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Enhancing DDoS Detection in SDNs: Integrating AFSOA with BiLSTM for Real-Time Threat Management

Enhancing DDoS Detection in SDNs: Integrating AFSOA with BiL...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： C. Umarani Jyoti Metan Piyush Kumar Pareek Mahantesh Mathapati Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Bengaluru India Department of ISE Atria Institute of Technology Bengaluru India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Amruta Institute of Engineering & Management Sciences Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Software-defined networking (SDN) has emerged as a popular solution to the inherent difficulties of traditional dispersed networks. The primary focus of this work is the early identification of anomalous assaults. We investigate the efficacy of combining a Bidirectional Long Short-Term Memory (BiLSTM) network with an Artificial Fish Swarm Optimization Algorithm (AFSOA) to detect distributed Denial of Service (DDoS) attacks in an SDN environment. For dimensionality reduction, we combine information gain (IG) with principal component analysis (PCA), allowing for a more effective and economic analysis of complex, high-dimensional datasets. The AFSOA optimizes the BiLSTM model's parameters to improve its ability to detect patterns in network traffic indicative of DDoS attacks. The proposed solution leverages the programmable and dynamic nature of SDNs to build and test the detection system, enabling adaptive threat management in accuracy of 95.20%, precision of 97.64%, recall of 98.20%, and an F-measure of 98.67%, significantly outperforming existing methods. This approach opens up new possibilities for enhancing network security in the future.

关键词： Bidirectional long short term memory Telecommunication traffic Network security Denial-of-service attack Fish Real-time systems Particle swarm optimization Software defined networking computer crime Principal component analysis

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th international conference on Future networks and distributed systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Fog-enabled Intelligent Network Intrusion Detection Framework for Internet of Things Applications 13

Fog-enabled Intelligent Network Intrusion Detection Framewor...

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13th international conference on Cloud Computing, Data Science and Engineering, Confluence 2023

作者： Bebortta, Sujit Das, Saneev Kumar Chakravarty, Sujata Ravenshaw University Department of Computer Science Cuttack India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9781665462631

With the continual growth in Internet of Things (IoT), several concerns regarding the security and privacy of the connected IoT devices arise. Attacks like distributed Denial of Service (DDoS) and probing attacks cause specific damage to the underlying network infrastructure. In this paper, we propose an Equilibrium Optimization-based Artificial Neural Network (EO-ANN) Framework for detection of network attacks pertaining to IoT systems. The proposed approach leverages the dynamic mass balance technique to obtain the optimal number of features for training the ANN algorithm. To evaluate the effectiveness of the proposed EO-ANN model, its performance is compared with several baseline prediction models. Experimental findings demonstrate that the suggested approach has precision and recall values of 0.9389 and 0.9248, respectively, and an F1-score of 0.9371, which results in an overall accuracy of 93.4731%. In addition to these performance requirements, the model was implemented over the Fog computing platform and the corresponding metrics viz., delay and energy consumption were obtained towards execution of different workloads. Hence, the experimentations offered in this study show that the suggested EO-ANN model is effective and that it can be successfully validated for classifying network intrusion in densely deployed IoT networks. © 2023 IEEE.

关键词： Internet of things

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Multi-Modal Knowledge Graph System with Embedding Module

Multi-Modal Knowledge Graph System with Embedding Module

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Yi Sha Xinqiao Gu Huashu Kuang Qiang Li Qixin Zuo Department of Biology Medicine and Health The University of Manchester Great Manchester Manchester Beijing Zhongdian Puhua Information Technology Co. Ltd Beijing China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Recommendation system is to predict appropriate Point of Interest (POIs) which have not visited before the users provides its historical records of check-ins. The implementation of knowledge graph system resolves the heterogeneity in auxiliary data. Though, previous knowledge graph system about the POI recommendation makes the utilization of relationship among POIs and users for context data to recommendation system and neglected the deep relation taken attribute integration of POIs itself. Many researches developed the Knowledge Graphs (KGs) based system through leveraging the significant auxiliary data. Many researches ignored the difference of data types in multi-modal. In this article, developed the Multi-modal Knowledge Graph Embedding Module (MKGEM) for enhancing the recommender system through knowledge of multi-modal. In MKGEM, the multi-modal graph attention method is evaluated to data propagation and next used the resulted aggregation representation of embedding for knowledge graph system. The developed MKGEM showed effective performance while compared to existing techniques like Collaborative Knowledge base Embedding (CKE).

关键词： Knowledge based systems Collaboration Knowledge graphs computer networks computer security Recommender systems

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Circle Search Algorithm with Convolutional Neural Network for distributed Denial-of-Service Attack Detection in Software-Defined networks

Circle Search Algorithm with Convolutional Neural Network fo...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： S. Padmakala Hassan M. Al-Jawahry R. Palanivel Y.M. Mahaboob John Arelli Madhavi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE Mahendra College of Engineering Salem India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The distributed Denial-of-Service (DDoS) attack is a crucial cybersecurity threat for Software-Defined Network (SDN). However, DDoS in SDN severely affect network availability which reduces the classifier performance. Hence, Circle Search Algorithm with Convolutional Neural Network (CSA-CNN) is proposed for detecting DDoS anomaly attacks in SDN. The DDoS anomaly dataset such as CIC-DDoS2019 is preprocessed through label encoding which changes categorical data to arithmetical data types through allocating single numerical labels to every class. Then, CSA is applied for feature selection which helps to efficiently detect malicious traffic patterns by exploring local and global search spaces and improves the detection accuracy. The CNN has ability to detect known and unseen attacks due to handling multidimensional data which increase the security of SDN. The CSA-CNN achieved better performance in terms of 99.97%, 99.95%, 99.94% and 99.98% of precision, sensitivity, f1-score and accuracy for CIC-DDoS2019 dataset when compared to Gated Recurrent Unit (GRU).

关键词： Accuracy Sensitivity Traffic control Network security Feature extraction Encoding Vectors Convolutional neural networks computer crime Software defined networking

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Innovative Data Encryption Techniques using AI for Wireless Sensor Actuator Network security

Innovative Data Encryption Techniques using AI for Wireless ...

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distributed Computing and Optimization Techniques (ICDCOT), international conference on

作者： M Dharmateja Prabha K.P. Rama N Asha P Nithya S. Lalitha P Manojkumar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of computer science and Engineering Arunai Engineering college Tiruvannamalai Tamil Nadu India Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India

Wireless Sensor Actuator networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement.

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Machine Learning-Based Architecture for DDoS Detection in VANETs System

Machine Learning-Based Architecture for DDoS Detection in VA...

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2022 international conference on Artificial Intelligence of Things, ICAIoT 2022

作者： Alkadiri, Naam Ilyas, Muhammad Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey Altinbas University Dept. of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350396768

With the fast and huge vehicular communication systems currently being developed, there is a clear and urgent need for advanced security. To determine whether a vehicle has been attacked, we developed a tool called the misbehavior detection system (MDS), which is intended to help the vehicle take action and minimize any potential harm from attackers. One of the most dangerous forms of attack that threaten vehicular communication systems are distributed denial of service (DDoS) attacks, and increasing the security of VANETs against such attacks is a topic that a large number of researchers are now considering, were to provide highly effective security capabilities, machine learning (ML) techniques were applied. NSL-KDD or KDD-CUP99 datasets form the basis for the greater part of the current research. Attacks on these datasets were outdated. Therefore, we used a new dataset generated by OMNeT++, Veins, and Sumo. Two different types of attacks were conducted during this simulation, and the XGBoost classifier was used to evaluate and predict MDS systems. The median F1-score for this XGBoost classifier was 99%, which represented a clear advantage over another ML method, where we used the Synthetic Minority Oversampling Technique (SMOTE) to class balance the datasets. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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A Grouping Design for Cascaded Coded distributed Computing 8

A Grouping Design for Cascaded Coded Distributed Computing

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8th IEEE international conference on computer and Communications, ICCC 2022

作者： Dong, Hongyan Jiang, Jing Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

ISBN: (纸本)9781665450515

In order to reduce the communication load in general distributed computing frameworks such as MapReduce Li et al. introduced coded distributed computing (CDC). The authors in [1] proposed a scheme for cascaded coded distributed computing on general networks. Although the numbers of input files and output functions in [1] are much less than those in [2], large numbers of input files and output functions are still required. In this paper, we present a new CDC scheme which 1) designs the assignments of input file and output function;2) requires less number of input files and output functions compared with those in the scheme derived by [1]. Meanwhile, the ratio of communication load to that of the scheme in [1] is less than 4/3. © 2022 IEEE.

关键词： distributed computer systems

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Enhancing Frequency System Damping Efficiency via Optimal Integration of VRE in Grid 2

Enhancing Frequency System Damping Efficiency via Optimal In...

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2nd international conference on Energy Transition and security, ICETS 2023

作者： Abderrazak, Tadjeddine Ali Iliace, Arbaoui Sofiane, Bendelhoum Med Ilyas, Bendjillali Ridha Hichem, Hamiani Abdelkader, Chaker Nour Bachir University Center Lseter Laboratory Technology Institute El-Bayadh Algeria Ahmed Draia University Faculty of Material Sciences Mathematics and Computer Science Adrar Algeria National Polytechnic School of Oran - Maurice Audin Scamre Laboratory Algeria

ISBN: (纸本)9798350372182

This comprehensive study addresses the integration stability of photovoltaic (PV) systems in the SAIDA-NAAMA region of western Algeria, focusing on dynamic and static considerations. The research introduces an innovative approach, combining frequency stability analysis, optimal power flow control, and reactive power compensation within distributed power grids. Emphasizing the ESC PV Park in El-Abiodh-SidiCheikh, the primary goal is to enhance energy quality through dynamic stability. Implementation of maximum power point tracking (MPPT) techniques and compensating for reactive power variations demonstrates substantial progress, with up to a 24.72% reduction in active power losses observed under different load scenarios. Additionally, the study integrates reactive power control, utilizing NAAMA's static var compensator for the transmission network and localized compensations for the distribution network, ensuring voltage stability. Beyond reducing losses, these findings offer insights for increasing the ESC photovoltaic station's production capacity, contributing to Algeria's energy transition and green energy utilization. The proposed optimization algorithm proves effective in achieving multiple objectives, highlighting its potential for successful PV system integration into Algeria's national interconnection network (RIN), thus providing valuable contributions to the evolving energy landscape. © 2023 IEEE.

关键词： Electric power transmission networks

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Assessing the Impact of Artificial Intelligence on the Investment Decisions of Working Women

Assessing the Impact of Artificial Intelligence on the Inves...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Devayani. K.S G. Kalpana Department of Commerce Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Artificial intelligence is revolutionising every aspect of modern human life from automating daily tasks to advancing scientific research. AI in finance is reshaping traditional investment practices by empowering investors to make informed investment decisions. This research article investigates the influence of technological advancement and artificial intelligence (AI) on the investment behaviour of working women in Kerala. The study examines how working women perceive and utilise technology and AI in their investment decisions. Additionally, it seeks to identify barriers and limitations that may hinder the effective utilisation of technology and AI in the investment process. IBM SPSS 23 version was used for data analysis where factor analysis, weighted average, rank analysis were conducted. This study acknowledges that there is a slight change in the risk tolerance of participants exposed to the use of technology and artificial intelligence (AI) and also highlights the need for education and awareness programs to help empower investors to take advantage of technology.

关键词： Data analysis Education Finance computer networks Artificial intelligence computer security Investment

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