As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medi...As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.展开更多
Blockchain is a technology that provides security features that can be used for more than just cryptocurrencies.Blockchain achieves security by saving the information of one block in the next block.Changing the inform...Blockchain is a technology that provides security features that can be used for more than just cryptocurrencies.Blockchain achieves security by saving the information of one block in the next block.Changing the information of one block will require changes to all the next block in order for that change to take effect.Which makes it unfeasible for such an attack to happen.However,the structure of how blockchain works makes the last block always vulnerable for attacks,given that its information is not saved yet in any block.This allows malicious node to change the information of the last block and generate a new block and broadcast it to the network.Given that the nodes always follow the longer chain wins rule,the malicious node will win given that it has the longest chain in the network.This paper suggests a solution to this issue by making the nodes send consistency check messages before broadcasting a block.If the nodes manage to successfully verify that the node that generated a new block hasn’t tampered with the blockchain than that block will be broadcasted.The results of the simulation show suggested protocol provided better security compared to the regular blockchain.展开更多
文摘As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.
基金supported by research fund of Chungnam National University.
文摘Blockchain is a technology that provides security features that can be used for more than just cryptocurrencies.Blockchain achieves security by saving the information of one block in the next block.Changing the information of one block will require changes to all the next block in order for that change to take effect.Which makes it unfeasible for such an attack to happen.However,the structure of how blockchain works makes the last block always vulnerable for attacks,given that its information is not saved yet in any block.This allows malicious node to change the information of the last block and generate a new block and broadcast it to the network.Given that the nodes always follow the longer chain wins rule,the malicious node will win given that it has the longest chain in the network.This paper suggests a solution to this issue by making the nodes send consistency check messages before broadcasting a block.If the nodes manage to successfully verify that the node that generated a new block hasn’t tampered with the blockchain than that block will be broadcasted.The results of the simulation show suggested protocol provided better security compared to the regular blockchain.
