Iraqi Journal of Information and Communication Technology https://ijict.edu.iq/index.php/ijict <p>The Iraqi Journal of Information &amp; Communication Technology (IJICT) is a quarterly published specialized, referred, and indexed journal published by the college of Information Engineering at Al–Nahrain University, Baghdad, Iraq. IJICT invites contributions from researchers, scientists and practitioners from all over the world.</p> College of Information Engineering | Al-Nahrain University en-US Iraqi Journal of Information and Communication Technology 2222-758X DETECTION OF ANOMALOUS EVENTS BASED ON DEEP LEARNING-BILSTM https://ijict.edu.iq/index.php/ijict/article/view/207 <p>Video anomaly detection in smart cities is a critical errand in computer vision that plays an imperative role in intelligent surveillance and public security but is challenging due to its differing, complex, and rare event in real-time surveillance situations. Different deep learning models utilize a critical amount of training data without generalization capabilities and with long time complexity. In this work, and to overcome these problems, an algorithm for reducing the size of the extracted features have been suggested, and this was done by combining every 15 video frames to generate the new features vectors which will be fed into our classifier model, the values of new features vectors represent the summation of the values of original features vectors got from Resnet50. Finally, the new feature vectors are fed into our classifier model to detect the abnormality. We conducted comprehensive tests on a variety of anomaly detection benchmark datasets to verify the proposed framework's functionality in complex surveillance scenarios. The Numerical results were carried out on the UCF-Crime dataset, with the proposed approach achieving Area Under Curve (AUC) scores of 93.61% on the database's test set.</p> Zainab K. Abbas Ayad A. Al-Ani Copyright (c) 2022 Iraqi Journal of Information and Communication Technology 2022-12-30 2022-12-30 5 3 34 42 10.31987/ijict.5.3.207 MOBILE CLOUD COMPUTING SERVICES AUTHENTICATION SCHEME https://ijict.edu.iq/index.php/ijict/article/view/201 <p>Because of the rapid growth of cloud computing and the expansion of mobile phone users in recent years, mobile cloud computing has attracted wide attention. In the mobile cloud, wireless computing networks are the basics of sharing data between mobile devices and cloud services. Since air is the communication medium, it must be properly protected; otherwise, it will be subject to a variety of security threats, for example, attacks from middle-man, identity tracking, etc... Furthermore, mobile devices are limited in storage, resources, and computing powers. Hence, designing an efficient and secure balance of authentication schemes is extremely important. First of all, a multi-factor authentication scheme based on biometric (fingerprint information), hash function, and fuzzy vault algorithm is presented in that paper. Secondly, the Validation and Analysis tool of AVISPA Security was approved. Thirdly, the security of the scheme proposed is compared to other related schemes. &nbsp;</p> Safana A. Abdulrahman M. F. Al-Gailani Copyright (c) 2022 Iraqi Journal of Information and Communication Technology 2022-12-30 2022-12-30 5 3 1 14 10.31987/ijict.5.3.201 AN EFFECTIVE AND EFFICIENT FEATURES VECTORS FOR RANSOMWARE DETECTION VIA MACHINE LEARNING TECHNIQUE https://ijict.edu.iq/index.php/ijict/article/view/205 <p>Ransomware is a high major danger program that may harm any company or person and cost them hundreds of billions. Its number growing rapidly across the years. &nbsp;As a result, creating a strong defense strategy against this crucial virus is required. Ransomware has grown in importance, and its consequences are becoming more severe. To solve the problem of effectively detecting ransomware, so this paper introduces a new technique to detect ransomware based on five machine learning techniques. To evaluate the proposed method, different evaluation metrics have been used. &nbsp;The approach was captured n-gram characteristics based on static analysis and used n-gram vector with CF-NCF values to build the models. Using real datasets, the proposed approach shows Its ability to reliably identify between goodware and ransomware files successfully with an accuracy of classification of equal to 98.33%.</p> Nawaf A. Khalil Ban M. Khammas Copyright (c) 2022 Iraqi Journal of Information and Communication Technology 2022-12-30 2022-12-30 5 3 23 33 10.31987/ijict.5.3.205 EVALUATION OF QUANTUM KEY DISTRIBUTION BY SIMULATION https://ijict.edu.iq/index.php/ijict/article/view/157 <p>Quantum key distribution is a secure method for exchanging keys across communication entities. The first quantum key distribution (QKD) protocol, BB84, was introduced in 1984. The primary paradigm of QKD was initiated for a depolarizing channel. This paper demonstrates the ability to use MATLAB to simulate QKD. It has been found that the security of the QKD protocol depends on a variety of factors, including the amount of photon input and the severity of the Eve attacks. It turned out that the high level of Eve’s attack, according to the data, indicates a lack of protection.</p> Harith A. Qaisi M. F. Al-Gailani Copyright (c) 2022 Iraqi Journal of Information and Communication Technology 2022-12-30 2022-12-30 5 3 15 22 10.31987/ijict.5.3.157