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检索条件"任意字段=International Conference on Security in Computer Networks and Distributed Systems, SNDS 2012"

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Securing networks: Leveraging Machine Learning for Enhanced DDoS Detection

Securing Networks: Leveraging Machine Learning for Enhanced ...

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Software, systems and Information Technology (SSITCON), international conference on

作者： Som Chauhan Prajval Byahatti Sunil Kumar Patel Department of Computer Science & Engineering Manipal University Jaipur Jaipur India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The growing complexity of distributed Denial of Service (DDoS) attacks poses a serious threat to network security, making the development of effective detection and mitigation techniques essential. This study tackles this issue by introducing a novel approach that utilizes machine learning (ML) to improve the identification of DDoS attacks using realworld datasets. By leveraging the CSE-CIC-IDS2018 AWS dataset, we develop a strong classifier to examine network traffic patterns that suggest potential DDoS activity. Our approach encompasses comprehensive steps, including data collection, preprocessing, and the deployment of sophisticated ML algorithms. The outcomes reveal impressive performance, with the model achieving a $99.98 \%$ accuracy rate and nearperfect precision and F1 scores. These results highlight the strength of neural network-based models, especially when implemented using advanced frameworks such as Keras. This research ultimately bolsters network defenses against DDoS attacks and emphasizes the necessity of ongoing innovation in detection techniques to protect digital resources and maintain uninterrupted online services in our increasingly digitalized environment.

关键词： Technological innovation Machine learning algorithms Accuracy Telecommunication traffic Network security Denial-of-service attack Software systems Trajectory computer crime Streams

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An Investigative Evaluation of Open Source Intrusion Detection systems for Operational Technology networks Using MITRE ICS Attack Simulation on a Thermal Power Plant Test Bed

An Investigative Evaluation of Open Source Intrusion Detecti...

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IEEE India conference (INDICON)

作者： Anooja Joy Madhav Chandane Faruk Kazi Computer Engineering Dept. VJTI Mumbai India Department of Electrical Engineering VJTI Mumbai India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Intrusion Detection systems (IDS) play a critical role in identifying security breaches by monitoring network activities and protecting computer networks and systems. The significant increase in cyber attacks targeting the Operational Technology (OT) domain has necessitated the deployment of IDS solutions to detect malicious activities within industrial networks. High-speed traffic generated in Industrial Control System (ICS) environments presents a unique challenge for IDS, as they must monitor large and diverse traffic volumes while maintaining low latency and high accuracy. This study evaluates the performance of three widely used open source IDS solutions-Zeek, Suricata, and Wazuh-across various parameters like attack detection coverage, packet drop rate, and resource utilization which are crucial for ICS environments. The evaluation was conducted using a Hardware-in-the-Loop (HIL) Thermal Power Plant (TPP) test bed, where various cyberattacks using various techniques from the MITRE ICS ATT&CK matrix were executed simulating real-world threats to critical infrastructure. Events detected by IDS sensors from the network traffic of the test bed were transmitted to a distributed Elastic cluster and visualized using Kibana dashboards. This systematic assessment aims to provide insight into the effectiveness of open-source IDS solutions in addressing the unique security challenges of ICS environments.

关键词： Visualization Systematics Industrial control Intrusion detection Telecommunication traffic Thermal conductivity Sensors security Power generation Monitoring

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AI-Enhanced security Architecture for 6G networks: A Federated Learning and Multi-Agent Approach

AI-Enhanced Security Architecture for 6G Networks: A Federat...

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Computing and Intelligent Reality Technologies (ICCIRT), international conference on

作者： V.V Teresa J. Dhanaseker S Arjun R Naveenraj A Antru Subil Pm Ahmed Najeeb Department of ECE Sri Eshwar College of Engineering Coimbatore

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

The coming 6G network is expected to provide better connectivity and network performance but will generate new security challenges. In this paper, we demonstrate a novel technological solution to address the security challenges of the 6G network by combining artificial intelligence with federated learning and multi-agent systems. Federated learning enables the distributed learning of data in the network without revealing the data’s confidentiality. In the meantime, multi-agent systems use intelligent autonomous agents that are equipped to quickly identify and handle any potential threat in a variety of network environments. In combination, these technologies constitute a system-on-system (SoS) type, end-to-end security architecture that is modular, dynamic, and self-contained, consistent with ultra-reliability requirements, high device count in the terahertz regime, and wide-ranging traffic data found in 6G. Through simulations and case studies, this paper demonstrates that the proposed method significantly enhances the accuracy of threat detection and response, as well as improves the efficiency of threat mitigation and the overall security compliance of the network for a more secure and robust 6G environment.

关键词： 6G mobile communication Accuracy Federated learning Computational modeling computer architecture Threat assessment security Artificial intelligence Stress Multi-agent systems

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IoT Based Smart systems using Artificial Intelligence and Machine Learning: Accessible and Intelligent Solutions

IoT Based Smart Systems using Artificial Intelligence and Ma...

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international conference on Information systems and computer networks (ISCON)

作者： Bilal Ahmed Mirza Shuja Hari Mohan Mishra Ahmed Qtaishat Mukesh Kumar School of Computer Application Lovely Professional University Phagwara Punjab Department of Information Technology Sohar University Sohar Sultanate of Oman

The Internet of Things (IoT) has grown in importance in both the technological and social spheres as consumers want smarter homes, more efficient businesses, and more efficient healthcare systems. The social setting is very important for this. Communication networks provide uninterrupted internet connectivity for both fixed and mobile devices like PCs and smartphones. Customers may now keep up their level of engagement while interacting with one or more apps at any time and from any location, thanks to developments in information and communication technologies. Many kinds of smart devices might potentially coordinate their responses to events, sending out alerts or keeping records of the system’s condition in line with a predetermined policy. Constant progress toward intelligent solutions is improving many facets of modern life, including manufacturing, transportation, electronic healthcare, electronic education, and many more. Machine-to-machine (M2M) communication in the IoT is autonomous and dynamic. Diverse approaches are being developed to address the challenges posed by disparate data sources, fluctuating signal quality, and overwhelming quantities of data. From the perspectives of intelligent system technologies, security, vulnerabilities, and the potential role that intelligent solutions based on Machine Learning (ML) and Artificial Intelligence (AI) may play, this article examines the IoT. Concerns about privacy and safety have arisen as a major obstacle to the widespread adoption of the Internet of Things (IoT). We propose that the Standard security Framework (SSF) be made mandatory for all distributed networks to ensure that hardware and software modules may communicate with one another regardless of their respective platforms. In addition, the effectiveness and security of new innovations in using fog and edge computing in conjunction with cloud applications have been studied.

关键词： Technological innovation Soft sensors Transportation Machine learning Safety Internet of Things security

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Detection and Evaluation of Cybersecurity Threats in MANET Based on AI

Detection and Evaluation of Cybersecurity Threats in MANET B...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), international conference on

作者： R. Mohandas Karthik Kumar Vaigandla N. Sivapriya Kirubasankar K Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu Department of Electronics & Communication Engineering Balaji Institute of Technology and Science Warangal Telangana Department of Computer Applications Cauvery College for Women Trichy Tamil Nadu

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This research focuses on the use of AI to enhance the security of Mobile Ad-hoc networks (MANETs), which aredecentralized and mobile wireless networks characterized by their dynamic nature. The work aims to assess the risks associated with cyber activities and identify various sorts of cyber threats, such as distributed Denial of Service (DDoS) attacks, malware invasions, data leaks and unauthorized access attempts. This will be achieved by using Artificial Intelligence (AI)-powered algorithms and models. The objective is to enhance the accuracy in identifying and categorizing these threats, hence increasing the security and dependability of MANET networks. Anomaly detection is a supplementary protection mechanism that specifically targets the hardware and network traffic of MANETs. Implementing a monitoring strategy is necessary in order to detect any anomalous activity that might potentially indicate security vulnerabilities or attacks in the MANET setups. The primary objective is to identify and address the unique characteristics of timely detection, which may enhance the security capabilities of MANET against cyber attacks. Machine learning (ML) algorithms often demonstrate impressive performance in efficiently identifying and accurately classifying various cyber threats, including DDoS attacks, malware infiltrations, and attempted unauthorized access. This anomaly detection technology effectively identifies robot abnormalities and malicious actions in network data, while also proactively avoiding system vulnerabilities or threats. In addition, this research discovered very effective AI-based cybersecurity solutions for dynamic decentralized MANET systems. These systems were specifically designed for tasks such as street-view switching, route finding, self healing, and self configuration.

关键词： Privacy Accuracy Machine learning Denial-of-service attack Ad hoc networks Malware Reliability Object recognition Mobile computing Anomaly detection

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Levy Flight Strategy with Chicken Swarm Optimization Algorithm for Real-Time Anomaly Detection in Cybersecurity

Levy Flight Strategy with Chicken Swarm Optimization Algorit...

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

作者： Mallubhotla Syamala SaiSree Subramanya S G Sripelli Jagadish S. Sureshkumar Mohammed Al-Farouni Mallareddy Engineering College for Women (UGC-Autonoumous) Hyderabad India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Kalasalingam Academy of Research and Education Krishnankoil India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The cybersecurity field has witnessed several Intrusion Detection System (IDS) and Anomaly Detection, which are essential for identifying malicious activities in network traffic. However, growing volume and diversity of modern network attacks pose challenges, impacting the accuracy of these detection systems. This paper, Levy Flight strategy with Chicken Swarm Optimization Algorithm (LCSO) and Gradient-Boosted Decision Tree (GBDT) techniques achieve better accuracy. GBDT builds decision trees iteratively and helps in reducing bias and variance, leading to high accuracy models, especially with feature selected by using CSO. The CSO algorithm applied to various types of data, making it a flexible feature selection method for different cybersecurity. Initially data obtained from KDD-99 and NSL-KDD dataset and normalization is essential to prevent feature with large range from dominating the learning process. The feature selection using LCSO algorithm focusing on the most relevant features, helps improved efficiency achieve classification accuracy. The GBDT build better classifiers that handle both normal and anomalous pattern efficiently, especially with optimized feature set. The proposed LCSO-GBDT algorithm achieves better accuracy of 99.15% on KDD and 99.12% on NSL-KDD dataset when compared to existing techniques Deep Neural Network (DNN) and Bat Algorithm approach.

关键词： Accuracy Focusing Artificial neural networks NSL-KDD Feature extraction Classification algorithms Decision trees computer security Particle swarm optimization Anomaly detection

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Investigating a Comprehensive IoT security Solution Based on Blockchain 6th

Investigating a Comprehensive IoT Security Solution Based on...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Madhan, Ravinder Singh Singh, Randeep IEC University Himachal Pradesh Baddi174103 India

ISBN: (纸本)9783031840586

The term "Internet of Things" (IoT) refers to a network of interconnected objects capable of gathering data from their immediate surroundings and facilitating communication with one another over the Internet. "The proliferation of Internet of Things (IoT) devices has resulted in the generation of a significant amount of sensitive data, which is vulnerable to a range of security breaches. The growth of the IoT necessitates a significant emphasis on security measures, including aspects such as confidentiality, integrity, dependability, and several other issues. Due to the inherent limitations of hardware, the conventional security measures used in regular networks are not viable for implementation. Due to these factors, the decentralized record-based technique known as the blockchain (BC) approach is used instead of a centralized one. The main aim of this research is to conduct a complete analysis of the mechanism for blockchain-based securities in the context of the IoT. This study has specifically examined the application of blockchain technology in many businesses and categories. This approach yields superior outcomes concerning security considerations, including aspects such as security and privacy, when contrasted with the existing security systems. This study also examines the technology of Blockchain, its many uses, and the special issues it poses in the context of the IoT. The distributed and decentralized nature of blockchain technology offers hope for resolving these security issues in IoT networks. The integration of blockchain technology with the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm enhances the security of Internet of Things (IoT) networks. This integration facilitates more effective data management and ensures the integrity of transactions, hence promoting the durability and safeguarding of these networks. The objective of this article is to enhance the security of Internet of Things (IoT) networks. To do this, the paper proposes a

关键词： Information management

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An Innovative Ensemble Approach to Detecting distributed Denial of Service Attacks Using Machine Learning

An Innovative Ensemble Approach to Detecting Distributed Den...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), international conference on

作者： Deepak Singh Rajput Arvind Kumar Upadhyay Dept. of Computer Science and Engineering Amity School of Engineering &Technology (ASET) Amity University Gwalior Madhya Pradesh

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

In the present era marked by rapid Internet development, there is a growing emphasis on information security. distributed Denial of Service (DDoS) attacks poses significant threats to servers globally. Various methods and services have emerged to prevent and mitigate damage caused by these attacks. The challenge in implementing defence lies in timely attack detection, requiring a highly accurate mechanism. DDoS attack methods are diverse and evolving, rendering traditional prevention methods less effective. This research explores the application of machine learning, utilizing the CICDDoS2019 dataset to train hybrid models. The proposed ensemble model combines unsupervised K-Means for dataset labelling and supervised classifiers (Random Forest, XGBoost, AdaBoost, SVM) with Artificial Neural Network (ANN). Achieving an overall accuracy of 99.46%, this study underscores the effectiveness of deep learning in DDoS attack detection.

关键词： Training Support vector machines Accuracy Scalability Artificial neural networks Denial-of-service attack Rendering (computer graphics) Real-time systems Servers computer crime

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Research on blockchain architecture for terminal side business applications of distributed iot

Research on blockchain architecture for terminal side busine...

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Energy, Power and Grid (ICEPG), international conference on

作者： Xiukui Pan Ke Yang Na Li Shangzhuo Zheng Qinglei Guo State Grid Blockchain Technology (Beijing) Co. Ltd Beijing China State Grid Digital Technology Holdings Co. Ltd Beijing China Blockchain Application Technology Laboratory of State Grid Corporation of China Beijing China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

As the most important energy exchange hub of the new power system, the distribution network is of great significance for supporting clean energy production and electricity substitution for energy consumption. The blockchain-based distribution network supports distributed power generation and has the function of good interaction with the main network and users, adapting to the construction and development of a strong intelligent distribution network. The focus of this article is on the authenticity and security requirements of data on the intelligent terminal side of the distributed Internet of Things system. A blockchain and chip software and hardware collaborative technology framework are designed for the terminal side business, and an embedded edge intelligent framework and efficient parallel blockchain hardware acceleration architecture are designed. Ultimately, high-performance, highly trusted, and highly secure underlying technology support is provided for blockchain end-side applications, creating a trustworthy software and hardware integrated environment, hence improving the operational efficiency of the underlying blockchain platform, reducing blockchain latency, and supporting adaptation to multiple types of blockchain basic service platforms.

关键词： computer architecture Distribution networks Production Software systems Blockchains Power systems Internet of Things security Hardware acceleration Business

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Beyond Basic Trust: Envisioning the Future of NextGen Networked systems and Digital Signatures

Beyond Basic Trust: Envisioning the Future of NextGen Networ...

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IEEE international conference on Trust, Privacy and security in Intelligent systems and Applications (TPS-ISA)

作者： Attila A. Yavuz Kiarash Sedghighadikolaei Saleh Darzi Saif E. Nouma Computer Science & Engineering University of South Florida Tampa FL USA

Authentication and integrity are foundational security services for trustworthy systems and the prerequisite of privacy preservation. At the heart of these services lies digital signatures, widely deployed in real-life applications and supported by various standards. Yet, newly emerging next-generation (NextG) networked systems are vastly distributed, include many resource-limited components, and demand advanced features such as privacy, anonymity, and post-quantum (PQ) security. However, the current signature standards and specialized signatures only meet some of these important requirements in isolation. Hence, there is a significant gap in the state-of-the-art in identifying the needs of emerging networked systems and synergizing them with the features of advanced signatures. In this work, we strive to mitigate this gap by uniting burgeoning ubiquitous systems with advancements in digital signatures and then envisioning the trust via signatures with extended features for NextG networked systems. We investigate the current signature standardizations and advanced constructions for their potentials and drawbacks in three essential aspects of NextG networks - decentralized, privacy-preserving, and resource-constraint settings. We first analyze threshold cryptography efforts proffered by NIST, both from secure multi-party computation and custom design constructions, with applications on distributed systems like blockchains, federated cloud, and NextG Public Key Infrastructures (PKIs) in mind. We then investigate the intersections of distributed signatures and privacy-preservation techniques for privacy-sensitive NextG applications (e.g., medical, cryptocurrency). We also focus on research gaps for resource and time-limited systems and identify suitable signatures to remedy this gap for security-critical applications (e.g., vehicular networks, smart grids). Finally, we discuss potential directions for these ubiquitous NextG systems and advanced signatures in the PQ era

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