(HIADIoT): Hardware Intrinsic Attack Detection in Internet of Things; Leveraging Power Profiling

2019 ◽  
Author(s):  
Hawzhin Mohammed ◽  
Tolulope A Odetola ◽  
Syed Rafay Hasan ◽  
Sari Stissi ◽  
Isaiah Garlin ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Attack Detection ◽  
Power Profiling

Botnet attack detection in Internet of Things devices over cloud environment via machine learning

2021 ◽  
Author(s):  
Muhammad Waqas ◽  
Kamlesh Kumar ◽  
Asif Ali Laghari ◽  
Umair Saeed ◽  
Muhammad Malook Rind ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Attack Detection ◽  
Cloud Environment

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 RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 183678-183689
Author(s):  
Semih Cakir ◽  
Sinan Toklu ◽  
Nesibe Yalcin
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Attack Detection ◽  
The Internet ◽  
The Internet Of Things

Attack detection in medical Internet of things using optimized deep learning: enhanced security in healthcare sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
J Aruna Santhi ◽  
G Vijaya Saradhi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Hybrid Algorithm ◽  
Attack Detection ◽  
Hospital Environment ◽  
Healthcare Sector ◽  
Content Type ◽  
Accurate Detection ◽  
Iot Devices ◽  
Better Than

PurposeThis paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.Design/methodology/approachThis paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.FindingsFrom the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.Originality/valueThis paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

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