CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

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.

Improving naive Bayes classifier by dividing its decision regions

Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.

Naive Bayes Classifier for Debris Flow Disaster Mitigation in Mount Merapi Volcanic Rivers,Indonesia,Using X-band Polarimetric Radar

Debris flow triggered by rainfall that accompanies a volcanic eruption is a serious secondary impact of a volcanic disaster.The probability of debris flow events can be estimated based on the prior information of rainfall from historical and geomorphological data that are presumed to relate to debris flow occurrence.In this study,a debris flow disaster warning system was developed by applying the Na?¨ve Bayes Classifier(NBC).The spatial likelihood of the hazard is evaluated at a small subbasin scale by including high-resolution rainfall measurements from X-band polarimetric weather radar,a topographic factor,and soil type as predictors.The study was conducted in the Gendol River Basin of Mount Merapi,one of the most active volcanoes in Indonesia.Rainfall and debris flow occurrence data were collected for the upper Gendol River from October 2016 to February 2018 and divided into calibration and validation datasets.The NBC was used to estimate the status of debris flow incidences displayed in the susceptibility map that is based on the posterior probability from the predictors.The system verification was performed by quantitative dichotomous quality indices along with a contingency table.Using the validation datasets,the advantage of the NBC for estimating debris flow occurrence is confirmed.This work contributes to existing knowledge on estimating debris flow susceptibility through the data mining approach.Despite the existence of predictive uncertainty,the presented system could contribute to the improvement of debris flow countermeasures in volcanic regions.

Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers

The Washington,DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41789 crashes at unsignalized intersections,which resulted in 14168 injuries and 51 fatalities.The economic cost of these fatalities has been estimated to be in the millions of dollars.It is therefore necessary to investigate the predictability of the occurrence of theses crashes,based on pertinent factors,in order to provide mitigating measures.This research focused on the development of models to predict the injury severity of crashes using support vector machines(SVMs)and Gaussian naïve Bayes classifiers(GNBCs).The models were developed based on 3307 crashes that occurred from 2008 to 2015.Eight SVM models and a GNBC model were developed.The most accurate model was the SVM with a radial basis kernel function.This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%.The GNBC produced the worst-performing model with an accuracy of 48.5%.These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand.

GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).

Machine Learning Aided Key-Guessing Attack Paradigm Against Logic Block Encryption

Hardware security remains as a major concern in the circuit design flow.Logic block based encryption has been widely adopted as a simple but effective protection method.In this paper,the potential threat arising from the rapidly developing field,i.e.,machine learning,is researched.To illustrate the challenge,this work presents a standard attack paradigm,in which a three-layer neural network and a naive Bayes classifier are utilized to exemplify the key-guessing attack on logic encryption.Backed with validation results obtained from both combinational and sequential benchmarks,the presented attack scheme can specifically accelerate the decryption process of partial keys,which may serve as a new perspective to reveal the potential vulnerability for current anti-attack designs.