<span>The evolution of the internet of things as a promising and modern technology has facilitated daily life. Its emergence was accompanied by challenges represented by its frequent exposure to attacks and its being a target for intruders who exploit the gaps in this technology in terms of the nature of its heterogeneous data and its large quantity. This made the study of cyber security an urgent necessity to monitor infrastructures It has network flaw detection and intrusion detection that helps protect the network by detecting attacks early and preventing them. As a result of advances in machine learning techniques, especially deep learning and its ability to self-learning and feature extraction with high accuracy, the research exploits deep learning to analyze the real data set of CSE-CIC-IDS2018 network traffic, which includes normal behavior and attacks, and evaluate our deep model long short-term memory (LSTM), That achieves accuracy of detection up to 99%.</span>

<p>In this paper, developing high performance software for demanding real-time embedded systems is proposed. This software-based design will enable the software engineers and system architects in emerging technology areas like 5G Wireless and Software Defined Networking (SDN) to build their algorithms. An ADSP-21364 floating point SHARC Digital Signal Processor (DSP) running at 333 MHz is adopted as a platform for an embedded system. To evaluate the proposed embedded system, an implementation of frame, symbol and carrier phase synchronization is presented as an application. Its performance is investigated with an on line Quadrature Phase Shift keying (QPSK) receiver. Obtained results show that the designed software is implemented successfully based on the SHARC DSP which can utilized efficiently for such algorithms. In addition, it is proven that the proposed embedded system is pragmatic and capable of dealing with the memory constraints and critical time issue due to a long length interleaved coded data utilized for channel coding.</p>

The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes increasing in attacks. Malicious attacks are continuously changing, that cause new attacks. In this paper we present a survey about the detection of anomalies, thus intrusion detection by distinguishing between normal behavior and malicious behavior while analyzing network traffic to discover new attacks. This paper surveys previous researches by evaluating their performance through two categories of new datasets of real traffic are (CSE-CIC-IDS2018 dataset, Bot-IoT dataset). To evaluate the performance we show accuracy measurement for detect intrusion in different systems.

Today it has become important to reconfigure the networks in to new form to be more manageable, scalable, dynamic and programmable. The networks recently are so inflexible and failing to deal with the required changes for the Information Technology. Software Defined Networking (SDN) is a modern paradigm that focused to change the main idea of current network infrastructure (traditional network) by breaking the chain between the data forwarding and the control planes to introduce flexible programmability network. This paper makes comparison between the performance of traditional fat-tree network and SDN fat-tree network, which found that average Round Tripe Time (RTT) in SDN fat-tree topology will decrease by 8.96% than traditional fat-tree topology. Then shows the basic operation of OpenFlow protocol that can be applied on fat-tree topology by using SDN technology and how that can be effect on the performance of network and make it more flexible to enable the SDN module applications, like load balancer and firewall for optimizing the SDN network. In this paper the physical switches are replaced by software switches in a virtual network environment and display the SDN structure in GUI, also Floodlight controller is chosen to use as the network operating system for SDN network.

Cardiovascular diseases (CVDs) are consider  the main cause  of death today According to World Health Organization (WHO),and because that ECG signal is very important tool in monitoring and diagnosis of these disease , different automatic methods were proposed based on this signal. [1]. The manual analysis of ECG signals is suffered different challenges such as differeculty of detecting and classify waveform of this signal, So, many machine learning methods  are  explored to describe  the anomalies ECG signal accurately . Deep learning (DL) can be used in ECG classification, it can improve the quality of the automatic classification system. In this paper , we have proposed a deep learning classification system by using  different layers of convolution, rectifier and pooling operations  that can be used to increase feature extraction of ECG signal.        We have proposed two models, one is used for input signal of 1-D, in which we designed model for classification csv type of data for ECG signal, while in the second proposed system, we used model for 2-D signal after convert it from its csv type .  2-D signal (ECG image) is used in order to augment the two dimensional signal with different methods to increase the accuracy of the model by training it with geometric transformation of the original input images such as rotation, shearing etc.The results are compared with AlexNet and other  models  based on the metrics, which are    used to measure the performance of the proposed work, the result show that, the proposed models improve the efficiency of the classification  in the two systems.