SDSA: A framework of a software-defined security architecture

Liu, Yanbing; Lu, Xingyu; Yi, Jian; Xiao, Yunpeng

doi:10.1109/cc.2016.7405735

Cited by 11 publications

(3 citation statements)

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“…In [42], an intelligent honeynet architecture based on the SDS framework [43] is proposed, enabling flexible deployment and dynamic provisioning over Network Function Virtualization Infrastructure (NFVI). This paper focuses on migrating resources according to the workloads of each honeypot and power off unused modules, which is a kind of countermeasure that increases the cost-efficiency of the honeypot resource.…”

Section: B Sdn-enabled Honeypot Architecturesmentioning

confidence: 99%

HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design

Fan

Smith-Creasey

et al. 2019

IEEE J. Select. Areas Commun.

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Section: B Sdn-enabled Honeypot Architecturesmentioning

confidence: 99%

HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design

Fan

Smith-Creasey

et al. 2019

IEEE J. Select. Areas Commun.

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“…Basically, the orchestrator takes inputs from the mitigation module and, under the supervision of the operator, applies the best security reaction strategy on the telecommunication network implementing a Software Defined Security (SDS) approach as shown in Figure 3. As introduced in [31], SDS is a framework mimicking the SDN approach that has been successfully being applied for managing communications networks. The main goal of SDS is the decoupling of the control and the operation part of a security system by 23 exploiting virtualization of security techniques.…”

Section: Orchestratormentioning

confidence: 99%

Integrated protection of industrial control systems from cyber-attacks: the ATENA approach

Adamsky

Aubigny

Battisti

et al. 2018

International Journal of Critical Infrastructure Protection

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Industrial and Automation Control systems traditionally achieved security thanks to the use of proprietary protocols and isolation from the telecommunication networks. Nowadays, the advent of the Industrial Internet of Things poses new security challenges. In this paper, we first highlight the main security challenges that advocate for new risk assessment and security strategies.To this end we propose a security framework and advanced tools to properly manage vulnerabilities, and to timely react to the threats. The proposed architecture fills the gap between computer science and control theoretic approaches. The physical layers connected to Industrial Control Systems are prone to disrupt when facing cyber-attacks. Considering the modules of the proposed architecture, we focus on the development of a practical framework to compare information about physical faults and cyber-attacks. This strat-egy is implemented in the ATENA architecture which has been designed as an innovative solution for the protection of critical assets.

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“…IDSs are able to distinguish between legal as well as illegal behavior based on normal behavior, patterns of good traffic, or specific regulations that define an attack. Data mining, which is used to explain knowledge discovery, may assist in the development and deployment of IDSs with greater accuracy and more resilient behavior as opposed to existing IDSs [7].…”

Section: Introductionmentioning

confidence: 99%

Improved restricted Boltzmann machine-based optimization model for the network security system in cloud environment

2024

Eng. Res. Express

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This paper introduces a novel approach to enhancing computer network security through deep learning and cloud technologies, focusing on combating insider DDoS attacks in cloud computing. By employing the IRBM architecture and a trust-oriented secure cloud framework, the work utilizes parameter tuning in RBM through the Gradient-Based Optimization (GBO) algorithm resulting in the proposed IRBM-GBO model. This model significantly outperforms SVM, SC-CNSP-ML, and RBM in terms of latency and Packet Delivery Ratio (PDR), showing 21.67%, 14.55%, and 9.62% improvements in latency, and 10.23%, 5.43%, and 12.79% in PDR, respectively. The research highlights the potential of combining soft computing with deep learning and cloud technologies to identify cyberattacks by emphasising improvements in latency, Quality of Service (QoS), throughput, and PDR across various cybersecurity datasets.

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SDSA: A framework of a software-defined security architecture

Cited by 11 publications

References 0 publications

HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design

HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design

Integrated protection of industrial control systems from cyber-attacks: the ATENA approach

Improved restricted Boltzmann machine-based optimization model for the network security system in cloud environment

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