The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
Internet of things(IoT)comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life.Generally,IoT nodes are connected in wireless media and thus they are ...Internet of things(IoT)comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life.Generally,IoT nodes are connected in wireless media and thus they are prone to jamming attacks.In the present scenario jamming detection(JD)by using machine learning(ML)algorithms grasp the attention of the researchers due to its virtuous outcome.In this research,jamming detection is modelled as a classification problem which uses several features.Using one/two or minimumnumber of features produces vague results that cannot be explained.Also the relationship between the feature and the class label cannot be efficiently determined,specifically,if the chosen number of features for training is minimum(say 1 or 2).To obtain good results,machine-learning algorithms are trained by large number of data sets.However,collection of large number of datasets to solve jamming detection is not easy and most of the times generation and collection of large data sets become paradigmatic.In this paper,to solve this problem,more number of features with nominal number of data’s is considered that eases the data collection and the classification accuracy.In this research,an efficient technique based on locality sensitive hashing(LSH)for K-nearest neighbor algorithm(K-NN),which takes less time for constructing and querying the hash table that gives good accuracy is proposed and evaluated.From the results,it is clear that the obtained results are validatable and the model is more sensible.展开更多
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
基金This research was supported by the Deanship of Scientific Research,Imam Mohammad Ibn Saud Islamic University,Saudi Arabia,Grant number:(20-13-09-004).
文摘Internet of things(IoT)comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life.Generally,IoT nodes are connected in wireless media and thus they are prone to jamming attacks.In the present scenario jamming detection(JD)by using machine learning(ML)algorithms grasp the attention of the researchers due to its virtuous outcome.In this research,jamming detection is modelled as a classification problem which uses several features.Using one/two or minimumnumber of features produces vague results that cannot be explained.Also the relationship between the feature and the class label cannot be efficiently determined,specifically,if the chosen number of features for training is minimum(say 1 or 2).To obtain good results,machine-learning algorithms are trained by large number of data sets.However,collection of large number of datasets to solve jamming detection is not easy and most of the times generation and collection of large data sets become paradigmatic.In this paper,to solve this problem,more number of features with nominal number of data’s is considered that eases the data collection and the classification accuracy.In this research,an efficient technique based on locality sensitive hashing(LSH)for K-nearest neighbor algorithm(K-NN),which takes less time for constructing and querying the hash table that gives good accuracy is proposed and evaluated.From the results,it is clear that the obtained results are validatable and the model is more sensible.
