Ward_p_HMM: A Shilling Attack Detection Technique Using Ward_p Method and Hidden Markov Model

2021 ◽  
Vol 25 (3) ◽  
Author(s):  
Keya Chowdhury ◽  
Abhishek Majumder ◽  
Joy Lal Sarkar ◽  
Sukanta Chakraborty ◽  
Sudipta Roy
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Attack Detection ◽  
Detection Technique ◽  
Shilling Attack ◽  
Shilling Attack Detection

An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network

2021 ◽  
Vol 15 (01) ◽  
pp. 35-41
Author(s):  
Choukri Djellali ◽  
Mehdi adda
Keyword(s):  
Deep Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Experimental Studies ◽  
Learning Model ◽  
Attack Detection ◽  
Success Factor ◽  
Network Attack ◽  
Deep Learning Model

In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental studies conducted on attack detection indicate that our proposed model outperformed existing Deep Learning models and gives an enhanced generalization.

scholarly journals Cloud Zero-Day Attack Detection Using Hidden Markov Model with Transductive Learning

2021 ◽  
Author(s):  
Sohel Rana ◽  
Md Alamin Hossan ◽  
Abidullha Adel
Keyword(s):  
Deep Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Original Data ◽  
Attack Detection ◽  
Transductive Learning ◽  
Trust Value ◽  
Value Network ◽  
Detection Model

Abstract In cloud security, detecting attack software is considered an essential task. Among several attack types, a zero-day attack is considered as most problematic because the antivirus cannot able to remove it. The existing attack detection model uses stored data about attack characteristics, which fails to detect zero-attack where an altered attack is implemented for an antivirus system to detect the attack. To detect and prevent zero-day attacks, this paper proposed a model stated as Hidden Markov Model Transductive Deep Learning (HMM_TDL), which generates hyper alerts when an attack is implemented. Also, the HMM_TDL assigns labels to data in the network and periodically updates the database (DB). Initially, the HMM model detects the attacks with hyper alerts in the database. In the next stage, transductive deep learning incorporates k-medoids for clustering attacks and assign labels. Finally, the trust value of the original data is computed and computed in the database based on the value network able to classify attacks and data. The developed HMM_TDL is trained with consideration of two datasets such as NSL-KDD and CIDD. The comparative analysis of HMM_TDL exhibits a higher accuracy value of 95% than existing attack classification techniques.

scholarly journals Research on SSDF Attack Detection Algorithm in Cognitive Internet of Things

2021 ◽  
Author(s):  
Miao Liu ◽  
Di Yu ◽  
Zhuo-Miao Huo ◽  
Zhen-Xing Sun
Keyword(s):  
Internet Of Things ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Hidden Markov ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Iot Devices ◽  
Cognitive Internet Of Things

Abstract The Internet of Things (IoT) is a new paradigm for connecting various heterogeneous networks.cognitive radio (CR) adopts cooperative spectrum sensing (CSS) to realize the secondary utilization of idle spectrum by unauthorized IoT devices,so that IoT objects can effectively use spectrum resources.However, the abnormal IoT devices in the cognitive Internet of Things will disrupt the CSS process. For this attack, we propose a spectrum sensing strategy based on the weighted combining of the Hidden Markov Model. In this method, Hidden Markov Model is used to detect the probability of malicious attack of each node and report it to the fusion center (FC). FC allocates a reasonable weight value according to the evaluation of the submitted observation results to improve the accuracy of the sensing results.Simulation results show that the detection performance of spectrum sensing data forgery(SSDF) attack in cognitive Internet of Things is better than that of K rank criterion in hard combining.

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