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检索条件"任意字段=7th Cyber Security in Networking Conference, CSNet 2023"

共 305 条记录，以下是1-10 订阅

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2023 7th cyber security in networking conference, csnet 2023

2023 7th Cyber Security in Networking Conference, CSNet 2023

引用

7th cyber security in networking conference, csnet 2023

ISBN: (纸本)9798350342871

the proceedings contain 40 papers. the topics discussed include: adaptive, multi-factor authentication as a service for web applications;a benchmark of graph augmentations for contrastive learning-based network attack detection with graph neural networks;an energy-efficient multiple-factor authentication protocol for critical infrastructure IoT systems;inferring the confidence level of BGP-based distributed intrusion detection systems alarms;adversarial security and differential privacy in mmWave beam prediction in 6G networks;a study on the privacy concerns of the Internet of things;rejectable SoulBound tokens for credentials assignment and acceptance of terms;collection and statistical analysis of a fixed-text keystroke dynamics authentication data set;a tree-mapped taxonomy of blockchain attacks;Ariadne: a privacy-preserving network layer protocol;and towards anomaly detection using multiple instances of micro-cluster detection.

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Program 2023 7th cyber security in networking conference (csnet)

Program 2023 7th Cyber Security in Networking Conference (CS...

引用

cyber security in networking conference (csnet)

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Mirage: cyber Deception against Autonomous cyber Attacks 7

Mirage: Cyber Deception against Autonomous Cyber Attacks

引用

7th cyber security in networking conference, csnet 2023

作者： Kouremetis, Michael Alford, Ron Lawrence, Dean The Mitre Corporation

ISBN: (纸本)9798350342871

As the capabilities of cyber adversaries continue to evolve, now in parallel to the explosion of maturing and publicly-available artificial intelligence (AI) technologies, cyber defenders may reasonably wonder when cyber adversaries will begin to also field these AI technologies. In this regard, one promising (read: scary) area of AI for cyber attack capabilities are search and automated planning. One possible defensive mechanism against future AI-enabled adversaries is that of cyber deception. To that end, in this work we present and evaluate Mi-rage, an experimentation system that allows for the implementation and testing of novel cyber deceptions designed to counter cyber adversaries that use AI search and planning capabilities. © 2023 IEEE.

关键词： Crime

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MCM-CASR: Novel Alert Correlation Framework for cyber Attack Scenario Reconstruction Based on NLP, NER, and Semantic Similarity 7

MCM-CASR: Novel Alert Correlation Framework for Cyber Attack...

引用

7th cyber security in networking conference, csnet 2023

作者： Diakhame, Mouhamadou Lamine Diallo, Cherif Mejri, Mohamed Gaston Berger University Lacca Lab Saint Louis Senegal Université Laval QuébecG1V 0A6 Canada

ISBN: (纸本)9798350342871

cyber attack scenario reconstruction plays a crucial role in understanding and mitigating security breaches. In this paper, we propose a novel framework that leverages Natural Language Processing (NLP), specifically Named Entity Recognition (NER), and semantic similarity techniques to reconstruct cyber attack scenarios. By analyzing Intrusion Detection alerts, our offline approach identifies relevant entities, detects relationships between them, and measures semantic similarity to uncover hidden patterns and connections. We demonstrate the effectiveness of our framework through experimental evaluations using a public dataset. the results highlight the potential of NLP-based approaches in cyber attack scenario reconstruction. © 2023 IEEE.

关键词： Computer crime

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An Efficient Intrusion Detection Framework in Software-Defined networking for cyber security Applications 5th

An Efficient Intrusion Detection Framework in Software-Defin...

引用

5th International conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Sandhya Vani, Meruva Durga Devi, Rajupudi Mandru, Deena Babu Department of Information Technology Malla Reddy Engineering College Hyderabad500100 India

ISBN: (纸本)9789811986680

the board of directors and mixed media mining strategies are excited about further research and development of the organization's traffic processes. Relying on a unified, programmable controller, security has recently become the most complex task in Software-Defined networking (SDN). Next, observing the organization's traffic is essential to distinguish and discover outage anomalies in the SDN environment. therefore, this paper provides an in-depth review and examination of the NSL-KDD dataset using five different clustering calculations: K-implies, further before, canopy, density-based computation, and expansion of exceptions (EM), contrasting these five calculations largely involving Waikato Environment Programming for Knowledge Analysis (WEKA). Additionally, this paper introduces an SDN-based outage identification framework that uses a deep learning (DL) model and the Knowledge Discovery in Databases (KDD) dataset. A deep learning technique is proposed to promote a successful SDN-based outage detection framework. the findings provide in-depth investigation and flawless intelligent investigation into the different types of attacks. In addition, the results show that the proposed deep learning strategy outperforms existing methods with regard to interrupt discovery performance. For the analyzed dataset, for example, the proposed technique achieves an identification accuracy of 94.21%. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Smart Home IoT Communication Protocols and Advances in their security and Interoperability 7

Smart Home IoT Communication Protocols and Advances in their...

引用

7th cyber security in networking conference, csnet 2023

作者： Holguin, Ismael Errapotu, Sai Mounika University of Texas at El Paso Department of Electrical and Computer Engineering El PasoTX79968 United States

ISBN: (纸本)9798350342871

Advances in sensor and communication technologies have transformed traditional homes into smart homes, equipped with sensors and actuators for various functionalities like smart lighting, temperature control, irrigation, solar monitoring, entertainment, and security. this transition is powered by the Internet of things (IoT) architecture, enabling smart home hubs to integrate and control devices with different communication protocols. However, this shift has also introduced new security and privacy issues in the Smart Home IoT (SH-IoT) environment. To address these challenges, new communication protocols with cryptographic features have been developed, and a unified standard called Matter has been created to promote interoperability among different device manufacturers. this paper presents a comprehensive survey of recent trends and advances in the smart home IoT landscape, focusing on communication protocols, their security issues and protection features against vulnerabilities in the SH-IoT environment. © 2023 IEEE.

关键词： Internet of things

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security-Oriented Network Intent Placement using Particle Swarm Optimization 7

Security-Oriented Network Intent Placement using Particle Sw...

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7th cyber security in networking conference, csnet 2023

作者： Landeau, Gabriel Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Department of Software and It Engineering Montréal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350342871

As the network infrastructure grows, its configuration and service provisioning become a tedious process. Accordingly, new paradigms have emerged, such as the Intent-Based networking (IBN), that envision the automation of the network configuration, while minimizing the human intervention. Specifically, IBN allows users to interact with the network through high-level and declarative requests, called intents, which later can be translated into low-level configurations. IBN can entail different scopes and target network infrastructures, while being domain specific, which can create several challenges in terms of the final activation of the requested intents. To this end, in this paper, we mainly focus on intents that are expressing security and Quality of Service (QoS) network services demands that can be translated into Service Function Chains (SFC) and automatically deployed over a campus network. Our work and depending on the security level expressed in the intent, tries to optimally decide the level of multi-tenancy or complete segregation of the users' services that can be achieved, while satisfying the network provider's objectives. In particular, an artificial intelligence inspired algorithm called Particle Swarm Optimization (PSO) is modeled that automatically tries to find the best placement of the intents, while satisfying the security and QoS requirements of the users issuing the intents. © 2023 IEEE.

关键词： Quality of service

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Graph-Based Attack Path Discovery for Network security 7

Graph-Based Attack Path Discovery for Network Security

引用

7th cyber security in networking conference, csnet 2023

作者： Meng, Qiaoran Wang, Huilin Oo, Nay Lim, Hoon Wei Johannes Schatz, Benedikt Sikdar, Biplab National University of Singapore Singapore Singapore Ncs Cyber Special Ops-R&D Singapore Singapore

ISBN: (纸本)9798350342871

Enterprise network systems are confronted with an escalating threat landscape, requiring timely and effective attack detection and mitigation of the risk of potential financial losses and system damages. However, existing algorithms mostly rely on machine learning techniques or attack knowledge bases. they face challenges dealing with the large volumes of noisy network logs in enterprises, as well as the emergence of unknown cyber attacks. Moreover, previous research has predominantly focused on anomaly detection using raw network traffic capture, with limited exploration on attack path prioritization. To address these challenges, this paper introduces a novel algorithm for attack path detection and prioritization in network systems. Our approach gathers comprehensive asset information and network logs from multiple Network Intrusion Detection Systems (NIDSs). through data processing and collation, the network data undergoes significant noise reduction and transformation into a network communication graph format. Subsequently, a Graph Neural Network (GNN) based anomaly detection algorithm is employed to extract and prioritize potential attack paths on the graph. this methodology leverages the power of unsupervised Machine Learning (ML) techniques and operates independently of prior attack databases. Incorporating path mining techniques, our algorithm provides visibility into identified attack propagation chain and the sequence of assets involved, which offers more valuable information compared to the repetitive atomic network traffic data from NIDSs. the algorithm is evaluated using the UNSW-NB15 dataset and proven to be effective and accurate with comprehensive experiment settings. © 2023 IEEE.

关键词： Anomaly detection

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Adversarial security and Differential Privacy in mmWave Beam Prediction in 6G networks 7

Adversarial Security and Differential Privacy in mmWave Beam...

引用

7th cyber security in networking conference, csnet 2023

作者： Krishna, Ghanta Sai Supriya, Kundrapu Singh, Sanskar Baidya, Sabur Iiit Naya Raipur Department of Computer Science India University of Louisville Department of Computer Science and Engineering KY United States

ISBN: (纸本)9798350342871

In the forthcoming era of 6G, the mmWave communication is envisioned to be used in dense user scenarios with high bandwidth requirements, that necessitate efficient and accurate beam prediction. Machine learning (ML) based approaches are ushering as a critical solution for achieving such efficient beam prediction for 6G mmWave communications. However, most contemporary ML classifiers are quite susceptible to adversarial inputs. Attackers can easily perturb the methodology through noise addition in the model itself. To mitigate this, the current work presents a defensive mechanism for attenuating the adversarial attacks against projected ML-based models for mmWave beam anticipation by incorporating adversarial training. Furthermore, as training 6G mmWave beam prediction model necessitates the use of large and comprehensive datasets that could include sensitive information regarding the user's location, differential privacy (DP) has been introduced as a technique to preserve the confidentiality of the information by purposefully adding a low sensitivity controlled noise in the datasets. It ensures that even if the information about a user location could be retrieved, the attacker would have no means to determine whether the information is significant or meaningless. With ray-tracing simulations for various outdoor and indoor scenarios, we illustrate the advantage of our proposed novel framework in terms of beam prediction accuracy and effective achievable rate while ensuring the security and privacy in communications. © 2023 IEEE.

关键词： Beamforming

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An Efficient Explainable Artificial Intelligence (XAI)-based Framework for a Robust and Explainable IDS 8

An Efficient Explainable Artificial Intelligence (XAI)-based...

引用

8th cyber security in networking conference

作者： Nugraha, Beny Jnanashree, Abhishek Venkatesh Bauschert, thomas Tech Univ Chemnitz Chair Commun Networks Chemnitz Germany

ISBN: (纸本)9798331534110;9798331534103

Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) significantly advance network security by leveraging Machine Learning (ML) and Deep Learning (DL) for highly accurate and dynamic cyber threat detection. However, a critical limitation of current AI-based IDS is their inherent "black box" nature, which disrupts the decision-making processes, thereby compromising trust and accountability. In response to these challenges, we propose an efficient Explainable Artificial Intelligence (XAI)-based framework designed to enhance both the robustness and explainability of IDS. Our two-stage process integrates traditional statistical methods with XAI techniques, specifically SHAP and LIME, for feature selection and explanation analysis, providing transparent and interpretable decision-making while maintaining high detection performance. Our experimental evaluation, conducted using the CIC-DDoS2019 and CICIoT2023 datasets, demonstrates that the framework can sustain high detection accuracy while significantly enhancing the interpretability of IDS decisions. Moreover, the proposed feature reduction process lowers the computation effort of the XAI techniques, resulting in up to 87% faster.

关键词： Explainable Artificial Intelligence Network security Intrusion Detection System SHAP LIME Feature Reduction

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