Federated Learning (FL) is a promising form of distributed machine learning that preserves privacy by training models locally without sharing raw data. While FL ensures data privacy through collaborative learning, it faces several critical challenges. These include vulnerabilities to reverse engineering, risks to model architecture privacy, susceptibility to model poisoning attacks, threats to data integrity, and the high costs associated with communication and connectivity. This paper presents a comprehensive review of FL, categorizing data partitioning formats into horizontal federated learning, vertical federated learning, and federated transfer learning. Furthermore, it explores the integration of FL with blockchain, leveraging blockchain’s decentralized nature to enhance FL’s security, reliability, and performance. The study reviews existing FL models, identifying key challenges such as privacy risks, communication overhead, model poisoning vulnerabilities, and ethical dilemmas. It evaluates privacy-preserving mechanisms and security strategies in FL, particularly those enabled by blockchain, such as cryptographic methods, decentralized consensus protocols, and tamper-proof data logging. Additionally, the research analyzes regulatory and ethical considerations for adopting blockchain-based FL solutions. Key findings highlight the effectiveness of blockchain in addressing FL challenges, particularly in mitigating model poisoning, ensuring data integrity, and reducing communication costs. The paper concludes with future directions for integrating blockchain and FL, emphasizing areas such as interoperability, lightweight consensus mechanisms, and regulatory compliance.

In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.

Software vulnerabilities are among the significant causes of security breaches. Vulnerabilities can severely compromise software security if exploited by malicious attacks and may result in catastrophic losses. Hence, Automatic vulnerability detection methods promise to mitigate attack risks and safeguard software security. This paper introduces a novel model for automatic vulnerability detection of source code vulnerabilities dubbed DB-CBIL using a hybrid deep learning model based on Distilled Bidirectional Encoder Representations from Transformers (DistilBERT). The proposed model considers contextualized word embeddings using the language model for the syntax and semantics of source code functions based on the Abstract Syntax Tree (AST) representation. The model includes two main phases. First, using a vulnerable code dataset, the pre-trained DistilBert transformer is fine-tuned for word embedding. Second, a hybrid deep learning model detects which code functions are vulnerable. The hybrid model is built on two Deep Neural Networks (DNN). The first model is the Convolutional Neural Network (CNN), which is used for extracting features. The second model is Bidirectional-LSTM (BiLSTM), which has been used to maintain the sequential order of the data as it can handle lengthy token sequences. The utilized source code dataset is derived from the Software Assurance Reference Database (SARD) benchmark dataset. Final experimental findings show that the proposed model outperforms the state-of-the-art approaches’ performance by improving precision, recall, F1-score, and False Negative Rate (FNR) by 2.41%-8.95%, 4.0%-16.28%, 1.85%-12.74%, and 18% respectively. The proposed model reports the lowest FNR in the literature, a significant achievement given the cost-based nature of vulnerability detectors.

Because Internet of Things (IoT) networks are widely deployed, they have become attractive targets for botnet and distributed denial of service (DDoS) attacks, which require effective intrusion detection. Convolutional neural networks (CNNs) can achieve strong detection performance, but their many hyperparameters are usually tuned manually, which is costly and time-consuming. This paper proposes a new hybrid metaheuristic optimizer, FW-CNN, that combines Grey Wolf Optimization and Red Fox Optimization to automatically tune the key hyperparameters of a one-dimensional CNN for IoT intrusion detection. The Red Fox component enhances exploration and helps the search escape local optima, while the Grey Wolf component strengthens exploitation and guides convergence toward high-quality solutions. The proposed model is evaluated using the N-BaIoT dataset and compared with a feedforward neural network as well as a metaheuristic-optimized model based on the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-CNN. It achieves a final accuracy of 95.56%, improving on the feedforward network by 12.56 percentage points and outperforming the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-based CNN model by 1.02 percentage points. It also yields higher average precision, Kappa coefficient, and Jaccard similarity, and significantly reduces Hamming loss. These results indicate that the proposed hybrid optimizer is stable and effective for multi-class IoT intrusion detection in real environments.

Phishing emails continue to pose a significant cybersecurity threat, particularly through the increasing use of malicious attachments to evade traditional text-based detection systems. Most existing approaches focus primarily on email content, creating a blind spot in attachment-aware phishing detection. This paper proposes a multi-modal phishing email classification model that integrates email header features, body text analysis, and attachment inspection within an ensemble learning framework. Independent machine learning classifiers are employed for each email component, and a majority voting mechanism is used to determine the final classification decision. The proposed model is evaluated using publicly available email and attachment datasets that are combined to simulate attachment-bearing phishing emails. Experimental results demonstrate strong detection performance across multiple evaluation metrics. Nevertheless, the study acknowledges the limitation of using synthetically paired email bodies and attachments, which may not fully capture real-world semantic relationships. The findings highlight the importance of incorporating attachment-aware analysis into phishing detection systems and provide a foundation for future research on semantic consistency modeling and transformer-based architectures.

الهدف من مشاريع التعلم الآلي الصناعي هو تطوير حلول تعلم الآلة في أسرع وقت ممكن ووضعها في مرحلة الإنتاج. ومع ذلك، فإن الكثير من تجارب التعلم الآلي لا ترقى إلى مستوى متطلباتها وتوقعاتها لأن أتمتة وتشغيل أنظمة التعلم الآلي تمثل تحديًا كبيرًا. عمليات تطوير تعلم الآلة، وهو نموذج لعمليات التعلم الآلي، يتعامل مع هذا الأمر. هناك العديد من مكونات عمليات تطوير تعلم الآلة ، بما في ذلك ثقافة التطوير ومجموعة من المفاهيم وأفضل الممارسات المرتبطة مع هذا النموذج. ومع ذلك، فإن عواقب استخدام عمليات تطوير تعلم الآلة بالنسبة لكلا من الأكاديميين والمهنيين لا تزال غير معروفة بشكل واضح وكافى للتعامل معه. ونتيجة لذلك، يقدم هذا البحث شرحًا شاملاً للأفكار والعناصر والبنية الأساسية، للمساعدة في تطوير برامج قابلة للاستخدام تعتمد على أساليب تعلم الآلة التي يمكن أن تجعل نماذج عمليات تطوير تعلم الألة سهلة المراقبة والتكامل والقياس بشكل آمن للمساعدة في توفير الصيانة. التكاليف وتحسين عدد عمليات نشر البرامج. كما تلفت الدراسة الانتباه إلى المشاكل المتبقية في المنطقة قبل الانتهاء منها.