Iraqi Journal of Information and communication technology

With the growth of processed data for wireless networks and the establishment of new applications and services, mobile operators keep searching for solutions to deal with the issues raised by next generation networks (NGN). The fifth generation (5G) mobile networks, for example, should enhance their performance without consuming a lot of energy. For these reasons, software-defined networks (SDN) appear to be the promising technology that will make achieving the architectural agility required for the upcoming 5 G mobile networks easier. SDN is a clever solution for providing innovation and enforcing the primary drivers in5Gmobile networks, such as flexibility, dependability, service-oriented management, and cost reduction through the control and management of the 5G core network. In this paper, the integration of SDN and multiple controllers with the 5G core network is investigated to determine how these two technologies can affect mobile IP network performance. An energy model suitable for the proposed network architecture has been developed. A widely used Diamond network is being considered for the core network. Open-Flow controllers were used to improve the routing performance by considering load balancing of IP traffic. According to such a model, reduced energy consumption is experienced with SDN. The addition of an SDN controller results in a11%reduction in consumed energy. The results also show that the use of SDN and multi-controller with NGN can improve the performance further. Using SDN reduced the average delay of the network by14%. A great reduction in packet error rate (PER) is also achieved when SDN is used.

Internet of Things (IoT) equipment is rapidly being used in a variety of businesses and for a variety of reasons (for example, sensing and collecting data from the environment in both public and military settings).Because of their expanding involvement in a wide range of applications and their rising computational and processing capabilities, they are a viable attack target for malware tailored to infect specific IoT devices. This study investigates the potential of detecting IoT malware using different deep learning techniques: the classic feedforward neural network (FNN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The proposed method analyses the execution operation codes of IOT app sequences using modern NLP (natural language processing) methods. The current work utilized an IoT application dataset with 500 malware(collected from the IOTPOT dataset) and 500 good ware samples to train the proposed algorithms. The trained model is tested against 2971 fresh IoT malware and good ware samples. The samples were input into deep learning models, and performance metrics were obtained. The results demonstrate that the RNN model had the best accuracy(99.19%)in detecting fresh malware samples. On the other hand, the results were compared by the time required for training; the CNN model shows that it could achieve high accuracy(98.05%)with less training time. A comparison with various deep learning classifiers demonstrates that the RNN and CNN techniques produce the best result

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into80%for training and20%for testing, with5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is95.6%and99.5%, respectively. Finally, from the results, it is obvious that the number of moments selected by the SP should exceed30%of the overall EEG samples for accuracy to be over90%.

Orthogonal Frequency Division Multiplexing-Index Modulation (OFDM-IM) based on chaos is a communication technique that uses chaotic systems to enhance the security of OFDM-IM systems. In this technique, chaotic signals are used to modulate the index values of the subcarriers and the data using DCSK, resulting in amore robust and efficient transmission. OFDM-IM uses the activated index as an information-carrying unit, which increases data rate and spectral efficiency. In this paper, we propose a secure index strategy for our system using a logistic map and He non map to generate the chaotic signals, considering additive white Gaussian noise (AWGN)and the Raleigh fading channel. A lookup table based on the chaotic system was created using the indices of a randomized index modulation scheme with DCSK modulation. Even if the active subcarriers and their symbols are correctly calculated until the opening frame, the randomized index modulation prevents eavesdroppers from accurately determining message bits.

Video steganography is a common field of data security in digital communication where data include text, image, and video. Compressed video is a suitable modern source for hiding huge secret data that will trust security. This work proposes a compressed video steganography scheme for hiding cipher secret data at high-security levels. The proposed work consists of four phases, protected secret data (Text in English, Text in Arabic, Image, and Video) based on the chaotic maps module and RC4 algorithm. The chaotic maps module is a combination of(a He non map and a sine map) with simple operations for key generation. As for the RC4 algorithm, it is one of the encryption algorithms of stream data, and its weakness has been addressed by strengthening the key in the proposed module for key generation, so it will be strong. Second phase, hidden cipher secret data between the quantized discrete cosine transform (QDCT) and entropy coding from the inter-prediction mode at the H.264/ AVC video frames using Least Significant Bit (LSB) replacement. The third phase is transmitting and receiving secret data. The fourth phase, recover plain data from the compressed video steganography. The experimental result shows that the proposed scheme is a high-security level where cipher secret data with an average entropy value of (7.9992), an average PSNR value of (8.790 dB), an average correlation value of (0.0042), a key length is 228 bit and key space is2228. Moreover, the measured Randomness of the generation key by NIST succeeded in all the tests of Robustness, and imperceptibility. Embedding capacity is high while maintaining visual quality, where the average PSNR value is about (36.65 dB), and the average SSIM value is about (0.95).

This research is an attempt to highlight that the presented antenna design is proposed for modern wireless communication systems, including 5G networks. The proposed antenna design is structured to realize are configurable antenna terminal to suit smart wireless systems. Therefore, the proposed antenna is structured from twelve-unit cells of metamaterial (MTM) inclusions. For this, effectively, the proposed antenna profile is reducedto0.21λ0, whereλois the free space wavelength at2.45GHz, which occupies40×30 mm2equivalently. This is accomplished by using T-resonator inductors to conduct3rditeration Hilbert-shaped MTM inclusions. The proposed antenna is printed on a substrate to cover the frequency bands from2.6GHzto 4.4GHz. To optimize the antenna performances, a numerical-parametric analysis based on CST MWS commercial software package formulations is invoked for this study. The suggested antenna performance is numerically evaluated for validation using the HFSS soft

Technological developments in the domains of electronics and telecommunication have led to an expanding variety of arrangements in the home and office. This project presents the design methodology for a mobile fingerprint-based student attendance system that utilizes a global system for mobile communication (GSM)technology. This course of study mostly entails matching fingerprints based on the number of minute pairings between two fingerprints and extracting minutiae points from model fingerprint pictures. The system has an attendance module and a terminal for collecting fingerprints. It is capable of autonomously carrying out tasks like fingerprint data collection and processing as well as wireless communication, creating an attendance record, and matching fingerprints. Following the attendance check, Through GSM, this technology transmits each student’s attendance to their parent’s mobile device. An attendance system makes it easier to obtain a specific student’s attendance for a given class. This effort heralds a new era for parent alerts and student security.