerais Brazil Mathematics data sciences and emerging technologies FST Settat Hassan 1st University Morocco IDS Team ENSA of Tangier Abdelmalek Essaadi University Tangier Morocco

ISBN: (纸本)9798331533137

Optical Reconfigurable Intelligent Surfaces (ORIS) has recently gained attention as a promising technology for enhancing the performance of multiuser multiple-input multiple-output (MIMO) visible light communication (VLC) systems. By integrating ORIS with advanced optimization techniques, such as Deep Reinforcement Learning (DRL), significant improvements can be achieved regarding spectral efficiency, energy efficiency (EE), and overall system capacity. This paper proposes a novel approach that employs DRL to optimize both the transmit BF matrix at the transmitter and the phase shift configuration at the ORIS. The proposed method demonstrates significant improvements over conventional optimization algorithms, achieving a performance increase of up to 25% in efficiency during extensive simulations conducted over 800 episodes. This enhanced solution provides greater adaptability for complex indoor MU-MIMO VLC environments, showcasing its effectiveness compared to traditional approaches. ©2024 ieee.

关键词： Deep reinforcement learning

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Protecting data Integrity from Cyber Attacks with an Effective ML-Based Framework

Protecting Data Integrity from Cyber Attacks with an Effecti...

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2024 ieee international conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Narne, Harish UiPath Inc DallasTX United States

ISBN: (纸本)9798331508432

In cybersecurity, machine learning is becoming more and more common. Machine learning has many potential applications in cybersecurity, one of which is to replace human malware detection methods. Making them more actionable, scalable, and effective is the aim. In the subject of cybersecurity, there are machine learning difficulties that require effective theoretical and systematic solutions. Numerous statistical and machine learning methods have proven effective in lessening the damage caused by cyberattacks. Some examples of these methods are Bayesian classification, deep learning, and support vector machines. In order to thwart such attacks, intelligent security system design must be able to spot previously unseen insights and patterns in network data. The next step in protecting against these types of attacks is to construct a data-driven machine learning model. Because of the increased development and deployment of complex analytics solutions, machine learning (ML) algorithms are being exploited by novel theft attempts to achieve a high success rate and do significant harm. Governments, organisations, and people should prioritise ML-based theft attacks due to the urgency and difficulty of detecting and protecting against them. This review covers the most up-to-date information on this emerging attack type and the defences put in place to stop it. This article examines the ML-based theft threat from the vantage point of three types of controlled information: authentication data, data pertaining to ML models, and data pertaining to restricted user behaviours. This paper reviews recent research in order to draw conclusions on the strengths and weaknesses of ML-based theft attacks, as well as their potential future applications. In addition, steps to build robust defences against discovery, disruption, and isolation are suggested. © 2024 ieee.

关键词： Adversarial machine learning

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The Impact of 5G on Real-Time IoT data Processing: Exploring Challenges and Innovative Solutions

The Impact of 5G on Real-Time IoT Data Processing: Exploring...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Pradeep, S. Venkatesh, K. Bhagavatula, Sritika Roy, Rincy Bhargavi, Polampally Gupta, Ankur Dept. of CSE Telangana Secunderabad India Dept. of CSE-IoT Telangana Secunderabad India Vaish College of Engineering Dept. of CSE Haryana Rohtak India

ISBN: (纸本)9798350378092

The introduction of 5G technology is a significant advancement in the processing of real-time data from IoT, offering speed, connection, and unprecedented data management capabilities. This study explores the impact of 5G on real-time IoT data processing, focusing on its potential and challenges. The ultra-low latency and high bandwidth characteristics of 5G enable real-time applications to handle data with great efficiency, improving fields like smart cities, healthcare, industrial automation, and autonomous cars. This improved connection allows for more complex data analytics and informed decisions, driving innovation across various industries. However, the integration of 5G into IoT systems presents several challenges, including increasing cybersecurity risks due to the growing network of connected devices, the need for strong privacy controls and compliance with regulations like GDPR and CCPA, significant network changes for 5G infrastructure implementation, and the difficulty in establishing constant real-time performance due to network conditions and device capabilities. Interoperability issues across various IoT devices further complicate the integration process, necessitating the introduction of defined protocols for smooth functioning. As the number of connected devices increases, managing network congestion and resource allocation becomes increasingly difficult, necessitating more sophisticated resource management solutions. This study aims to provide methods for maximizing the adoption of 5G in real-time IoT applications by investigating security technologies, infrastructure development, and standardization initiatives. Ultimately, 5G technology will revolutionize IoT data processing, but it is crucial to overcome these obstacles to fully realize its potential. © 2024 ieee.

关键词： 5G mobile communication systems

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A Speech Emotion Recognition System for Improved Communication and Enhancing Human-Machine Interaction

A Speech Emotion Recognition System for Improved Communicati...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Deshpande, Manasi Savadekar, Priyanka Marathwada Mitramandal's College of Engineering Savitribai Phule Pune University Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350376470

In today's digital world of remote communication, speech remains one of the most natural ways for human beings to express themselves. However, effective communication involves more than just words;emotions playa crucial role in conveying meaning and intent. Therefore, the detection and analysis of emotions in speech have become essential for understanding and improving communication in various applications. Emotion detection is a complex task due to the subjective nature of emotions. To address this challenge, this paper presents a Speech Emotion Recognition (SER) system, which comprises a set of methodologies designed to process and classify speech signals with the goal of detecting underlying emotions. To build and evaluate the SER system, we utilize the RA VDESS and TESS dataset, which contains recorded speech data with annotated emotions. The first step involves augmenting the audio data to enhance its diversity and robustness. Subsequently, we employ feature extraction methods and pre-processing techniques to derive meaningful acoustic features from the audio recordings. The processed data is then fed into machine learning models to perform emotion classification. These models are trained on the extracted features and learn to recognize patterns associated with different emotions. The system can be employed in call center environments to analyze caller-agent conversations, helping businesses gauge customer satisfaction and improve service quality. Additionally, it can find applications in improving virtual voice-based assistants, enabling them to understand human emotions and respond accordingly. Overall, this research aims to advance emotion detection in speech and contribute to the development of more emotionally intelligent communication technologies that can enhance human-machine interactions andre mote conversations. © 2024 ieee.

关键词： Customer satisfaction

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Human Brain Stroke Prediction using Machine Learning Methods with Synthetic Minority Oversampling Approach 7

Human Brain Stroke Prediction using Machine Learning Methods...

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7th international Women in data Science conference at Prince Sultan University (WiDS-PSU)

作者： Mujahid, Muhammad Ayesha, Noor Azar, Ahmad Taher Saba, Tanzila Haider, Zeeshan Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou 450001 Henan Peoples R China Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Prince Sultan Univ Automated Syst & Soft Comp Lab ASSCL Riyadh 11586 Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

ISBN: (纸本)9798350395839;9798350395846

The brain is considered to be the principal anatomical component of the human body. A stroke is a medical condition defined by cerebral injury that occurs as a consequence of blood vessel rupture within the brain. Symptoms might become apparent when there is an interruption in the brain circulation of blood and other vital chemicals. Stroke is widely acknowledged as a significant medical disorder because to its elevated mortality rates, enduring cognitive impairments, and concomitant comorbidities. The timely recognition of the numerous prodromal manifestations linked to a stroke has the capacity to attenuate its magnitude. The primary objective of this study is to utilize machine learning methodologies in order to predict the likelihood of an early-stage occurrence of a cerebral stroke. In order to assess the efficacy of the algorithm, a dataset for stroke prediction was acquired via the Kaggle platform. Various preprocessing techniques are applied to normalize and clean the dataset for stroke prediction. This research utilized many classification models including Random Forest, Ada Boost, LightGBM,, Decision Tree (DT), Logistic Regression (LR),and Support vector machine (SVM) to perform classification tasks. The Random Forest classifier has a higher level of classification accuracy in comparison to alternative machine learning classifiers, obtaining an impressive accuracy rate of 99.45%. The results of the investigation revealed that machine learning approaches demonstrated a higher level of performance in comparison to alternative methods.

关键词： Brain stroke Prediction Random Forest Stroke data SMOTE Health Risks

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A Single Shot Multi-Head Gender, Age, and Landmarks Detection using Shared Convolution Features 29

A Single Shot Multi-Head Gender, Age, and Landmarks Detectio...

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29th international conference on Automation and computing (ICAC)

作者： Khan, Gulraiz Pimbblet, Kevin Wertheim, Kenneth Ahmed, Waqas Univ Hull Data Sci AI & Modelling Ctr DAIM Kingston Upon Hull N Humberside England

ISBN: (纸本)9798350360882;9798350360899

Considering the face as a vital and most informative portion of the human body, it reflects different high-level information about an individual. This high-level information includes Age, Gender, and Emotion. Facial muscles' shape and movement can be the best descriptors for the automatic extraction of these high-level facial features. Detection of these high-level features has applications in different areas including entertainment, surveillance, multimedia, and educational training. However, with the varying nature of these features, it becomes difficult to capture one class with the variability of other classes. This article presents a lightweight heterogeneous neural network with one shared backbone and three network heads to predict multiple face features: landmarks, age, and gender. The proposed system (MultiHeadCNN) captures these high-level facial features in the wild with extreme face pose, occlusions, and lightening conditions. The system is capable of predicting one type of feature with different variability of other types: predicting gender for different age groups and vice versa. The system is tested on comprehensive (UTKFace) and complex (Adience) datasets with varying age, gender, pose, and lightening conditions. The experiment shows promising results in terms of accuracy, with results for age and gender detection on the UTKFace and Adience datasets being 99.9%, 99.7%, 90.3%, and 61.7%, respectively. Furthermore, the parallel inference speed is 20 frames per second.

关键词： Convolution Neural Network Convolution Feature Sharing ResNet Backbone Network

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