Asia Othman Aljahdali

This paper introduces GLANet, a novel FL-based framework designed for real-time, privacy-preserving anomaly detection in distributed network environments. GLANet is a FL-based framework that integrates a lightweight convolutional neural network (CNN) as a local anomaly detector at each distributed node with a global model aggregation mechanism based on federated averaging (FedAvg). The framework incorporates consistency regularization to align local and global model parameters, ensuring that node-specific threat patterns are preserved while enabling network-wide generalization. Differential privacy is employed through the injection of calibrated Gaussian noise into model updates before transmission to the central server, providing formal privacy guarantees without compromising detection performance. The experimental findings reveal that GLANet achieves a high detection accuracy of 97.8% on the CICIDS 2017 dataset, surpassing traditional FL baselines that achieved 92.7%. The model also achieves superior precision (96.5%), recall (97.0%), and F1 Score (96.8%), reflecting a well-balanced anomaly detection performance. Additionally, GLANet reduces communication costs by 37.5% and achieves a lower privacy loss of ε = 0.8 compared to 1.5 in baseline methods. These findings demonstrate the potential of GLANet as an effective, scalable, and secure anomaly detection framework for comprehensive defense against evolving cyber threats in dynamic and distributed network environments.

Information and communication technology (ICT) has become a major global driver, but it also exposes organizations to frequent cyber threats, making asset protection increasingly difficult. Cyber threat intelligence (CTI) is essential for improving cybersecurity, especially when integrated into a security operations center (SOC) for real-time threat monitoring and analysis. This study proposes a real-time CTI framework within a SOC environment, hosted on Linode, which integrates the Malware Information Sharing Platform (MISP) and a Security Information and Event Management (SIEM) system to collect indicators of compromise (IoCs). The framework uses machine learning to detect fraud in mobile money transactions such as cash-in, cash-out, debit, payment, and transfer. Fraudulent activity often involves the use of stolen identity information for unauthorized transactions. The system generates detailed alert reports and provides predictive insights into potential threats, helping organizations strengthen user trust and protect their reputation. Experimental results using financial datasets show high performance: logistic regression achieved 98.83% accuracy, while the random forest model reached a test accuracy of 95.86% and cross-validation accuracy of 95.76%. The F1-score was 0.9586, and the ROC-AUC score was 0.9923, indicating strong classification capability.

Raccoon Stealer malware is difficult to detect as it actively evades traditional methods. This study proposes Recealer, a hybrid detection model that integrates machine learning (ML) and deep learning (DL). The model applies static and dynamic analysis to extract features from sample files, which are first classified using an ML algorithm. Files with uncertain classification results are then transformed into grayscale images and analyzed by a convolutional neural network for improved precision. Experimental results demonstrate that Recealer achieves 94% overall detection accuracy, with the random forest algorithm attaining 97.53% in the ML stage and the DL stage reaching 95%. These findings indicate that Recealer is both efficient and reliable for detecting Raccoon Stealer malware.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.