Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail.

In this paper, four different block matching algorithms using motion estimation are evaluated where the effects of the macro block size used will be reviewed to find the best algorithm among them is scrutinized to determine the most optimal algorithm. Four different block matching algorithms are considered and implemented. Each algorithm is evaluated using different movies from the TRANS database [11] and comparisons are made through the Peak Signal to Noise Ratio (PSNR) and search points per macro block (i.e. computation time) for different sizes of macro blocks and search areas. The results suggest that among all the evaluated algorithms, ARPS has the best PSNR based on computation time

The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like structure to learn tasks. A successful deep learning technique method is convolution neural network (CNN); however, it is presently not suitable to detect anomalies. It is easier to identify expected contents within the input flow in CNNs, whereas there are minor differences in the abnormalities compared to the normal content. This suggests that a particular method is required for identifying such minor changes. It is expected that CNNs would learn the features that form the characteristic of the content of an image (flow) rather than variations that are unrelated to the content. Hence, this study recommends a new CNN architecture type known as mean convolution layer (CNN-MCL) that was developed for learning the anomalies content features and then identifying the particular abnormality. The recommended CNN-MCL helps in designing a strong network intrusion detection system that includes an innovative form of convolutional layer that can teach low-level abnormal characteristics. It was observed that assessing the proposed model on the CICIDS2017 dataset led to favorable results in terms of real-world application regarding detecting anomalies that are highly accurate and have low false-alarm rate as opposed to other best models.

Human action recognition from video has several potential to apply in different real-life applications, but the most cases in this field suffer from the variation in viewpoint. Most of published methods in this area are considered the performance of each single camera, therefore the change in the viewpoints significantly decrease the recognition rate. In this paper, multiple views are considered together and a method has proposed to recognize human action depicted in multi-view image sequences. In the first step, the border of the human body's silhouette is extracted and distance signal is calculated. In the next step, the wavelet transform is applied to extract coefficients of single-view features, and then the extracted features are combined to compose multi-view features. Finally a hierarchical classifier using support vector machine and Naïve Bayes classifiers is implemented to classify the actions. The average of overall action recognition accuracy for 12 actions using 5 different angles of views on the IXMAS dataset is 88.22. The results of experiments on the popular multi-view dataset have shown the proposed method achieves high and state-of-the-art success rates. In other word, combination of single-view extracted features from the wavelet approximation coefficients and composing the multi-view features can be used as the multi-view features. Further, the hierarchical classifier can be applied to recognize actions in multi-view human action recognition area.

In this paper we will analyze and study a solar power plant of a linear parabolic type after introducing it. Monitoring system via the Internet is designed for the plant according to present parameters. Besides studying different dynamics of the Internet as important components we discuss the quality and effectiveness of each Internet parameter in order to explain the Internet behavior. According to previous studies one important obstacle to implementing remote monitoring system is variable delays. We try to explain delay behavior by using results of this study and other projects. After studying delay behavior during different periods, dynamics related to the delay in the Internet are modeled by using system recognition technique. Wave Variable method is chosen as the best monitoring method based on the studies which were done on remote Monitoring methods and due to the variability of delays Internet. Solar power plants monitoring system via the Internet is finally designed and modeled according to this method and linear dynamic delay in the Internet.