Mesopotamian Journal of CyberSecurity

Network security has become increasingly critical in recent years. Among the various aspects of network security and considering several approaches to network security, intrusion detection systems (IDSs) have gained considerable attention. The prominence of this factor, among other factors of network security, is due to its ability to address the complex and uncertain nature of security breaches. Whenever data flow over the network, precise categorization of normal and malicious data is necessary. Past IDS systems lack precise categorization. Thus, the present study focuses on the use of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier to categorize network instances into malicious types and normal behavior. Using the KDD99 dataset, the performance of ANFIS is evaluated and compared with that of traditional machine learning models such as decision trees and multilayer perceptrons. Through experimentation with different membership functions, such as Gaussian, triangular, bell-shaped, and sigmoidal functions, Gaussian functions are identified as optimal for this specific task. The results underscore the effectiveness of ANFIS, leveraging the strengths of both artificial neural networks (ANNs) and fuzzy reasoning systems. ANFIS demonstrates superior capabilities in understanding nonlinear interaction patterns, adapting to evolving threats, and facilitating rapid learning in intrusion detection applications.

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT applications that require real-time data transmission and low latency, such as smart cities, autonomous vehicles, and industrial automation. Additionally, MIMO technology helps in mitigating interference and improving spectrum utilization, making it an essential enabler for the massive connectivity demands of IoT networks in the 5G era. However, due to the high channel dimension, complex channel estimation and precoding algorithms in the system, the system hardware and software overhead will increase. The precoding algorithms of massive MIMO systems are divided into three types: digital, analog and hybrid. The three types of precoding algorithms are summarized and compared, and the advantages and disadvantages of different precoding algorithms and applicable scenarios are summarized. The channel estimation schemes are divided into training estimation and blind estimation, and the advantages and disadvantages of the two types of schemes are summarized. It is pointed out that the reasonable use of the channel sparsity of massive MIMO can improve the quality of channel estimation and reduce the estimation overhead.

Cognitive radio networks provide an important function in the efficient use of the radio spectrum. Therefore, dedicated cognitive radio ad hoc networks (CRAHNs) are expected to improve communication performance in a multihop network that requires a dedicated routing protocol that considers the dynamic mobility of secondary user nodes on the basis of the random availability of primary node channels. To enhance routing in the CRAHNs environment, in this paper, a novel Hybrid Cooperative Spectrum Structured (HCSS) approach that combines cooperative spectrum sensing and spectrum-aware routing protocols can be effective in appropriate decision making for routing packets in such a network. This approach integrates the strengths of Spectrum-Aware Semi-Structured Routing (SSR) and Spectrum-Aware Routing Protocol (SARP) to enable adaptive and efficient routing in dynamic CRAHN environments. The results showed that the proposed HCSS routing protocol increased the user demand while ensuring the quality of service (QoS) requirements by achieving an average throughput of 5164.55 Kbps, exceeding the SARP and SSR, which recorded 4585.56 Kbps and 4194.66 Kbps, respectively. The PDR for HCSS was 98.77%, which was significantly higher than that for SARP (95.60%), and the SSR was 90.15%, indicating better reliable connectivity in delivering packets to their final destinations.

Healthcare monitoring Cloud-IoT systems use data mining and machine learning methods to analyse patient data in real-time from linked devices. By offering insights for the early diagnosis of anomalies and individualized treatment suggestions, this strategy improves healthcare management. In this research first the Collect and Load the Clevant Heart Disease Dataset for Data Collection Process. Next, preprocess the loaded data using the Synthetic Minority Oversampling Technique (SMOTE), and then the feature extraction process is done using the Principal Component Analysis (PCA) Method. In this case, the characteristic must be extracted by feeding a specific column. The classification procedure is then carried out using Generative Adversarial Networks (GAN) and an optimization approach called Adaptive Moment Estimation. This is where the model executes GAN operations, and the output will be produced. The data is then transferred to an edge-cloud environment to minimize storage problems and provide instant access to critical data. This process starts with the encryption and decryption of data using Homomorphic encryption with the Laplacian technique. In addition, have taken the generated values from the GAN network as original values and encrypt them using Homomorphic encryption with the Laplacian technique. Next, the routing process is done using the leach protocol to optimize energy consumption and communication efficiency. The leach protocol is used to route among the data to divide the data into clusters and perform energy consumption. Finally, the simulation of this research is conducted by Python – 3.9.6 network simulator, and the performance of the proposed model is estimated based on various performance metrics such as accuracy at 90%, precision at 94%, authentication time, throughput at 90%, and packet delivery ratio with 94% this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.

Android devices are rapidly being used, which makes it easy for the malware threat to rise to higher levels. This ever-growing problem has prompted the need to enhance detection systems as far as these devices are concerned. Standard techniques of machine learning (ML) are sufficient from the point of view of their speed for searching patterns and behaviors of contemporary malware; however, it is more important to have effectively enhanced methods. The purpose of this paper is to expand the utilization of Android malware identification via artificial neural networks (ANNs) and compare its efficiency with that of other ML methods. An ANN is used in this study, and the results are compared against those of several other types of ML, including logistic regression (LR), k-nearest neighbors (KNN), extremely randomized trees (extra trees), gradient boosting (GBM), adaptive boosting (AdaBoost), and categorical boosting (CatBoost). The six evaluation values include training accuracy, testing accuracy, average accuracy, precision, recall and the F1 score. The ANN model performed well, with training and testing accuracies of 0. 99 and an average accuracy of 0. 99, precision of 0. 99, recall of 0. 98, whereas the F1 score, which is an average of both precision and recall, was 0. 99. Related studies based on conventional ML methods are also highly efficient, with some accuracy and an F1 score of 0. 95 and 0. 96. On the other hand, the ANN model ranked the best in the assessed measures. Thus, this study focuses on the reliability of ANNs for improving mobile security systems against next-generation malware and their applicability to secured Android smartphones. The reason behind the high accuracy of the ANN model is the enhanced learning ability of the ANN, which helps it learn the characteristics and dynamics of malware better than traditional ML models do.

Blockchain technology is a type of distributed ledger that provides secure and efficient storage, management, and transmission of data over a decentralized network. With its ability to ensure transparency and immutability, blockchain is increasingly adopted across various sectors ranging from finance, healthcare, and logistics to education. In healthcare, blockchain technology is attracting attention because of its potential to fundamentally transform health ecosystems. The healthcare sector has significantly benefited from blockchain technology by enhancing data security and interoperability and reducing medical errors. In this context, a set of studies highlighted the importance of blockchain in the field of healthcare, enhancing trust and security in the exchange of data and preventing unauthorized access. The article also studies the meaning, structure, function, types, and areas of use of blockchain technology and discusses the distribution of medical products in supply chain management. This article concludes that blockchain technology is highly important for storing health records, enhancing patient privacy, protecting patient data, and allowing the secure sharing of these data with physicians and healthcare workers.

This study explores the integration of eye-tracking technology, specifically the gaze point (GP3) eye tracker, to develop a novel encryption and decryption model that leverages an individual's unique eye patterns as a cryptographic key. This research addresses the critical problem of balancing user-friendly encryption mechanisms with robust security. This study aims to design a gaze-based encryption system that uses the eye's binary matrix as a dynamic encryption key. The methodology involves capturing an eye image, converting it into a binary matrix, and using the extracted matrix to generate a unique iris key (IK) applied to encrypt and decrypt text messages on the basis of gaze interaction. This study evaluates the proposed approach against metrics such as encryption and decryption time, key entropy, and resilience to cryptanalysis. The results demonstrate that the system achieves high levels of security, with the iris key offering sufficient randomness and robustness against brute-force attacks. The gaze-based mechanism reveals hidden text when the user interacts with specific characters, enhancing privacy. The proposed model integrates biometric authentication with real-time encryption, setting a foundation for future applications in secure communication systems. The proposed model provides safe, user-centric encryption and addresses key management challenges while harnessing the potential of gaze-based interaction.

Malware detection is a vital problem, and efficient methods that can efficiently detect malware are needed. The increasing use of mobile computers makes malware detection a vital part of security in an era where smartphones have come to play a key role in many of our daily lives. Earlier approaches, however, suffer from high false positive rates; they are not scalable for larger databases, or they are not amenable to adapt well to novel zero-day malware. For these reasons, the demand for more sensitive and flexible detection models is high. In this study, we develop a hybrid mobile malware detection framework that leverages ant colony optimization (ACO) and deep neural networks (DNNs) to improve detection accuracy, reduce the rate of false positives, and make the model resilient to new malware. AntDroidNet is a novel ACO-enabled feature selection model that dynamically reduces the feature dimensionality by selecting single instances to include the most informative properties and avoid dimensionality. A DNN is consequently constructed to train the determined set of features, improving the identified classification performance and decreasing the number of instances with false discoveries. In this way, a self-optimizing feedback loop can iteratively improve the feature selection process given the performance of the DNN, leading to a dynamic and efficient detection model. Using the CICMalDroid2020 dataset, the proposed AntDroidNet model achieves a remarkable accuracy of 99.89% and an excellent false positive rate of only 0.13% and outperforms the classical machine learning algorithms in terms of accuracy and efficiency. AntDroidNet is a scalable and powerful mobile malware detection model that eclipses all state-of-the-art methods and shows important enhancements in efficiency and reliability. By prototyping whitelisting systems, this work opens new avenues in mobile security and lays the groundwork for future work on building real-time detection components and system components able to scale to the fast pace of evolution of mobile malware in new connected ecosystems.

Blockchain technology consists of distributed ledgers or database systems, regarded as immutable, secure, and innovative, characterized by unsupervised internal maintenance with a special security protocol used to prevent inference from malicious or third parties. The widespread use of this technology has led to deep research into the problems posed by this technology, which can be summarized in terms of computational cost and latency time. The crime detection process in video surveillance has made great progress with the use of technologies such as the Internet of Things and blockchain technologies. However, to reach high levels of security in the physical crime detection process in which data are sent to servers via a computer network, there must be a high degree of security for Internet of Things systems related to the crime detection process. There has been a significant increase in the number of problems associated with crime detection in video surveillance systems, including the modification of surveillance data during transfer to and from servers. For this reason, establishing a reliable and secure system for transferring video surveillance data to servers has become a high priority. This paper presents a lightweight security system to protect data generated in the crime detection process, both from video surveillance cameras and the servers that store these data. The challenges related to IoT-based video surveillance cameras and monitoring and control centers have been considered, turning the system primarily into a decentralized system. In this paper, a lightweight blockchain system based on a proof of secret share consensus algorithm technology is proposed, along with the encryption of surveillance data via modified Okamoto–Uchiyama homomorphic encryption technology. The proposed system is evaluated via standard blockchain and security evaluation metrics, demonstrating efficient utilization of computational costs and realization of security, with a high scalability rate. The VGG16 deep learning model is employed in the proposed system to detect and classify criminal activities in surveillance videos. Owing to its ability to identify patterns and anomalies, the model achieved an accuracy of 94%, demonstrating a high level of performance in crime detection and prevention. Overall, the use of VGG16 provides an efficient and reliable approach for improving the security of public spaces and reducing the risk of criminal activity.

The number of cybersecurity incidents is increasing, and the cost of a security breach has a catastrophic financial effect. The existing real estate information systems require more robust and resilient cybersecurity solutions to secure hardware, software, and databases. Cybersecurity often relies on user behavior. Phishing attacks and social engineering can bypass blockchain security, highlighting the need for robust user education and awareness. Many blockchain networks face challenges in handling a high volume of transactions. Solutions that work well on a small scale may not perform efficiently as the network grows, leading to delays and increased costs. A secure system is a significant area in cybersecurity that protects digital transactions from unauthorized access. This paper proposes a new real estate transaction encryption scheme using the modified SLIM cryptography algorithm in the context of blockchain based on a 2Dimension Chialvo map with high nonlinearity generation of the substitution box (S-Box) to make it robust against many linear attacks, which is a novel approach to enhancing transaction speed and security. Modifying lightweight SLIM cryptography is highly resistant to linear and differential cryptanalysis attacks, providing adequate protection. The modified lightweight SLIM cryptography applies to wireless networks, IoT applications, and real estate systems. The performance analysis of the blockchain shows that it completes transactions in a short time of approximately 29.368877 in milliseconds and satisfies the Bit Independence Criterion (BIC) and Hamming Distance (HD) tests above 50%, the new hash will be regarded as good and trustworthy. The performance analysis of the proposed S-Box shows that it has high nonlinearity and satisfies the Strict Avalanche Criteria (SAC), BIC, balanced, and low linearity/differential probability. In addition, the strength and randomness of the random number generators are analyzed via a set of tests and guidelines provided by the National Institute of Standards and Technology (NIST) tests (approximately 97%) to ensure that the generated random data are sufficiently unpredictable and suitable for cryptographic applications.

Smartphone authentication systems must balance security and user convenience, which is a persistent challenge in the digital realm. Traditional biometrics, such as fingerprints and facial recognition, face vulnerabilities to spoofing and environmental conditions, limiting reliability. This study introduces a novel approach by integrating three-dimensional (3D) touch sensors with finger location data for authentication. The goal is to develop a system that improves accuracy while minimizing false positives and negatives, leveraging touch pressure and spatial interaction as unique biometric identifiers. Data from 20 participants, including pressure levels, spatial coordinates, and timestamps, were analysed using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that combining pressure sensitivity with spatial data significantly improved performance, achieving an F1- score of 0.83 and an accuracy of 83%. The system demonstrated balanced precision (0.84) and recall (0.83), effectively reducing false positives and negatives. Robustness was confirmed through cross-validation tests, which validated the consistency across datasets and real-time usability scenarios. This study establishes a foundation for secure, user-friendly smartphone authentication, highlighting the potential of 3D touch technology in addressing current biometric system limitations. This approach opens avenues for further research in mobile security, integrating multimodal biometric data with advanced machine learning techniques.

In today's communications, it is vital to protect audio data privacy before and after transfer. This work therefore introduces the enhanced audio encryption scheme (EAES), which employs the most effective techniques of cryptography and processes them to make it impossible for unauthorized persons or programs to access or change the contents of audio files. This method uses RSA to encrypt the Blowfish key, which is used for data encryption, the HMAC-SHA256 algorithm for integrity checks, and the Message-Digest algorithm (MD5) for the checking phase. The performance of the EAES is measured statistically via the MSE, PSNR, and correlation coefficient. For the encrypted signal, the MSE was approximately 2×1072, whereas for the decrypted signal, the MSE was zero, which means that the original signal was the same as the processed signal. The PSNR for the decrypted signal was inf. The correlation coefficient test for the decrypted signal was 1, and that for the encrypted signal was 0.0006. The experimental results show that this technique can securely encrypt and decode an audio stream while maintaining its quality and being resistant to popular attacks. This method provides a dependable method of securing extremely sensitive audio data in a variety of applications that demand high security.

This literature review looks at the transformative impact of Internet of Things (IoT) technology on smart building management systems with a focus on enhancing operational efficiency, occupant safety, and comfort. As the number of IoT applications in the built environment increases, technology breakthroughs simplify and automate critical functions, including energy management, security, and environmental control. Despite all the advantages, such as the integration of the IoT with smart buildings, there are barriers, such as the compatibility of systems, scalability of infrastructure and data security and privacy issues in a connected ecosystem. This review examines some of those barriers and outlines potential solutions, such as standardized communication protocols, improved cybersecurity, and scalable, cost-effective IoT frameworks for existing and new buildings. The aim of the findings is to guide building owners, stakeholders, architects and urban planners in maximizing the benefits of IoT technology to create smarter, more sustainable and responsive buildings that support urban sustainability and foster better quality of life for tenants.

In today's digital era, cybersecurity has become a principal concern because of the increasing frequency and advancement of cyber threats. This study explores machine learning models for detecting and predicting anomalies in cybersecurity datasets. The research evaluates models such as linear regression, decision tree, RF, gradient boosting, KNN, SVR, LSTM, and neural networks utilizing performance metrics such as accuracy, MAE and MSE. A hybrid model that integrates different learning strategies is additionally proposed to improve the predictive accuracy and strength. The results highlight the superiority of ensemble approaches, especially the hybrid model, in improving peculiarity detection capabilities. The comparative analysis demonstrates that traditional models struggle with nonlinear patterns, whereas hybrid approaches successfully relieve this limitation. Moreover, this study emphasizes the importance of temporal data analysis for proactive threat detection and response. By leveraging diverse machine learning methods, this research contributes to strengthening cybersecurity infrastructures, enabling early threat detection, and minimizing security breaches. These discoveries emphasize the importance of adopting a comprehensive machine learning system to support cybersecurity resilience.

Underwater wireless sensor network (UWSN) requirements have increased beyond applications in environmental monitoring and underwater exploration to military surveillance. The complex underwater environment raises many challenges due to high propagation delays, limited bandwidth, high error rates, and dynamic underwater currents. Most traditional clustering algorithms do not consider the multifaceted requirements of UWSNs. In most cases, a single objective is optimised at the cost of other essential factors, such as energy consumption, network robustness, and data transmission reliability. This paper proposes a new UWSN protocol based on the tiger beetle optimisation (TBO) algorithm for multiobjective K-means clustering (TBO-MOK). The protocol comprises adaptive search procedures motivated by tiger beetle hunting behaviors and lightweight AES-based encryption for data security. TBO-MOK is excellent in multiobjective optimisation since it simultaneously considers performance metrics of more than one aspect. Many problems are resolved by TBO-MOK, which optimises all the involved performance metrics to provide balanced energy usage and robust communication links. Comprehensive simulations demonstrate that TBO-MOK outperforms the traditional LEACH, PSO, and GA approaches in grossly enhancing network lifetime, energy efficiency, load balancing, and data transmission reliability. These results show the potential of TBO-MOK to provide a more effective and resilient solution for UWSNs.

The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tree (DT) and mutual information (MI). For classification, adaptive boosting (AdaBoost), XGBoost and categorical boosting (CatBoosting) are used to categorize incoming data as normal or spoofing. The experimental results indicate the efficiency of the suggested approach for correctly identifying spoofing attacks with high accuracy, fewer false positives, and reduced time needed. By utilizing feature importance and robust classification algorithms, the system can accurately differentiate between legitimate and malicious IoT traffic, thereby improving the overall security of IoT networks. The CatBoost classifier outperformed the AdaBoost and XGBoost classifiers in terms of accuracy.

Securing big data in power plants is an important and fundamental step in the infrastructure of smart cities. In addition, it becomes a barrier if it is not controlled from the beginning. Security must be a combination of fast and robust properties. This research presents a traffic security system (TSS) in a smart city (SC). It is a novel paradigm meant to improve data security and integrity by means of a multilayered method. Advanced fault analysis, dual-stage pseudonymizing, quantum key distribution via the BB84 protocol, and Falcon signatures (FS) are combined in the proposed system. TSS enhances data security by enhancing privacy, supporting resilience against quantum computing threats, and not burdening the network with complex and large keys. The proposed system has been tested against several recently known attacks, such as replay, supply chain, Sybil, blackhole, eavesdropping, advanced persistent threat (APT), tampering, ransomware identity fraud, and desynchronization, and it has been proven to overcome them. In terms of performance evaluation, the average signing time was approximately 0.002 milliseconds. In comparison, the average signature verification time was approximately 0.004 milliseconds, with an average execution time of approximately 0.54 milliseconds and a precision of approximately 93.6 %, a recall of approximately 97.8 %, and an F1_score of approximately 95.6 %, which are considered low compared with those of state-of-the-art research. Thus, the proposed TSS system is highly acceptable for power plant applications. This work lays the groundwork for future developments in safe, privacy-conscious urban systems by presenting a multilayered approach to secure energy data in smart cities.

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the threat landscape, with malwares arising as a critical concern. Advanced learning methods such as machine learning (ML), deep learning (DL), and federated learning (FL) are essential for handling complex IoT data. ML provides tools for pattern identification and detecting anomalies. DL boosts malware detection by automatically extracting features and identifying patterns. FL enables collaborative model training across decentralized devices, ensuring data privacy, which is crucial for diverse IoT systems. This comprehensive review specifically synthesizes ML, DL and FL for malware detection in the IoT environment, highlighting key trends and developments. Additionally, several significant contributions have been provided, including an overview of various types of malwares and their approaches and a comparison with existing studies. Importantly, notable trends and advancements are highlighted, and the current limitations of these learning techniques in malware detection are identified. It concludes by outlining future research directions to develop robust, scalable malware detection mechanisms tailored to safeguard the prosperity of the IoT environment against evolving cyber threats.

In the current digital landscape, information security has become a critical necessity given the escalating frequency and sophistication of cyberattacks across global computing networks. Cryptography is the science and practice of securing information by transforming it into a format that is unreadable or inaccessible to unauthorized parties. The strength of the cryptography algorithm lies in the strength of used encryption key. Recently, the nonlinear behavior of chaotic maps has been utilized as a random source to generate robust key stream bits for cryptographic purposes. The aim of this paper is to introduce an efficient and robust system for generating strong key stream bits against different types of attacks. The proposed system implemented six proposed scenarios for generating a highly strong key stream bits based on different 5-types of chaotic maps (Tent, Ikeda, Chua's, Rössler and Double Pendulum). In the first five scenarios, use each map with Deep-Reinforcement Learning (DRL) algorithm called Advantage Actor-Critic (A2C), while in the sixth scenario, the fusion of all above five chaotic maps with Advantage Actor-Critic (A2C) are used at the same time. For each scenario of the proposed system generates three different lengths of key stream bits (128-bit, 192-bit, and 256-bit). To evaluate the robustness, randomness, and effectiveness of the proposed system, several types of tests were applied like NIST, brute-force attack, Auto Correlation (AC), Cross Correlation (CC), and Discrete Fourier Transform (DFT). All the obtained results indicated to the highly robustness and highly strength of generated key stream bits for all six proposed scenarios of the proposed system.