The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

The current work is concerned with preparing cobalt manganese ferrite (Co1-xMnxFe2O4) with different concentrations of cobalt and manganese (x=0.2, 0.4, and 0.6) and decorating it with polyaniline (PAni) for use in supercapacitive applications. The results of the X-ray diffraction (XRD) manifested a broad peak of PAni and a cubic structure of cobalt manganese ferrite having crystal size between 60 nm and 138 nm, which decreases with increasing concentration of Mn. The field emission scanning electron microscopy (FESEM) images evidenced that the PAni has nanofiber (NF) structures, according to the method of preparation, where the hydrothermal method was used. The magnetic properties of the prepared ferrite, as well as the prepared PAni/Co1-x

Photonic crystal fiber interferometers (PCFIs) are widely used for sensing applications. This work presented solid core-PCFs based on Mach-Zehnder modal interferometer for sensing refractive index. The general structure of sensor was applied by splicing short lengths of PCF in both sides with conventional single mode fiber (SMF-28).To apply modal interferometer theory collapsing technique based on fusion splicing used to excite higher order modes (LP01 and LP11). A high sensitive optical spectrum analyzer (OSA) was used to monitor and record the transmitted wavelength. This work studied a Mach-Zahnder interferometer refractive index sensor based on splicing point tapered SMF-PCF-SMF. Relation between refractive index sensitivity and tape

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing

Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l