<p>The emergence of the Internet of Things (IOT) as a result of the development of the communications system has made the study of cyber security more important. Day after day, attacks evolve and new attacks are emerged. Hence, network anomaly-based intrusion detection system is become very important, which plays an important role in protecting the network through early detection of attacks. Because of the development in  machine learning and the emergence of deep learning field,  and its ability to extract high-level features with high accuracy, made these systems involved to be worked with  real network traffic CSE-CIC-IDS2018 with a wide range of intrusions and normal behavior is an ideal way for testing and evaluation . In this paper , we  test and evaluate our  deep model (DNN) which achieved good detection accuracy about  90% .</p>

To prevent detection, attackers frequently design systems to rearrange and rewrite their malware automatically. The majority of machine learning techniques are not sufficiently resistant to such re-orderings because they develop a classifier based on a manually created feature vector. Deep learning techniques like convolutional neural networks (CNN) have lately proven to perform better than more traditional learning algorithms, especially in applications like picture categorization. As a result of this success, CNN network proposed with data augmentation techniques (to enhance the performance) to classify malware samples. We trained a CNN to classify the photos using converted grayscale images from malware files. Our methodology outperforms other methods with an accuracy of 98.80%, according to experimental results.

Streaming of video over wireless heterogeneous networks coping with the problem of packet loss which affects the perceived video quality. The service providers usually use the Peak Signal to Noise Ratio PSNR as a metric measure for the quality of their provided service. So they use the quality of service QoS of the network as a sign on the quality of their presented service. The QoS deal with the objective tests of the provided service, which mean the measure of PSNR of the presented objects. The presented objects may not get the satisfaction of the network users due to many factors although that the PSNR of the used service is enough for presenting the service. Recently the service providers use the Quality of Experience QoE term which deal with the subjective test of the presented object (i.e. the user satisfaction measure). In this paper we propose a new model to identify the importance or the significance of the role of the QoE assessment for the service providers. To verify our proposed model we did a referendum for 55 participants in order to assess their judgment on the quality of some presented videos. The results of the referendum match the consideration of the proposed model.

With the adoption of assorted gadgets and technology loaded devices, there is need to work on security and privacy while using such platforms. Now, the focus of concern has turned to the overwhelming secrecy, the high performance security, and integrity of the transactions in the cyber space. In relation to a chain of records which is interlinked and highly encrypted due to the involving hashing and encryption each process, it is known as a blockchain. The blockchain removes the possibility of a fraudulent or accidental tampering with the framework. Blockchain has the ability to store sensor data, as well as the capacity to thwart data falsification. IoT deployment plans are usually complex, and the distributed ledger is particularly well-suited for Internet of Things (IoT) discovery, authentication, and recording of information. Wireless body networks are set to be published here on the use trends of Blockchain deployment using advanced scripting and embedded technology the included incorporation of effectual effects gives the final results as well as opposed to the conventional cryptography approach to security.

Network Intrusion Detection System (NIDS) detects normal and malicious behavior by analyzing network traffic, this analysis has the potential to detect novel attacks especially in IoT environments. Deep Learning (DL)has proven its outperformance compared to machine learning algorithms in solving the complex problems of the real-world like NIDS. Although, this approach needs more computational resources and consumes a long time. Feature selection plays a significant role in choosing the best features only that describe the target concept optimally during a classification process. However, when handling a large number of features the selecting such relevant features becomes a difficult task. Therefore, this paper proposes Enhanced BPSO using Binary Particle Swarm Optimization (BPSO) and correlation–based (CFS) classical statistical feature selection approach to solve the problem on BPSO feature selection. The selected feature subset has evaluated on Deep Neural Networks (DNN) classifiers and the new flow-based CSE-CIC-IDS2018 dataset. Experimental results have shown a high accuracy of 95% based on processing time, detection rate, and false alarm rate compared with other benchmark classifiers.