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In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.

The productions quality has become one of the essential issues in the modern manufacturing industry and several techniques have introduced for control and monitoring the production process. Control charts are the most practical and popular tools for continuously monitoring and, if required, make adjustments to the product or process. A new automatic method based on deep learning and optimization algorithms for nine control chart patterns (CCPs) recognition are proposed in this paper. This method has two principal parts: the classification part and the tuning part. In the last few years, a convolutional neural network (ConvNet) has led to an excellent performance on various tasks, like image processing, speech recognition, and signal processing. Therefore, in the classification part, ConvNet is used as the intelligent classifier for CCPs recognition. One significant difficulty of ConvNet is that it requires considerable proficiency to select suitable parameters like a number of kernels and their spatial sizes, learning rate, etc. The ConvNet parameters have domestic dependencies which make the tuning of these parameters a challenging task. According to these issues, in the tuning part of the proposed method, the Harris hawks optimization (HHO) algorithm is used for optimal tuning of ConvNet parameters. Contrasting the common CCPs recognition methods, the proposed method takes unprocessed data and passes to more than one hidden layer for extracting the optimal feature representation instead of relying on any feature engineering mechanisms. The quantitative and simulation results show the superiority of the proposed method over the previous techniques in terms of its performance.

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