A Blockchain‐based Cyber Attack Detection Scheme for Decentralized Internet of Things using Software‐Defined Network

2021 ◽  
Author(s):  
Deepsubhra Guha Roy ◽  
Satish Narayana Srirama
Keyword(s):  
Internet Of Things ◽  
Attack Detection ◽  
Software Defined Network ◽  
Cyber Attack ◽  
Detection Scheme

RAD-EI: A routing attack detection scheme for edge-based Internet of Things environment

2019 ◽  
Vol 32 (15) ◽  
pp. e4024 ◽  
Author(s):  
Mohammad Wazid ◽  
Poonam Reshma Dsouza ◽  
Ashok Kumar Das ◽  
Vivekananda Bhat K ◽  
Neeraj Kumar ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Attack Detection ◽  
Detection Scheme ◽  
Internet Of Things Environment ◽  
Routing Attack ◽  
Edge Based

scholarly journals IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1280-1303
Author(s):  
Jiushuang Wang ◽  
◽  
Ying Liu ◽  
Huifen Feng
Keyword(s):  
Internet Of Things ◽  
Firefly Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Attack ◽  
Improved Firefly Algorithm ◽  
Iot Devices ◽  
Ddos Attack Detection

<abstract><p>Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.</p></abstract>

scholarly journals A context-aware information-based clone node attack detection scheme in Internet of Things

2021 ◽  
pp. 103271
Author(s):  
Khizar Hameed ◽  
Saurabh Garg ◽  
Muhammad Bilal Amin ◽  
Byeong Kang ◽  
Abid Khan
Keyword(s):  
Internet Of Things ◽  
Attack Detection ◽  
Context Aware ◽  
Detection Scheme

scholarly journals A Distributed Cyber-Attack Detection Scheme With Application to DC Microgrids

2020 ◽  
Vol 65 (9) ◽  
pp. 3800-3815
Author(s):  
Alexander Julian Gallo ◽  
Mustafa Sahin Turan ◽  
Francesca Boem ◽  
Thomas Parisini ◽  
Giancarlo Ferrari-Trecate
Keyword(s):  
Attack Detection ◽  
Cyber Attack ◽  
Detection Scheme ◽  
Dc Microgrids

Deep Learning SDN Intrusion Detection Scheme Based on TW-Pooling

2019 ◽  
Vol 23 (3) ◽  
pp. 396-401
Author(s):  
Qingyue Meng ◽  
Shihui Zheng ◽  
Yongmei Cai ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Attack Detection ◽  
Numerical Control ◽  
Software Defined Network ◽  
Traffic Data ◽  
Control Plane ◽  
Data Set ◽  
Detection Scheme ◽  
Entire Network

The numerical control separation in the Software-Defined Network (SDN) allows the control plane to have the absolute management rights of the network. As a new management plane of the SDN, once it is attacked, it will cause the entire network to face flaws. For this reason, this paper proposes a SDN control plane attack detection scheme based on deep learning, which can detect and respond to attacks on the SDN control plane in time. In this scenario, we propose a new pooling scheme that uses the TF-IDF idea to weight the characteristics of network traffic. Ultimately, our method achieved an accuracy of 99.8% in the SDN network’s traffic data set including 24 attack types.

scholarly journals Cyber Attack Detection Scheme for a Load Frequency Control System Based on Dual-Source Data of Compromised Variables

2021 ◽  
Vol 11 (4) ◽  
pp. 1584
Author(s):  
Wenjun Bi ◽  
Kaifeng Zhang ◽  
Chunyu Chen
Keyword(s):  
Frequency Control ◽  
Measured Data ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Load Frequency Control ◽  
Cyber Attack ◽  
Detection Scheme ◽  
Load Frequency ◽  
Source Data ◽  
Dual Source

Cyber attacks bring key challenges to the system reliability of load frequency control (LFC) systems. Attackers can compromise the measured data of critical variables of the LFC system, making the data received by the defender unreliable and resulting in system frequency fluctuation or even collapse. In this paper, to detect potential attacks on measured data, we propose a novel attack detection scheme using the dual-source data (DSD) of compromised variables. First, we study the characteristics of the compromised LFC system considering potentially vulnerable variables and different types of attack templates. Second, by designing a variable observer, the relationship between the known security variables and the variables which are at risk of being compromised in the LFC system is established. The features of the data obtained by the observer can reflect those of the true data. Third, a Siamese network (SN) is designed to quantify the distance between the characteristics of measured data and that of observed data. Finally, an attack detection scheme is designed by analyzing the similarity of the DSD. Simulation results verify the feasibility of the detection scheme studied in this paper.

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