IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Cryptographic approaches based on chaos theory provide a several new and promising avenues for developing safe picture encryption solutions. This paper aims to complicate the process of decrypting images by adding encryption with keys, this was achieved by applying the principle of the 3D-chaotic system with the encryption algorithm, so we present an image encryption algorithm called black mask by using an efficient Multidimensional Chaotic Map represented by Lorenz system. For the confusion process, the suggested approach is based on a keys stream generator. The process of confusion is initiated by a 256-bit secret keys, which is produced by a logistic maps. To make the cipher more dynamic in the face of any threat. The suggested digital image encryption technique, as well as its security analysis and implementation, are discussed in depth. The experimental results suggest that the proposed method for image encryption and transmission is both efficient and safe.

Researchers have extensively utilized optical orthogonal frequency division multiplexing (O-OFDM) in visible light communication (VLC) to achieve high data rate transmission for free spectrum bandwidth. The peak-to-average power ratio (PAPR) is the critical challenge for VLC systems-based O-OFDM that produces non-linearity and degrades performance. In this paper, a proposed model for PAPR reduction can be applied with different O-OFDM technologies. This model considered using -law companding with O-OFDM transmitter to compress high amplitude peaks and restore the signals using de-companding in the receiver. The obtained simulation results show an efficient achievement of about 75% PAPR reduction compared with the original O- OFDM for different techniques. Furthermore, The convolutional encoder with Viterbi decoder is used with our proposed model for improvement BER performance and tradeoff with PAPR. The BER performance for different coding schemes, O-OFDM technologies, and modulation orders has been graphed and compared. It can notice the convolutional encoder/Viterbi satisfies better BER than Hamming coding/decoding. However, the number of memory cells of the convolutional encoder plays an essential role in BER improvement.

Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy.

This article presents a decentralized controller/observer for nonlinear large- scale interconnected systems with actuator fault. The proposal integrates a robust proportional-integral-derivative (PID) controller with the unknown input observer (UIO) to achieve closed-loop robustness against the interactions and the actuator faults. In this scheme, the PID controller is tuned using the Bacterial foraging optimization algorithm (BFO) algorithm. On the other hand, the unknown input observer can diagnose the actuator faults from the controller input. A numerical example consisting of two subsystems is adopted to clarify the effectiveness of the suggested method with a guarantee that the state estimation error is asymptotically converged to zero. The actuator faults have been added to the second subsystem, keeping the first subsystem free of fault. The simulation results demonstrated the influence of the interactions between subsystems, verifying that the unknown input observer can detect the actuator faults despite the presence of these interactions between the subsystems.

Cryptography is a security strategy that prevents disclosure of the information while it is transit, in storage, or both. There are a variety of methods for maintaining data security, including utilizing light weight speed algorithms for encryption and parameter validation. Many algorithms have originated in the area of information protection, helping to assure the validity of the information generated. These include the following algorithms: SHA-1, SHA-2, SHA-3, AES, RC5, RSA, and more. In order to secure the legitimacy of the information and monitoring data , the speed of encryption and authentication must be critical. . Due to the necessity of fast and secure algorithms, these features are required. In this work, modification of the SHA-3 algorithm by introducing a new function called (the keccak function), which has an extremely quick execution time and a high level of security, also versatile cryptographic function. This change is implemented via the 2D chaotic system, which is geared towards generating random values for constants for the SHA3 algorithm. These constants values are generated by the SHA3 algorithm, and so are random and unguessable by the intrude. Statistical tests conducted by the National Institute of Standards and Technology (NIST) effectively outperformed the randomness of a proposed approach .The proposed algorithm shows lower execution time compared to previous studies, which is 0.041616sec for 1MB.

This Face detection is considering one of the important topics for recognizing human, it is the first step before the face recognition process, it is considered one of the biggest challenges in the field of vision computer. In recent years Many algorithms for detection have appeared, which depend on extracting the features of the human face, and works continue to develop them to this day. This paper aims to make a comparison between two of the most commonly face detection methods, Viola Jones (V_J) and YOLO v3. This comparison is made to determine which of the two algorithms is being most useful when used to detect faces in digital video. These algorithms are used in many applications, including image classification, medical analysis of image, and objects detection in real time (especially in surveillance cameras). Both algorithms are applied to detect faces in the real time video. The experimental results of a sample consists of 20 video frames show that V_J algorithm consumes less time in comparison with YOLO v3 algorithm, but its results are less accurate, unlike the YOLO v3 algorithm, which is slower in detect face with high accurate rate.

Due to sophisticated cyber-attacks, and to produce false alarms on suspicious or unusual behavior to monitor computer resources, Intrusion Detection Systems (IDSs) are required. Hence, Many Machine Learning (ML) and data mining techniques have been proposed to increase the effectiveness of IDSs, whereas current IDS algorithms are still struggling to perform effectively while many IDSs depend on a single classifier to detect intrusions. Single- classifier IDSs cannot achieve high accuracy and low false alarm rates because of zero-day attacks. In this paper, a hybrid ensemble method using AdaBoosting and Bagging for IDS is proposed. This study aims to identify unknown (zero-day attacks) and known (well-known) attacks. So, the proposed model comprises three stages. The first stage is preprocessing. The second stage involves the application of AdaBoosting and Bagging methods by four different classifiers modifying (i.e., Naïve Bayesian (NB), Support Vector Machine (SVM), random forest (RF), and K_Nearest Neighbor (KNN)). Such a modification is performed for the AdaBoosting methods. The AdaBoosting classifier is then combined to work in the Bagging method. For attack recognition, uses the voting technique as the third stage. Experimental results reveal that using the UNSW BN15 dataset yields testing with 85.49% accuracy, 99.96% detection rate, and 0.006 false alarm rate. Therefore, the proposed Hybrid AdaBoosting and Bagging Method (HABBM) can outperform other comparable and state-of-the-art techniques across a variety of parameters.

Computer worms execute damaging functions in the network systems, compromising system security. Although researchers use a variety of methods to detect worms and prevent their spread. Detecting worms remains a challenge for the following reasons: First, a huge volume of irrelevant data affects classification accuracy. Second, frequently used individual classifiers in systems are poor at detecting all types of worms, Third, many systems are built on out-of-date information, rendering them useless for new worm species. As a result, providing a network intrusion detection system is vital for ensuring security and reducing the harm caused by worms on networks to information systems. The goal of the study is to discover computer worms in the computer networks and protect the systems from their damages. The proposed method uses the UNSW NB15 dataset to train and test the ensemble Ada boosting and Bagging algorithms with the Support Vector Nachine (SVM) as a contribution rather than a decision tree. Due to Correlation Feature Selection (CFS) identifying relationships between features and classes, and Chi-square (Chi2) determining whether features and classes are independent or not, we combined these two algorithms as a contribution in a method called CFS&Chi2fs to select the relevant features and reduce the time. The system achieved accuracy reaching 0.998 with Bagging(SVM), and 0.989 with Ada boost(SVM).

Recently, the Internet of Things (IoT) is begin used in many fields such as smart homes, healthcare systems, industrial applications, etc. Therefore, the use of the IoT led to a growth in the number of dangers especially in the areas of privacy and security for applications running on low- resource computers. Consequently, the demand for lightweight encryption methods is growing. To safeguard sensing data, this study introduces a Lightweight Advanced Encryption Standard (LAES) depending on dynamic ShiftRows, initial permutation instead of MixColumns, and a dynamic number of rounds. It was created with the goal of reducing encryption/decryption time. The proposed approach was assessed by using various measurements such as lengths of the key used was 2128 and it is quite enough for security, key sensitivity values were 100%, Also, this study compared the encryption/decryption time, NIST statistical test, and security strength of the proposed architecture to those of XTEA, SIMON, Skinny, SPECK, and PRESENT. The encryption/decryption time of the proposed approach was had the shortest period (0.0169 S) while the SPECK algorithm was had the longest period (4.1249 S) among the comparative algorithms. Whereas, NIST statistical test values of the proposed approach were passed successfully and had higher values than the comparative algorithms. Moreover, the proposed approach utilized 1280, 1024, and 768 GE with 6, 8, or 10 rounds respectively. The average number of GE was approximately 1000 GE. These numbers of GE are considered highly efficient with the IoT environment.

The control technique for an exoskeleton system for lower limb rehabilitation is complicated, and numerous internal and external elements must be taken into account, in addition to the uncertainties in the system model. In this paper, through the analysis of the lower extremity exoskeleton is utilized to obtain the corresponding equation and its linearized form. The nonlinear differential equations have been linearized by using Jacobean’s method in order to facilitate the controller design. Considering the interior and external factors of the connecting rod, the uncertain elements are introduced and therefore the optimal control technique is applied to regulate the system. An optimal state feedback control strategy of Linear Quadratic Regulator (LQR), and LQR-Servo have been implemented in this work. Finally, the physical parameters of the Knee-Ankle Orthosis (KAO) exoskeleton are used, and the simulation results show the advantage and applicability of the proposed controller’s design of the Knee-Ankle orthosis system.

Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. The proposal uses the YOLO v5 (You Only Look Once) method to improve crowd recognition and counting. This algorithm is entirely accurate and detects things in real-time. The idea relies on edge enhancement and pre-processing to solve overlapping feature regions in the image and improve performance. The CrowdHuman data set is used to train YOLO v5. The system counts the number of humans in the image to detect a crowd. Before training, this model enhanced the image with several filters. The YOLO v5 algorithm distinguishes a person inside a crowd by utilizing the surrounding box on the head and overall body. Therefore, the number of head detection is x- coordinated compared to the body. Assume the detected heads outnumber the bodies. A square of the head will be extracted, but not a body square. Also, cropping the image reduces interference between human beings and enhances the edge features. Thus, YOLOv5 can detect it. The idea improves head and body detection by 2.17 and 4.1 percent, respectively.

Ease of access to digital images and the many images editing programs available, like photoshop. All this makes the Issue of protecting images against modification becomes essential. Some images contain crucial information that can risk a patient's life, such as medical images and e- government images that relate to citizen information and state or ministry security. The watermark was one of the essential methods for this type of protection, especially the fragile watermark, which is very sensitive to any attack. Because of its other characteristics, it was one of the techniques that proved its efficiency in detecting tampering and the authenticity of images— also, watermarking focuses on protecting the image itself, not about protecting the secret message. A fragile watermark is a watermarking which inserts some information to cover an image to secure it .fragile watermarking could use in such a way and implement in spatial or frequency domain or in both so, making it a hybrid watermarking scheme. The Paper presented set of fragile watermark techniques used by the researchers with the performance metrics of an algorithm used in spatial and frequency domains, also showing how to use artificial intelligence with a watermarking technique to protect Document images from manipulation and forgery.