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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Khaleel, Thura Jabbar Shiltagh, Nadia Adnan Iraqi Commission for Computers and Information/Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9789819708918

Protecting software-defined networking (SDN) against cyber-attacks has become crucial in an expanding digital threat environment. Distributed Denial-of-Service (DDoS) attacks are risky since they may seriously interrupt operations. To mitigate these risks, this study introduces an anomaly detection method that utilizes a hybrid convolutional and short-term memory (CNN-LSTM) deep neural network. This model merges the CNN's ability to automatically extract spatial features with the LSTM's proficiency in sequence modeling, thereby enhancing the detection of anomalies in network traffic metadata. The model also integrates an autoencoder structure to facilitate representation learning and reduce dimensionality. The model's effectiveness was tested using publicly accessible SDN datasets, and the results were remarkable. The model identified DDoS attacks with an accuracy rate of over 99%, surpassing the performance of previous shallow learning models. Moreover, the model proved highly adaptable, successfully detecting attacks across various data samples. This deep learning-based detection system is a significant advancement, providing precise and efficient analytics that bolster real-time cybersecurity monitoring. However, it's crucial to continue research in deployment, interpretability, and the potential of combinatorial learning with other advanced technologies. We can only fully harness the great potential of artificial intelligence for adequate cyber protection by looking into these areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Denial-of-service attack

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IoT Based Designing of Secure Data Storage System in Distributed Cloud System with Big Data using Cryptography Algorithm 11

IoT Based Designing of Secure Data Storage System in Distrib...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Shukla, Ravi Shankar College of Computing and Informatics Saudi Electronic University Department of Computer Science Saudi Arabia

ISBN: (纸本)9781665487320

The cloud system plays a versatile role in the storage of vast data for future references. This includes the delivery of the computing sources that includes the servers with storage, various databases, networking system and software analysis. The web based services provides various usages in versatile forms. The cloud computing helps in numerous pathways for the web based service networks. The security of the data and the privacy constraints are the important aspects involved in the cloud computing techniques. This includes the safeguarding the sensitive data with higher precision. This is accompanied with the intelligent cryptographic algorithm to obtain secure data in the cloud system. This helps to save the data through several data processing applications accompanied with the cryptography algorithm. The cryptography algorithm are enabled to obtain encrypt data so that to have secure communication links in the networking system. This helps in the separation of data with the prescribed files in the distributed cloud networks. The operational time is secured by the classification of data in the form of data packets. © 2022 IEEE.

关键词： Distributed cloud

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Diversified and compatible web APIs recommendation based on game theory in IoT

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Digital Communications and Networks 2024年第4期10卷 1198-1209页

作者： Wenwen Gong Huiping Wu Xiaokang Wang Xuyun Zhang Yawei Wang Yifei Chen Mohammad R.Khosravi College of Information and Electrical Engineering China Agricultural UniversityBeijing100000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computer Science St.Francis Xavier UniversityAntigonishNSCanada Department of Computing Macquarie UniversitySydneyNSW2122Australia Department of Computer Engineering Persian Gulf UniversityBushehr***Iran Department of Electrical and Electronic Engineering Shiraz University of TechnologyShiraz71557-13876Iran

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

关键词： Internet of things Web APIs recommendation Game theory Diversity and compatibility

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On the Performance Evaluation of Mobility Model-Based GPSR Routing Protocol in Flying Ad hoc Networks 6

On the Performance Evaluation of Mobility Model-Based GPSR R...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Bezziane, Mohamed Ben Sahraoui, Yesin Brik, Bouziane Mekkas, Lina Bougeurra, Sara Khaldi, Anouar University Kasdi Merbah Computer Science And Information Technologies Department Ouargla Algeria National Higher School of Artificial Intelligence Computer Science Algiers Algeria University of Sharjah College of Computing And Informatics Computer Science Department Sharjah United Arab Emirates

ISBN: (纸本)9798350350265

In recent times, we have witnessed the rapid and remarkable evolution of communication technologies, particularly in the domain of Flying Ad Hoc Networks (FANETs). This evolution has necessitated a thorough evaluation and significant enhancement of routing protocols to improve the overall performance of the system. To address the challenge of efficiently routing data in FANETs, where conventional methods may fall short during dynamic scenarios, the need for an effective framework becomes paramount in addressing these challenges. This paper aims to optimize routing efficiency in the context of FANETs, evaluating the performance of Greedy Perimeter Stateless Routing (GPSR) routing protocol under various mobility models, including Random Waypoint Mobility (RWPM), Mass Mobility, and Gauss Markov mobility models, to uncover their strengths and limitations. The simulation results reveal notable variations in performance indicators, including End-to-End delay, Packet Delivery Ratio, and Loss rate across various mobility models. However, Mass Mobility and Gauss-Markov Mobility exhibited consistent increases in Energy Consumption as the UAV count rose. On the other hand, Random Way Point Mobility demonstrated higher energy consumption compared to the former models. These findings offer valuable insights for selecting the most suitable mobility model for specific applications, and improving routing protocols, thereby advancing the reliability and effectiveness of Flying Ad Hoc environments. © 2024 IEEE.

关键词： Routing protocols

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Wireless Sensor Networks: Revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. Ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats. © 2024 IEEE.

关键词： Terrorism

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

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6th International Conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Generative Adversarial Networks (GANs) for Artistic Style Transfer in Images

Generative Adversarial Networks (GANs) for Artistic Style Tr...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jamalpur, Bhavana Kiruthiga, S. Ranjitha, B. Lefty Joyson, J. Jayaram, K. Soundar, K. Ruba S R University Department of Computer Science and Artificial Intelligence India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Mohan Babu University Department of Mathematics India Jp College of Engineering Department of Computer Science and Engineering Ayikudi India College of Engineering Adoor Department of Computer Science and Engineering Pathanamthitta India Department of Computer Science and Engineering Sivakasi India

ISBN: (纸本)9798350375442

Many in computer vision and computational imaging consider Generative Adversarial Networks (GANs) a big advance. Style transfer converts semantic visual content into creative styles. Recently, GANs for style transfer have gained popularity. Adversarial training of GANs to create realistic counterfeits has yielded encouraging results. High-quality style transfer results have been achieved using this method. Mode collapse is a common problem in classical GANs. This causes unsteady training and poor style transmission. Additionally, the GAN generator only supports one style. Therefore, numerous GANs must be trained to allow users to shift styles. This study examines artistic style transfer in photographs using depth extraction generative adversarial network (DE-GAN). This research aims to improve photo aesthetics by adding famous artworks' distinctive qualities. This study investigates DE-GANs' mechanics and how they modify text while keeping a style's creative qualities. By studying adversarial training, this work hopes to understand how DE-GAN achieves this dual goal. This research analyzes DE-GAN-based style transfer models to evaluate their effectiveness, identify their limits, and recommend areas for improvement. The experiments show that DE-GAN creates realistic and attractive stylized visuals. This study also discusses DE-GAN's practical applications, problems, and future possibilities in artistic style transfer. © 2024 IEEE.

关键词： Generative adversarial networks

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of computer Science 2025年第2期8卷 303-333页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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Exploring Network Dynamics: Community Detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Andreas Vonitsanos, Gerasimos Karamitsos, Ioannis Al-Hussaeni, Khalil Ionian University Department of Informatics Corfu Greece University of Patras Computer Engineering and Informatics Department Patras Greece Rochester Institution of Technology Dubai United Arab Emirates Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates

ISBN: (纸本)9798350362480

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network's structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health. © 2024 IEEE.

关键词： Tweets

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