Tariq Khraishi

Machine-learning intrusion detection on multi-attack Internet-of-Things (IoT) datasets is often reported through aggregate metrics that can hide near-failure on rare attack classes, and through single hand-picked feature subsets whose stability is seldom examined. Using the real TON_IoT Network dataset (211,043 flows, 10 classes, with man-in-the-middle (MITM) traffic at 0.5% prevalence), this study contributes a transparent empirical mapping rather than a new detector. A strong full-feature Random Forest reaches 0.969 accuracy and 0.945 macro-averaged F1 (macro-F1) yet recalls only 0.776 of the rare MITM class, and a mutual-information filter raises aggregate macro-F1 to 0.948 while leaving worst-class recall unchanged at 0.776. A self-coded non-dominated sorting genetic algorithm (NSGA-II) is then used to map the trade-off between macro-F1 and worst-class recall as the selected subset varies, alongside a matched-size random-subset control, standard imbalance remedies, and two additional classifiers. Three findings are robust. First, imbalance remedies on the full feature set—class weighting and, especially, the synthetic minority over-sampling technique (SMOTE)—raise MITM recall to 0.818 and 0.904, matching or exceeding the wrapper, so feature selection is not the most effective tool for the rare class. Second, re-adding a single destination-port feature inflates ransomware recall by 0.094 and cross-site-scripting (XSS) recall by 0.066 while doing nothing for MITM, showing that several apparently detected classes are separable by an identifier artifact whereas the genuinely hard class is not. Third, selected subsets are unstable (Nogueira index 0.21) despite reproducible objective values. The wrapper’s own rare-class advantage is small and does not reach significance under a cross-validation-appropriate corrected test. The two transferable findings replicate on the NSL-KDD benchmark. We argue that IoT intrusion-detection studies should report the aggregate–worst-class frontier, an identifier ablation, a selection-stability index, and cross-validation-appropriate significance tests.

Studies on how college/university students approach online education and courses have been conducted. The purposes of such studies, including the current paper, is to assess the students’ use of online resources to identify patterns and any room for improvement of their online experience. The author has used the Canvas Learning Management System (LMS) to deliver asynchronous instruction in an undergraduate course and to facilitate communication and course organization in a face-to-face graduate course. This paper analyzes student access and usage data from both courses to identify meaningful patterns and insights. The collected data are examined in relation to course enrollment and major course milestones. Information obtained directly from Canvas is supplemented with basic statistical analyses, including descriptive measures such as averages. The findings are then discussed to provide insights into the advantages and limitations of online teaching and learning. Findings include reduced student engagement with online content over time. Graduate students are more likely to stay engaged online versus undergraduate students. Lastly, online group work shows the free-rider effect where some students rely on other group members to finish assigned tasks.

Material defects resulting from manufacturing and processing can significantly affect material properties. Voids and dislocations are material defects considered in this study, in which a numerical solution of the 3D stress field of a dislocation line (infinite or finite) outside a cylindrical void (either parallel to the cylinder axis or not) is developed using the collocation point method. The collocation point method is utilized to solve ordinary differential equations, partial differential equations, differential-algebraic equations, and integral equations by enforcing the solution at a set of spatial collocation points. Analytical solutions for such three-dimensional (3D) problems, e.g., a dislocation line or segment near an internal void of any shape, were not found. Therefore, a numerical solution for this problem has been constructed in this paper. The numerical solution developed is verified using an existing two-dimensional analytical solution. The numerical results and the 2D analytical solution are in perfect agreement as long as the cylindrical void is sufficiently long and the Saint-Venant’s principle is followed.

The method of indentation of an indenter into a material or substrate is widely utilized to characterize one of the mechanical properties of a material, namely hardness. This method is performed on different scales of indenting force, indenter size/dimensions, and size/dimensions of the indented material/substrate. The different scales do not always yield the same hardness output or reading due to material length scales. One of the length scales in a material is related to its heterogeneity, such as in reinforcing particles or second phases. This work performs 3D non-linear finite element analysis using a purely elastic indenter on a composite material. In addition to the scales above that could affect the results, the parameters of particle size, shape, spacing, and volume fraction are all shown to affect the hardness readout in the current work. One of our new findings is that the length scale effect is more phenomenal, especially at scales when the size of the indenter is comparable to any characteristic length in the material beneath the indented surface.

This study investigates fatigue life and fracture in welded stainless steel 304L (SS 304L) resulting from load-controlled fatigue. This was done for specimens from two welding manufacturers. Mechanical testing was carried out using an MTS test frame in uniaxial tension. Specimens fabricated in three weld configurations (butt, lap, and butt-with-backer) were cycled at stress ratio R = 0.1, frequency f = 5 Hz, and experienced varying mean tensile stress. A comparison of the fatigue life of welded and unwelded (or pristine) specimens determined that the fatigue life of welded specimens is considerably shorter. The microstructure in the parent material (PM), heat-affected (HAZ), and fusion zones (FZ) was analyzed using SEM, and results suggest that, for a limited number of specimens (backer welds), fracture in the (FZ) was primarily the result of Zener–Stroh cracks. Most welded specimens fractured at the weld-HAZ interface (weld toe), and we postulate that stress concentrations primarily due to abrupt geometrical change at the weld interface led to crack initiation there. Additional stress concentrations at welding-induced defects (resulting from high temperature) such as second-phase particles, fatigue-induced defects, and preexisting impurities are responsible for crack initiation, crack growth, and eventual fracture. Comparison of unwelded and welded SN curve data using numerical curve-fitting methods reveals that the fatigue life of pristine SS 304L follows the expected sigmoid shape found in literature (specifically the Chandran fatigue model), while that of welded SS 304L deviates considerably in low cycle fatigue (LCF) and follows the Basquin power law equation.