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Adversarial Attacks and Defenses Using Machine Learning for Cybersecurity in Corporates

Adversarial Attacks and Defenses Using Machine Learning for ...

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

作者： R. Gopinath C. Sathiyamoorthy D. Sugumaran K.S. Giriprasath Neha Tripathi Mohammad Arif Department of Computer Science and Engineering K S.Rangasamy College of Technology Tamil Nadu India Department of Commerce Saveetha College of Liberal Arts and Sciences Chennai Tamil Nadu India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore Tamil Nadu India Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science & Engineering Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This article suggests a novel method for protecting corporate cybersecurity systems from malevolent attacks, based on Capsule networks (CapsNets). The enhancement of hierarchical feature learning by Capital networks is a critical component of its capacity to differentiate between authentic and fraudulent data. Robust optimization techniques and adversarial training are implemented to develop a model. The training seeks to be more resilient and beneficial in a larger environment by introducing perturbations one capsule at a time. CapsNets executed an effective operation, achieving 95% accuracy and 97% precision. In terms of managing adversarial assaults, CapsNets outperform baseline models greatly. The proposed approach exhibits potential as an improved cybersecurity defense method, as a result of its exceptional resilience and precision. This study demonstrates the efficacy of CapsNets in improving cybersecurity and also offers a glimpse into the adversarial defenses used in enterprise machine learning applications.

关键词： Training Representation learning Measurement Analytical models Accuracy Perturbation methods Routing computer security Optimization Resilience

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Secure Resource Allocation Protocol (SecRAP) for Time-Sensitive Networking

Secure Resource Allocation Protocol (SecRAP) for Time-Sensit...

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Lukas Osswald Steffen Lindner Lukas Bechtel Tobias Heer Michael Menth University of Tuebingen Chair of Communication Networks Tuebingen Germany Esslingen University Computer Science and Engineering Esslingen Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

The convergence of operational technology (OT) and information technology (IT) networks through Time-Sensitive Networking (TSN) promises enhanced efficiency and new use cases in industrial settings. However, ensuring security in this shared infrastructure is crucial to prevent potential attacks that could compromise Quality of Service (QoS) of real-time streams and pose risks to operations and safety. This paper focuses on auditing the security of the Resource Allocation Protocol (RAP), a distributed QoS signaling protocol for TSN. We analyze the vulnerability of RAP to attacks during admission control, where end stations request network resources for data transmission. We leverage the Dolev-Yao attacker model to assess the security properties of RAP in both distributed hop-by-hop admission control and hybrid admission control with a central controller. We introduce novel security extensions to RAP, called Secure Resource Allocation Protocol (SecRAP), to mitigate the identified attack vectors. Finally, we present a prototype and discuss the security properties of SecRAP.

关键词： Protocols Admission control Prototypes Vectors Real-time systems security Resource management Protection Streams Manufacturing automation

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5th international conference on HCI for Cybersecurity, Privacy and Trust, HCI-CPT 2023, held as part of the 25th international conference on Human-computer Interaction, HCII 2023

5th International Conference on HCI for Cybersecurity, Priva...

引用

5th international conference on HCI for Cybersecurity, Privacy and Trust, HCI-CPT 2023, held as part of the 25th international conference on Human-computer Interaction, HCII 2023

ISBN: (纸本)9783031358210

The proceedings contain 44 papers. The special focus in this conference is on HCI for Cybersecurity, Privacy and Trust. The topics include: Usable Implementation of Data Sovereignty in Digital Ecosystems;research on the Capability Maturity Model of Data security in the Era of Digital Transformation;data Guardians’ Behaviors and Challenges While Caring for Others’ Personal Data;a Privacy-Orientated distributed Data Storage Model for Smart Homes;a Question Answering Tool for Website Privacy Policy Comprehension;perception of Privacy and Willingness to Share Personal Data in the Smart Factory;multi-ledger Coordinating Mechanism by Smart Contract for Individual-Initiated Trustworthy Data Sharing;privacy Awareness Among Users of Digital Healthcare Services in Saudi Arabia;threat Actors and Methods of Attack to Social Robots in Public Spaces;supporting Small and Medium-Sized Enterprises in Using Privacy Enhancing Technologies;overcoming Privacy-Related Challenges for Game Developers;Cybersecurity Compliance Requirements for USA Department of Defense Contractors - Dragons at the Gate;behavioral Biometrics Authentication in Critical Infrastructure Using Siamese Neural networks;trust and Blame in Self-driving Cars Following a Successful Cyber Attack;I Just Want to Help: SMEs Engaging with Cybersecurity Technology;Business Continuity Planning (BCP) for Election systems;cybersecurity as Part of Mission Assurance;investigating Mobile Instant Messaging Phishing: A Study into User Awareness and Preventive Measures;look Before You Leap! Perceptions and Attitudes Towards Inferences in Wearable Fitness Trackers;user Motivations of Secure Web Browsing;working for Home – Privacy and Confidentiality Issues in University Education;parents, Passwords, and Parenting: How Parents Think about Passwords and are Involved in Their Children’s Password Practices;Overcoming the UX Challenges Faced by FIDO Credentials in the Consumer Space.

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Performance Analysis of Linear Regression based Simple Reflex Artificial Intelligence Agent to Detect Malwares

Performance Analysis of Linear Regression based Simple Refle...

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

作者： P. Kalaivani C. Venkatesh Department of ECE Salem College of Engineering and Technology Salem Tamilnadu India Department of ECE KGISL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The new pathogen detection method is developed using Linear Regression and Simple Reflex. This is accomplished using artificial intelligence. This straightforward yet efficient real-time detection method employs linear regression techniques to anticipate the presence of malware based on system activity patterns. The Simple Reflex AI agent responds to the system's current perceptions without considering prior data to facilitate rapid and precise detection. This method enhances the accuracy of detection and reduces the cost of computation. This is accomplished through the following steps: data collection, feature engineering, model training, and deployment. The model achieves a recall of 96.7%, an accuracy of 97.3%, a precision of 97.7%, and an F1-score of 96.9%, significantly outperforming signature-based detection methods. The results of this investigation indicate that linear regression enhances malware detection and is optimal for the identification of intricate maladies due to its robust and adaptable foundation.

关键词： Training Adaptation models Accuracy Computational modeling Prevention and mitigation Linear regression Malware Real-time systems Artificial intelligence computer security

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Progress in Artificial Intelligence Applications Based on the Combination of Self-Driven Sensors and Deep Learning 4

Progress in Artificial Intelligence Applications Based on th...

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4th international conference on Consumer Electronics and computer Engineering, ICCECE 2024

作者： Wan, Weixiang Sun, Wenjian Zeng, Qiang Pan, Linying Xu, Jingyu University of Electronic Science and Technology of China Electronics & Communication Engineering Chengdu China Yantai University Electronic and Information Engineering Shandong Yantai China Zhejiang University Computer Technology Zhejiang Hangzhou China Trine University Information Studies PhoenixAZ United States Northem Arizona University Computer Information Technology FlagstaffAZ United States

ISBN: (纸本)9798350384062

In the era of the Internet of Things, developing a smart sensor system with sustainable power supply, easy deployment, and flexible use has become a challenging problem. Traditional power supplies, prone to frequent replacements or requiring charging during usage, hinder the advancement of wearable devices. The contact-to-separate friction nanogenerator (TEN G), composed of polychotomy ethylene (PTFE) and aluminum (AI) foils, addresses this issue by harvesting human motion energy through body movement arrangement. This energy is then utilized to monitor human motion posture by detecting changes in output electrical signals. In 2012, Academician Wang Zhonglin and his team invented the triboelectric nanogenerator (TENG), which operates as a self-driven sensor primarily powered by Maxwell displacement current. This enables direct conversion of mechanical stimulation into electrical signals during action, making it suitable for self-powered sensor applications. TENG-based sensors boast a simple structure and high instantaneous power density, providing a crucial tool for constructing intelligent sensor systems. Additionally, when combined with machine learning characteristics such as low cost, short development cycles, robust data processing, and predictive capabilities, the processing of the numerous electrical signals generated by TENG has a significant impact. The integration of TENG sensors is poised to drive the rapid evolution of intelligent sensor networks in various fields such as transportation, security, water conservancy, and construction, where urban sound management is essential. The method of seamlessly integrating sensors with Internet access is implemented using the NetBox network development platform, following these steps: 1) Initially, read the default parameter settings from pre-existing storage media, including parameters like IP address, subnet code, gateway address, sensor input and output types, and sensor range. These settings can be modified onl

关键词： Ethylene

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Federated Orchestration Framework for Integrated Core and Edge Network security: Combining P4-ShieldNet and SAFE-CAST

Federated Orchestration Framework for Integrated Core and Ed...

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IEEE international conference on Big Data

作者： Yusuf Kursat Tuncel Kasim Oztoprak Department of Computer Engineering Konya Food and Agriculture University Konya Turkiye

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This paper introduces a Federated Orchestration Framework that integrates P4-ShieldNet for core network security with SAFE-CAST for edge security, addressing the complex challenges in modern telecommunication networks. Our approach combines programmable data planes with distributed intelligence, providing a comprehensive security solution from core to edge. Simulations demonstrate significant improvements over traditional methods and standalone implementations, achieving 97% threat detection accuracy, 80% faster response times, and support for up to 50,000 devices. The framework incorporates cross-domain security coordination, federated learning, and intent-based policies, paving the way for adaptive, self-defending networks suitable for next-generation 5G and 6G infrastructures.

关键词： Adaptive systems Accuracy Image edge detection Computational modeling Network security Communications technology Threat assessment Time factors Telemetry Next generation networking

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Comparative Analysis of an AI-enabled State-of-the-Art Models for the Detection of DDoS Attacks on IoT Devices 3

Comparative Analysis of an AI-enabled State-of-the-Art Model...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Kumar, Rajneesh Gulati, Gaurav Gupta, Bhupesh Chhabra, Mohit Maharishi Markandeshwar Engineering College Mmdu Department of Computer Science & Engineering Haryana Mullana India Ims Engineering College Department of Computer Science& Engineering UP Ghaziabad India

ISBN: (纸本)9798331519582

With technological advancement, distributed Denial of Service (DDoS) attacks are gaining momentum in IoT networks. DDoS attacks are widely spreading on IoT devices since they have limited resources (memory) and no efficient security models. Different security measures are available to protect IoT devices from attacks. Some of these are firewalls, Intrusion detection systems, etc. Recently, researchers have utilized Artificial Intelligence for early detection of attacks due to its capability of detecting menaced attacks and, hence, boosting security. In this paper, a rigorous survey of different AI-enabled state-of-the-art models for detecting DDoS Attacks was done, and the future direction and scope of improvement for early Detection of DDoS attacks on IoT devices were highlighted. © 2025 IEEE.

关键词： Network intrusion

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Intrusion Detection systems for Cyber Attacks Detection in Power Line Communications networks

Intrusion Detection Systems for Cyber Attacks Detection in P...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Kashif Naseer Qureshi Noman Arshad Thomas Newe Department of Electronic & Computer Engineering University of Limerick Limerick Ireland Department of Computer Science Bahria University Islamabad Pakistan

Power Line Communication (PLC) is categorized into wired and wireless technologies to distribute the power and transmit the data at different frequency ranges. System administration is one of the significant area in these networks to manage communication processes. security is one of the significant concern which make networks slow and unavailable, false and altered instructions exist, malfunctioning, and abnormal behavior of systems observed. Intrusion Detection System (IDS) is one of the solution to handle security attacks and protect the systems from unauthorized access. However, the existing IDS systems have limited capabilities to handle the new attacks. This paper proposes a Machine Learning (ML) algorithm for IDS system used in PLC networks to improve the overall system performance and detect the vulnerabilities of the system. The proposed system can detect the latest assaults and protect the systems from unauthorized and malicious activities. The proposed IDS system is assessed by using a virtual environment using the latest dataset and compared with existing traditional systems. The experiment results indicated the better performance of the proposed system to handle the new assaults and protect the systems.

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Key Observation to Prevent IP Spoofing in DDoS Attack on Cloud Environment 8th

Key Observation to Prevent IP Spoofing in DDoS Attack on Clo...

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8th international conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Sunitha, T. Vijayashanthi, V. Navaneethakrishan, M. Mohanaprakash, T.A. Ashwin, S. Harish, T.R. Stanes, Emmanuel A. Department of Computer Science and Engineering P. B. College of Engineering Chennai India Chennai India Department of Computer Science and Engineering St. Joseph College of Engineering Sriperumbudur Chennai India Department of Computer Science and Engineering Panimalar Engineeering College Varadharajapuram Poonamallee Chennai India Department of Computer Science and Engineering Panimalar Insitute of Technology Varadharajapuram Poonamallee Chennai India

ISBN: (纸本)9789811998577

The utilization of Cloud to put together and execute distributed Denial of Service (DDoS) assaults is well-known among programmers in the current scenario. In the existing framework, networks can use Border Gateway Protocol (BGP) declaration setups to change the internet courses and an assortment of other sources in a precise manner. In this paper investigates in detail how an organisation can regulate this data source and the connection where the traffic enters an organisation. In this proposed work, PKI-Public key validation techniques with Advanced Encryption Standard were utilized (AES) and technique works on the basis that the courses are influenced by network control, thus the organization getting the caricature traffic affects it when it gets traffic, rather than depending on switches that are not influenced at all. The proposed method which is does not correspond to the existing follow back approaches requires no progressions to send hardware or participation from different organizations. This procedure work best when the caricature traffic begins from not many sources, as is normal in intensification DDoS assaults and also the experimental findings demonstrated that the approach has a 99.3% accuracy rate for detecting internal and external low/high rate faked DDoS assaults and offers higher performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network security

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Federated Intrusion Detection on Non-IID Data for IIoT networks Using Generative Adversarial networks and Reinforcement Learning 17th

Federated Intrusion Detection on Non-IID Data for IIoT Netwo...

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17th international conference on Information security Practice and Experience (ISPEC)

作者： Nguyen Huu Quyen Phan The Duy Nguyen Chi Vy Do Thi Thu Hien Van-Hau Pham Univ Informat Technol Informat Secur Lab Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9783031212796;9783031212802

Federated learning (FL) has become the promising approach for building collaborative intrusion detection systems (IDS) as providing privacy guaranteeing among data holders. Nevertheless, the non-independent and identically distributed (Non-HD) data in real-world scenarios negatively impacts the performance of aggregated models from training client updates. To this end, in this paper, we introduce Generative Adversarial networks (GANs) and Reinforcement Learning (RL) approach for federated IDS that can deal with Non-HD data among organizational networks. More specifically, the imbalanced state between data classes is tackled by GAN-based data augmentation, while RL provides better performance in the client choosing process for federated IDS model training. Finally, the experimental results on Kitsune dataset indicate that our work can help to set up the collaboration between data holders for building more effective IDS to deploy in practice with distinguished data distribution.

关键词： Federated learning (FL) Reinforcement learning (RL) Intrusion detection system (ids) Generative Adversarial networks (GANs) Cyberattack

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