Mesopotamian Journal of CyberSecurity

As it tries to incorporate computer-based technology into the public services provided by businesses or organisations, the digital transformation process is currently regarded one of the most prominent topics in circulation. Establishing fundamentals and points is necessary for digital transformation, as is relying on a specific set of employee talents and actively incorporating customers in the process's evolution. Cybersecurity has become an increasingly important issue for governments and businesses worldwide, and this shift towards digital services is a top priority for all of them. Accelerating the digital transformation process and the usage of its services are the vulnerabilities to cyberspace, the advancement of technology and gadgets, and the employment of artificial intelligence in the development of current apps. In order to build cybersecurity governance that can be relied upon and is useful in accomplishing duties without hacking and tampering with data and information, it is necessary to implement simple programmes and tactics. This article emphasises the significance of cybersecurity governance in delivering secure and efficient technical means that can meet all risks and challenges and protect persons' data across all industries.

Cryptography is the practice of secure communication and the study of secure communication techniques. The theoretical foundations of cryptography include concepts such as cryptography, secure key exchange, digital signatures, and authentication. These technologies are used to protect the confidentiality, integrity, and reliability of information while it is being transmitted over a network or stored in digital form. The core of encryption is the concept of an encryption algorithm, which is a set of mathematical rules used to encrypt and decrypt data. The security of a cryptographic system depends on the strength of the underlying algorithm and the confidentiality of the key used to encrypt and decrypt the data. In this paper, Cryptography concepts and modern techniques are reviewed for types of encryption algorithms and the best ones, and many types of attacks and their impact on the encryption process

ChatGPT, the cutting-edge language model developed by OpenAI, is one of the most exciting advancements in the field
of artificial intelligence. With its ability to generate human-like text and respond to complex questions, ChatGPT has
already made a significant impact and is poised to continue its rapid progression in the coming years. As we look to the
future of ChatGPT and large language models, there are many exciting possibilities and open opportunities for this
technology to enhance our lives and change the way we interact with technology

ChatGPT is a large language model developed by OpenAI. It is trained on a dataset of conversational text and can be used to generate human-like responses to prompts in a variety of languages and formats. It can be used for tasks such as chatbots, language translation, and text completion. The role of ChatGPT is to generate human-like text based on a given prompt or context. It can be used in a variety of applications such as chatbots, language translation, text completion, and question answering. Additionally, it can be fine-tuned for specific tasks such as generating product descriptions or summarizing articles. It can also be used to generate creative writing such as poetry and stories. It can be integrated into a wide range of industries from customer service to entertainment, to research.

Security for IoT gadgets is an undertaking that has been made more troublesome by the far-reaching utilization of network safety in different applications, including wise modern frameworks, homes, individual devices, and vehicles. The fact that has been introduced makes deep learning for interruption recognition one productive security method. I thought about a few relevant systematic reviews that had already been written. Recent systematic reviews may include older and more recent works on the subject. For better IoT security, late exploration has focused on improving deep learning calculations. The ideal methodology for carrying out interruption recognition in the Internet of Things is determined by looking at the exhibition of different deep learning executions and investigating interruption location techniques that utilise them. Convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are the deep learning models used in this review. A standard dataset for IoT interruption identification is considered to evaluate the proposed model. The practical information is then investigated and diverged from current IoT interruption discovery strategies. In contrast with currently utilized approaches, the recommended strategy seems to have the best precision.

SQL injection is a type of security vulnerability that occurs in database-driven web applications where an attacker injects malicious code into the application to gain unauthorized access to sensitive information. This paper aims to provide a comprehensive and systematic review of the existing methods for preventing and detecting SQL injection attacks. The review covers a range of techniques, including input validation, parameterized queries, and intrusion detection systems, as well as the advantages and disadvantages of each method. The most common prevention techniques include input validation, parameterized queries, and stored procedures, while the most common detection techniques include intrusion detection systems (IDS), honeypots, and signature-based detection. The choice of method will depend on the specific requirements of the organization and the level of security required. Still, a combination of prevention and detection methods is likely to be the most effective way to secure web applications against SQL injection attacks. The paper concludes that SQL injection attacks continue to be a significant security threat to web applications, and it is essential for organizations to implement effective prevention and detection methods to secure their web applications against SQL injection attacks.

Deepfake videos are becoming an increasing concern due to their potential to spread misinformation and cause harm. In this paper, we propose a novel approach for accurately detecting deepfake videos using the combination of Convolutional Neural Networks (CNNs) with the Jaya algorithm optimization. The approach is evaluated on two publicly available datasets, the DeepFake Detection Challenge (DFDC) dataset and the Celeb-DF dataset, and achieves state-of-the-art performance on both datasets. Our approach achieves an accuracy of 99.3% on the DFDC dataset and 97.6% on the Celeb-DF dataset, with high F1 scores indicating a high precision and recall for detecting deepfake videos. Furthermore, our approach is more robust against adversarial attacks than existing state-of-the-art methods. The combination of CNNs with the Jaya algorithm optimization enables effective capture of the temporal information in the video sequence, while the use of robust evaluation metrics ensures objective measurement and comparison with existing methods. Our proposed approach offers a highly effective solution for detecting deepfake videos, which has the potential to be a valuable tool for media forensics, content moderation, and cyber security.

The growth of internet become more development in communication medium to provide various services. Information sharing ad security is mostly suffered by crime attackers because of different models of cyber-attacks are carried out by attackers. Attackers creates jamming principles, communication delays, packet dropping, and information hacking, duplicate injection to do so many activities to destroy the security. Based on the communication data analysis and features are non-identified and difficult to find the malicious activities. So the development of cyber security needs advancement to find the attackers based on the communication breaking activities. To resolve this problem, we propose a Spectral entity feature selection based Cyber Crypto Proof Security Protocol (C2PSP) to improve the cyber security. The Defect Scaling Rate (DSR) is used to estimate the communication defect rate. By marginalize the scaling rate using Spectral entity feature selection approach (SEFSA) is applied to select the features and trained to identify with Artificial neural network classifier (ANN). Based on the attack principles and activities in communication medium, the Cyber Crypto Proof Security Protocol (C2PSP) is applied to ensure the security verification and validation to process the data safer and securely. The proposed system produce high performance compared to other system as well to identify the malicious activities to improve the security against the cyber-attacks

Cyber security is developing factor for protecting internet resources by handing various monitoring feature based support to improve the security. Increasing internet cries in the defined facts for need of advance met in cyber security. Most internet attacker’s theft the information through malicious activities, false data injection, hacking and make soon creating procedures. In most cases cyber sercuity failed to detect the malicious activities because the monitoring feature analyses improper to predict the result in previous machine learning algorithms. TO resolve this problem to propose an advance cyber security based on flow-based anomaly detection using Min max game theory optimized artificial neural network (MMGT-ANN). The reprocessing was carried out with KDD crime dataset. Then Data driven network model is applied to monitor the feature margins and defect scaling rate. Based on the feature scaling rate Transmission Flow defect rate is estimated and applied with Min max Game theory to select the feature limits. Then features are trained with optimized ANN to detect the crime rate. By the attention of the proposed system achieves higher performance in precision rate to attain higher detection accuracy with lower time complexity compared to the other system.

The term cybersecurity refers to an environment capable of protecting digital devices, networks and information from unauthorized access and preventing data theft or alteration. It is composed of a collection of carefully crafted techniques, processes, and practices to protect sensitive information and deterring cyber-attacks. In the recent period, the domain of cybersecurity has undergone rapid growth in response to the increasing cyber threats. Cybersecurity includes important tactics that help protect the digital environment, which are firewalls, encryption, secure passwords, and threat detection and response systems. Employees must be trained on these tactics. This article will discuss the five most pressing challenges facing the cybersecurity industry today that must be taken into account by businesses, organizations, and individuals in order to secure their confidential data from cybercrime. The conclusion of the article highlighted the significance of growing awareness about cybersecurity risks in order to effectively handle digital environments and protect them from any electronic threats.

Nowadays, we are moving towards cybersecurity against digital attacks to protect systems, networks, and data in developing areas. A collection of technologies and processes is at the core of cybersecurity. A network security system is a feature of network and computer (host) security. Cybercrime leads to billion-dollar losses. Given these crimes, the security of computer systems has become essential to reduce and avoid the impact of cybercrime. We propose the Logistics Decision Support Vector (LDSV) algorithm dealing with this problem. Initially, we collected the KDD Cup 99 dataset to create a network intrusion detection, such as penetrations or attacks, a prognosis model that varies between the \"Non Malicious\" and \"Malicious\" standard links. These method finds the cyber-attack category based on the behavior features. In the second step, data preprocessing should be cleaned from errors, and raw data should be converted into a prepared dataset. The third step is Feature Selection (FS) techniques often improve the feature selection process in an Intrusion Detection System (IDS) that is more convenient for using the mean of the Chi-square test (MAC) method. Finally, a classification is done to classify and detect the network intrusion detection based on LDSV for Cyber security. The proposed LDSV simulation is based on the Precision F-Measure, Recall, and Accuracy for the best result.

Today, blockchain technology is playing a vital role in the professional world as one of the most important discoveries and creative developments. Blockchain technology is continuously advancing and revolutionizing our society. An overview of blockchain technology is presented in this paper. The blockchain is a distributed ledger technology that allows different transactions and operations to be recorded in a chain of blocks without requiring a third party. A growing number of businesses and industrial communities are exploring blockchain technology as a technological option. In the last decade, blockchain technology has completely changed the paradigm of computer applications. Blockchain was created with the vision of creating an immutable, confidential, open-source, decentralized P2P network located on a decentralized network to facilitate data sharing. An overview of blockchain applications in different areas is presented. We provide a timely summary of blockchain research for individuals and organizations interested in this field. This paper presents an overview of blockchain technology, listing all the key characteristics, benefits, and features which make blockchain technology both superior and unique. We also discuss popular consensus protocols and taxonomy for blockchain systems.

The term \"Internet of Things\" (IoT) refers to a lengthy period in which physical objects that are not typically associated with PCs but are connected to the Internet in some fashion. Yet, computing must advance beyond conventional scenarios using portable devices and smartphones in order to realize the IoT goal. It must also network common physical objects and integrate intelligence with the environment. As a result, new computing features and problems will appear. The networking of smart devices ranging from a small coffee maker to a massive car that interact with one another without requiring human involvement is referred to as the \"Internet of Things,\" also known as \"Device communications.\". The term \"Internet of Things\" (IoT) alludes to a time in the future when regular physical objects that are not often used in a desktop environment will be somehow connected to the Internet. Yet, in order to realize the IoT vision, computing must advance beyond conventional use cases involving portable devices and smartphones to include networking of everyday physical objects as well as the fusion of intelligence with the environment. This will cause new computer features and issues to appear. This article\'s main objective is to analyses the internet of things survey and any potential traits, challenges, or privacy concerns that can arise when the IoT becomes a reality as more and more physical products are connected to the internet

In the present world, information and data are the greatest assets one can possess. If one cannot secure their information from cyber-attacks, they would lose everything in the blink of an eye. Penetration testing can help reduce this cyber-risk exposure of clients' data and protect them. Penetration testing (also called "pen testing") is a part of ethical hacking that exposes the weak areas, vulnerabilities, or loopholes in the core of a PC, its networks, and its applications with the purpose of securing the system. The main idea of pen testing is to find vulnerabilities in systems and fix them before attackers can take advantage of them. These vulnerabilities are identified, exploited, and analyzed in five phases: information gathering, scanning, gaining access, maintaining access, and covering tracks. Penetration testing is done regularly in order to maintain high-security standards. As it pertains to any organization’s secrecy and privacy, this testing is also constrained by a number of legal agreements. The work provided by many researchers in the field of penetration (PEN) testing is reviewed and analyzed in this paper. This report gives a detailed description of the process and tools used to conduct penetration testing

Following the COVID epidemic, the healthcare sector faced numerous issues as telehealth became more prevalent and the necessity for a safe and efficient healthcare record system became critical. Many issues plague the healthcare industry today, including security, trust, data availability, and drug traceability. Blockchain technology is a relatively new technology that has demonstrated its effectiveness in a variety of industries, including finance, banking, bitcoin, and healthcare. This research investigates the impact of blockchain on the trustworthiness of healthcare. The authors adopt a descriptive-analytical methodology. The trustworthiness of the healthcare sector is considered a dependent variable. However, blockchain technology is considered an independent variable. Three dimensions of dependent variables were explored: integrity, confidence, and reliability. For studying this impact, a questionnaire of 25 items was created and distributed to the stakeholders in the healthcare sector in the north of Lebanon. The collected answers were analyzed using the SPSS application and statistical tools. The results verify that the use of blockchain technology has a great impact on healthcare trustworthiness with its three dimensions (integrity, confidence, and reliability).

This paper presents a comprehensive review of security measures in database systems, focusing on authentication, access control, encryption, auditing, intrusion detection, and privacy-enhancing techniques. It aims to provide valuable insights into the latest advancements and best practices in securing databases. The review examines the challenges, vulnerabilities, and mitigation strategies associated with database security. It explores various authentication methods, access control models, encryption techniques, auditing and monitoring approaches, intrusion detection systems, and data leakage prevention mechanisms. The paper also discusses the impact of emerging trends such as cloud computing, big data, and the Internet of Things on database security. By synthesizing existing research, this review aims to contribute to the advancement of database security and assist organizations in protecting their sensitive data.

The goal of this research is to review the researcher's different attempts with respect to new and emerging technology in malware detection techniques based on machine learning approaches over smartphones. The aim is to evaluate and benchmark these techniques, identify the current landscape of research in this area, and construct a cohesive taxonomy. The available options and gaps will be analyzed to provide valuable insights for researchers regarding the technological environments within this research area. A deep analysis review was conducted to identify studies addressing smartphone security based on machine learning approaches in order to identify all related articles. The outcomes of the last classification scheme of these articles were categorized into types of detection: dynamic analysis, static analysis, hybrid analysis, and uniform resource locator (URL) analysis. The evaluation criteria used in malware detection techniques, with respect to machine learning approaches for smartphones, include accuracy, precision rates (including true positive, false positive, true negative, false negative), training time, f-measure, detection time, area under the curve, true positive, true negative, false positive, false negative, and error rate. Additionally, our classification covers the main machine learning techniques used in the reviewed studies. The taxonomy includes three distinct layers, each reflecting one aspect of the analysis. We also reviewed the details of various types of malicious and benign datasets used within malware detection. Furthermore, open issues and challenges were identified in terms of evaluation and benchmarking, which jeopardize the utilization of this technology. We have described a new recommendation pathway solution that aims to enhance the measurement process of smartphone security applications.

The intersection of Cybersecurity and AI has garnered increasing attention in recent years due to the growing importance of securing digital assets in an interconnected world. This bibliometric analysis aims to provide valuable insights into the research trends and developments within this interdisciplinary domain. Using data extracted from the Scopus database, a total of 501 papers were selected and analyzed to uncover key patterns and themes. The methodology involved conducting a comprehensive literature search using specific keywords related to Cybersecurity and AI applications. The initial search yielded 736 papers, which were subsequently filtered to include research articles, conference papers, editorial papers, and review papers, resulting in the final dataset of 501 papers. The analysis of publication trends revealed a remarkable surge in research output since 2015, indicating the escalating interest in this field. Collaboration patterns among researchers and institutions were analyzed through co-authorship networks, highlighting a well-connected research community that fosters knowledge exchange. Keyword analysis exposed common areas of application, such as network security, deep learning, and the Internet of Things, underscoring the importance of AI technologies in enhancing Cybersecurity measures. Furthermore, examination of the most cited documents showcased influential contributions that have shaped the trajectory of Cybersecurity and AI research. The study emphasizes the significance of Cybersecurity and AI applications research, considering the ever-increasing reliance on technology in various aspects of modern life. By integrating AI technologies, Cybersecurity measures can be fortified with automated threat detection, adaptive defense mechanisms, and proactive risk mitigation, thereby bolstering overall cybersecurity resilience. The findings of this bibliometric analysis have several implications for researchers and policymakers. Researchers can leverage the identified trends and gaps to explore new research directions and potential collaborations. Policymakers can utilize these insights to make informed decisions regarding resource allocation for research initiatives aimed at addressing emerging Cybersecurity challenges. This bibliometric analysis provides a comprehensive overview of the evolving landscape of Cybersecurity and AI applications research. It underscores the growing importance of this interdisciplinary field and its potential to reshape the future of cybersecurity. As technology continues to advance, the integration of AI in Cybersecurity will play a pivotal role in safeguarding digital assets and ensuring the secure functioning of critical systems in an increasingly interconnected world.

In this research, we delve into an exhaustive ex- amination of the usage of machine learning (ML) models for the identification of cyber threats within Cloud-Based Intrusion Detection Systems (IDS). With the escalating dependence on cloud services across various industries, the urgency to develop effective and resilient IDS to mitigate burgeoning cyber risks is paramount. Our study makes a significant contribution to this imperative field by analyzing the efficacy of diverse ML models in detecting cyber-attacks within a cloud context. We scrutinized an array of ML models, namely Decision Trees (DT), Random Forest (RF), XGBoost, and Support Vector Machines (SVM), utilizing key performance parameters such as accuracy, recall, precision, F1-s, and confusion matrix to understand their practical application in real-world IDS scenarios. XGBoost stood out as the most proficient model, showcasing not only an impressive accuracy but also a balanced performance in terms of precision and recall. This highlights the considerable potential of ensemble and gradient boosting techniques in optimizing cloud- based IDS detection capabilities. Our findings underscore the significant role of machine learning in fostering more dependable, robust, and efficient IDS in the cloud, thus significantly aiding in securing our digital ecosystems.