Spam messages pose a significant cybersecurity threat, leading to phishing attacks, fraud, and privacy breaches. Traditional spam detection methods, such as rule-based filtering and statistical models, often fail to capture the evolving and complex nature of spam messages. In this paper, we propose an Arabic spam detection model leveraging BERT (Bidirectional Encoder Representations from Transformers), a deep learning-based NLP model. Our approach enhances classification accuracy by utilizing contextual text representations specific to the Arabic language. We preprocess Arabic text using AraBERT tokenization and fine-tune the BERT-based model on a balanced dataset of Arabic spam and ham messages. Experimental results demonstrate that our model achieves high accuracy (98%), outperforming traditional machine learning and deep learning approaches. This research highlights the potential of transformer-based models in Arabic spam filtering, paving the way for more efficient and robust detection systems.

Electrical systems keep things running in modern life, but they often run into problems like imbalances, short circuits, ground faults, and overloading, which can cause equipment to break down, fires to break out, and even large-scale blackouts. To make matters worse, acts of sabotage, physical damage, or cyberattacks on systems like SCADA can mess up operations, throw grids off balance, and set off cascading failures. To avoid these risks, there is a growing need for smarter tools that can keep track of system performance and flag potential issues before they get out of hand. In this paper, we suggest a deep learning model built on the inception architecture, designed to monitor electrical systems, call out potential security faults, and spot malicious actions. Taking advantage of deep learning, our approach helps increase fault prediction accuracy and keep operations on track.

Emotion recognition from audio signals is essential in forensic applications, offering insight into emotional states during interrogations, threat assessments, and crime scene analysis. This paper proposes an Inception-based deep learning model tailored for forensic arabic audio emotion recognition. The Inception architecture, with its multiscale feature extraction capabilities, efficiently captures subtle emotional details from complex audio signals. The model was evaluated on a dataset that represents a diverse range of emotional expressions, achieving superior performance in accuracy, robustness, and adaptability compared to traditional approaches. Its precision and ability to handle real-world variability make it particularly suited for forensic investigations. This work underscores the potential of advanced neural architectures in enhancing forensic decision-making and analysis.

In the current landscape of IT, ransomware attacks pose a major threat to cybersecurity resulting in significant monetary losses and data breaches. The detection of ransomware in time presents a challenge due to its constant evolution and complex strategies for escaping detection. This study introduces a deep learning tool —named RansFighter—based on Gated Recurrent Unit (GRU) specifically developed for ransomware detection. Our model shows, at test time, an Accuracy of 96.67%, a Precision of 97.01%, a Recall of 96.37%, an F1-Score of 96.69% and an Area Under the Curve (AUC) of 96.67%. It showcases the potential of GRUs as valuable assets to safeguard systems against ransomware threats.

CAPTCHAs, used to be an important element of security, are now facing more challenges due to the great advancements in artificial intelligence. In this paper, we investigate whether CAPTCHAs are still effective in protecting websites from automated threats. A deep learning model is suggested to automatically recognize CAPTCHA embedded characters, with performance metrics achieving an accuracy of 99.46%, an AUC of 99.98%, a precision of 99.46% and a recall of 99.43%. These findings highlight the increasing susceptibility of CAPTCHAs to sophisticated AI driven attacks and seek to emphasize the pressing importance of reevaluating CAPTCHA technologies to guarantee sustainable security.

This paper exploits the rise of artificial intelligence (AI) and deep learning (DL) to improve the use of digital forensic evidence analysis, specifically criminal identification from facial images despite disguise and makeup. Our approach leverages the VGG16 architecture for face recognition and identification, coupled with the LIME framework (Local Interpretable Model-Agnostic Explanations) to explain model recognition. This combination enables interpretation and verification of results with enhanced trust and confidence in forensic analysis. We follow a "watch and iterate" procedure, utilizing the insights generated from LIME to curate the training dataset, improving the model’s performance iteratively. The efficacy of this procedure is reflected in the remarkable outcomes: our model has an accuracy of 98.10%, precision of 98.16%, recall of 98.10%, F1-score of 98.11%, AUC of 100%. This development in forensic technology has great potential to enhance the precision and speed of criminal identification, thus leading to safer and fairer societies.

This paper presents a novel methodology to enhance Autonomous Cybersecurity Knowledge Graphs (ACKGs) by incorporating distributed invariants, ensuring robust consistency in data integrity, access control, and threat detection. The proposed framework integrates Graph Neural Networks (GNNs) and Large Language Models (LLMs), facilitating real-time validation, automated threat mitigation, and continuous system updates as evolving threats are detected. By embedding these invariants into the cybersecurity architecture, the approach offers a scalable, dynamic, and self-sustaining solution, significantly improving the resilience, adaptability, and operational efficiency of cybersecurity systems in complex, large-scale environments.

Workplace stress, a widespread issue in modern professional environments, significantly increases the potential for errors and accidents. Timely and precise stress identification is vital for fostering a secure and efficient work environment. This research introduces an innovative, comparative-analysis framework designed for real-time stress detection, utilizing Heart Rate Variability (HRV) as a reliable physiological indicator. Unlike standard heart rate measurements, HRV offers a granular view of the autonomic nervous system (ANS) function, enabling accurate stress evaluation. We implement a comprehensive methodology incorporating a refined preprocessing stage—including the removal of outliers, feature selection, and data normalization—along with a comparative assessment of eight deep recurrent neural network (RNN) architectures. These include vanilla RNN, bidirectional RNN (BiRNN), Gated Recurrent Unit (GRU), bidirectional GRU (BiGRU), standard Long Short-Term Memory network (LSTM), bidirectional LSTM (BiLSTM), Peephole LSTM, and Attention-based LSTM, applied to binary stress classification. Utilizing a dataset of 410,322 HRV records from the SWELL Knowledge Work (SWELL-KW) Dataset, our framework demonstrates exceptional performance, with the BiGRU architecture achieving a test accuracy of 99.51%. This study highlights the effectiveness of advanced temporal modeling and comparative analysis in creating robust stress detection systems for various occupational contexts, thereby enhancing workplace safety.

Intentionally mutilated fingerprints pose a significant challenge in forensic identification. Such deliberate actions typically stem from individuals seeking to evade detection or association with past or prospective criminal activities. The detection of damaged fingerprints presents a formidable obstacle for most of current forensic systems, often leading to a pronounced incidence of false negatives. The ramifications of a false negative are profound, as they preclude the establishment of links between suspects and crime scenes, impeding the acquisition of vital evidence and potentially stalling investigative progress. In response to this critical issue, this paper delves into the development of a deep learning based model expressly designed to accurately discern and capture patterns present in damaged fingerprints.

In forensic investigations, facial recognition techniques serve as critical tools for identifying and apprehending suspects. In this study, we investigate the impact of Convolutional Neural Networks (CNNs) parallelization on the performance of facial recognition models within forensic contexts. Through experiments, we demonstrate the potential benefits of parallelization in enhancing model accuracy and robustness. Leveraging a reduced dataset, we employ augmentation techniques to expand the diversity of training samples. Our findings highlight the advantages of CNN parallelization in achieving superior recognition outcomes. Nevertheless, we identify constraints linked to excessive parallelization, which may induce model overfitting.

The pervasive issue of online sexism continues to pose significant challenges, fostering environments characterized by toxicity and perpetuating harmful societal norms. In response, this paper presents an approach for the discovery of sexist statements employing convolutional neural networks (CNNs), Word Embeddings, and data augmentation techniques. Through the fusion of CNNs’ capacity for hierarchical feature extraction with the semantic representations afforded by Word Embeddings, our method achieves exemplary discrimination performance. Additionally, the incorporation of data augmentation enriches the training dataset, thereby augmenting model generalization and resilience. Empirical evaluation on a larger dataset of statements demonstrates the efficacy of our approach, surpassing many baseline approaches in terms of discovery accuracy, precision, recall and F1-score.

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

In the realm of forensic science, precise identification of individuals holds paramount importance in both investigative procedures and legal proceedings. Hands and palms recognition has emerged as a valuable biometric modality within forensic applications, owing to the distinct and intricate features inherent to these anatomical regions. The elaborate patterns of veins, creases, and ridges present on palms and fingers serve as rich sources of biometric data, crucial for accurate identification purposes. Furthermore, given the frequent involvement of hands and palms in criminal activities such as theft and assault, their recognition becomes imperative for establishing links between suspects and crime scenes. However, developing robust recognition systems tailored for forensic applications poses notable challenges, including variations in hand poses, lighting conditions, and image quality. To address these hurdles, sophisticated deep learning techniques, notably transfer learning, have been employed. By harnessing pre-trained deep learning models namely NasNetLarge, NasNetMobile, and EfficientNet, initially trained on expansive datasets for general image recognition tasks, we can adapt these models to the specific task of hands and palms recognition in forensic contexts. Our findings reveal that all three models consistently achieved over 92% accuracy across all metrics evaluated, demonstrating their efficacy as strong contenders for the hands-and-palms recognition task. Notably, the EfficientNet model exhibited superior performance compared to its counterparts, boasting more than 95.8% accuracy, precision, F1-score and recall, along with more than 98.6% specificity and 99.4% AUC.

Cyberbullying, marked by its persistent and intentional aggression online, yields severe repercussions for its victims, extending beyond immediate distress to long-lasting effects such as heightened anxiety, depression, and social withdrawal. Individuals subjected to Cyberbullying often grapple with diminished self-esteem, compromised academic performance, and strained interpersonal relations. Given the escalating prevalence of this digital menace, there is a pressing need for advanced methodologies to address it effectively. This paper introduces an approach to Cyberbullying detection, integrating techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses, along with the parallel processing capabilities of Convolutional Neural Networks (CNNs) and the contextual comprehension provided by Bidirectional Long Short-Term Memory (BiLSTM) networks. Through an experimentation on the latest Ejaz-Choudhury-Razi Cyberbullying dataset, our framework exhibits satisfactory performance in identifying instances of online hostility. These results underscore the potential of our approach to significantly contribute to ongoing efforts aimed at combating Cyberbullying in digital environments.

The Tamazight civilization stands as a significant cultural entity, marked by its linguistic diversity, historical legacy, and scriptural traditions, which collectively enrich the cultural tapestry of North Africa. Among these traditions, the Tamazight handwritten script assumes particular importance, embodying centuries of cultural identity and artistic expression. Recognizing the imperative of safeguarding this cultural heritage, our study focuses on Tamazight handwritten character recognition. Leveraging the strategic application of Transfer Learning, we explore its efficacy in this domain. Transfer Learning presents a robust framework wherein pre-existing models are adapted for specific tasks despite limited data availability. Our research employs three prominent Transfer Learning architectures: VGG, ResNet, and MobileNet. Through a rigorous comparative analysis, we discern the efficacy of these methodologies in the context of Tamazight handwritten character recognition. Our findings underscore the potential of Transfer Learning to significantly augment the accuracy and efficiency of script recognition systems, thereby advancing the overarching objective of preserving and propagating the Tamazight cultural heritage.

Spam consists of unwanted messages that are often containers of malicious code and/or links pointing to shady sites or objects that pose real dangers to a company’s machines, software, or data. Spam detection is therefore a primary security objective. Nevertheless, the detection tools available on the market are few in number and their efficiency is often limited. In this paper, we propose a spam detection tool based on deep-learning. Our tool uses bidirectional Long-Short Term Memory networks while relying on Stanford Global Vectors for word representation. We present the techniques we use. Then, we conduct a series of experiments on a family of candidate detectors. Finally, we present the performance of the selected detector.

In this paper, we study an electromagnetic optimization technique using Taguchi's method and apply it to circular antenna array (CAA) design. Taguchi's method was developed on the basis of the orthogonal array (OA) concept, which offers systematic and efficient characteristics. The newly proposed idea is the implementation of Taguchi optimization method for CAA of order 10, 16 and 24 respectively. The optimization procedure is then used to provide an optimum set of weights for different CAAs. Obtained results show that the desired radiation pattern with optimum sidelobe level (SLL) reduction is successfully achieved. Compared to traditional optimization techniques and well-known algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Firefly Algorithm (FA)), Taguchi's method is easy to implement and efficient to reach the optimum solutions.

In this paper, we implement hybrid Woodward-Lawson Neural Networks and weighted Fourier method to synthesize antenna arrays. The neural networks (NN) is applied here to simplify the modeling of MIMO antenna arrays by assessing phases. The main problem is obviously to find optimal weights of the linear antenna array elements giving radiation pattern with minimum sidelobe level (SLL) and hence ameliorating the antenna array performance. To attain this purpose, an antenna array for reliable Multiple-Input Multiple-Output (MIMO) applications with frequency at 2.45 GHz is implemented. To validate the suggested method, many examples of uniformly excited array patterns with the main beam are put in the direction of the useful signal. The Woodward-Lawson Neural Networks synthesis method permits to find out interesting analytical equations for the synthesis of an antenna array and highlights the flexibility between the system parameters in input and those in output. The performance of this hybrid optimization underlines how well the system is suitable for a wireless communication and how it participates in reducing interference, as well.

In this paper, we are proposing a new formal framework for reasoning on authentication in security protocols based on analytic functions. We give sufficient conditions that, if satisfied, the protocol is declared correct with respect to authentication. We validate our approach on the Yahalom-Lowe protocol. First, we show that it satisfies these few conditions, thus, we conclude that it is correct for authentication.

The concepts of array processing and smart antenna give a promising solution to the significant increase of data rates in wireless transmission systems. In this paper, we deal with the problem of designing linear antenna arrays for specific radiation properties of MIMO applications based on Direction-Of-Arrival estimation and Taylor beamforming techniques. The objectives of this paper can be summarized as to minimize the maximum sidelobe level (SLL), combined the Taylor method and MUSIC (Multiple Signal Classification) algorithm. The performance of this hybrid optimization determines how well the system is convenient for a reliable wireless communication and interference reduction. This paper will discuss the application of MUSIC algorithm for linear array antenna (4, 8 and 16 antennas) in order to estimate the Direction-Of-Arrival of various angles of elevation and azimuth.