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Due to the rapid growth and use of Emerging technologies such as Artificial Intelligence, Machine Learning and Internet of Things, Information industry became so popular, meanwhile these Emerging technologies have brought lot of impact on human lives and internet network equipment has increased. This increment of internet network equipment may bring some serious security issues. A botnet is a number of Internet-connected devices, each of which is running one or more bots.The main aim of botnet is to infect connected devices and use their resource for automated tasks and generally they remain hidden. Botnets can be used to perform Distributed Denial-of-Service (DDoS) attacks, steal data, send spam, and allow the attacker to access the device and its connection. In this paper we are going to address the advanced Botnet detection techniques using Machine Learning. Traditional botnet detection uses manual analysis and blacklist, and the efficiency is very low. Applying machine learning to batch automatic detection of botnets can greatly improve the efficiency of detection. Using machine learning to detect botnets, we need to collect network traffic and extract traffic characteristics, and then use X-Means, SVM algorithm to detect botnets. According to the difference of detection features, botnet detection based on machine learning technology is divided into network traffic analysis and correlation analysis-based detection technology. KEYWORDS: Botnet, Study, Security, Internet-network, Machine Learning, Techniques.

Convolutional neural networks in short form often called as CNN is a kind of Deep neural networks, it is the part of Deep Learning, based on their features Convolutional neural networks are also called as space invariant artificial neural networks. The Word Convolution is the name given to it as it creates complexity over anything. Applications of Convolutional neural networks will be treated as Significant due to their wide use in various diverse areas such as medical image analysis, recognition of images and moving objects, Medical image classification, Language processing’s etc. There exit regularized Convolutional neural networks for MPI-Multilayer perceptron’s due to their significance Convolutional neural networks take a variety of approach to deal with the regularization, they follow hierarchical patterns to achieve it which makes things simpler. The main motivation of convolutional neural networks is recognizing objects in a video. The reason behind the popularity of CNN are the neurons of CNNs are resembles the neurons of Living species which is the inspiration of Convolutional neural networks to accomplish desired tasks. There exists many popular object detection and object classifications with convolutional neural networks for a diversity of data sets. In this paper we are going to do a comparative study on convolutional neural networks for any real time image classification and object recognition. Convolutional neural networks have that much of capability to create optimized image classifications and object recognitions.