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This paper presents a comprehensive analysis of V2X communication, focusing on the transition from IEEE 802.11p to IEEE 802.11bd standards, and introduces an analytical framework combining M/G/1 queuing analysis and Markov chain analysis. Our model includes saturation and non-saturation states of Enhanced Distributed Channel Access (EDCA) in the scenarios with and without fallback mechanism for IEEE 802.11bd Next Generation Vehicles (NGV) and focuses on optimizing Transmission Opportunity (TXOP) allocation for legacy (non-NGV) devices whilst taking into account the impact of vehicle-to-roadside Unit (RSU) distance. Our results show increased throughput for higher-priority traffic classes with augmented TXOP allocation for NGV devices and highlight the need to align RSU transmission coverage with On-Board Unit (OBU) data patterns and vehicle distance to prevent network saturation.

Recommending points of interest (POI) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it is important to analyze users’ historical behavior and preferences. In this study, we present a sophisticated location-aware recommendation system that uses Bidirectional Encoder Representations from Transformers (BERT) to offer personalized location-based suggestions. Our model combines location information and user preferences to provide more relevant recommendations compared to models that predict the next POI in a sequence. Based on our experiments conducted on two benchmark datasets, we have observed that our BERT-based model surpasses baselines models in terms of HR by a significant margin of 6% compared to the second-best performing baseline. Furthermore, our model demonstrates a percentage gain of 1–2% in the NDCG compared to second best baseline. These results indicate the superior performance and effectiveness of our BERT-based approach in comparison to other models when evaluating HR and NDCG metrics. Moreover, we see the effectiveness of the proposed model for quality through additional experiments.

Over the last few years, the malware propagation on PC platforms, especially on Windows OS has been even severe. For the purpose of resisting a large scale of malware variants, machine learning (ML) classifiers for malicious Portable Executable (PE) files have been proposed to achieve automated classification. Recently, function call graph (FCG) vectorization (FCGV) representation was explored as the input feature to achieve higher ML classification accuracy, but FCGV representation loses some critical features of PE files due to the hash technique. This paper aims to further improve the classification accuracy of FCGV-based ML model by applying both graph and non-graph features. We propose an FCGV-SF based Random Forest classification model, which applies both FCGV features (graph features) and statistical features (SF, non-graph features) extracted from disassembled PE files. Six types of effective non-graph features are chosen for our integrated vector, namely, metadata, symbol, operation code, register, section and data definition. We evaluate our model on a dataset provided by Microsoft hosted at Kaggle, and the experimental results indicate that the classification accuracy increases from 0.9851 to 0.9957 compared with the existing model based on FCGV only.

Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared with many traditional Machine Learning (ML) models in diverse fields. However, adversarial examples challenge the further deployment and application of DNNs. Analysis has been carried out for studying the reasons of DNNs' vulnerability to adversarial perturbation and focused on model architecture. No research has been done on investigating the impact of optimization algorithms (namely, optimizers in DNNs) employed in training DNN models on models' sensitivity to adversarial examples. This paper aims to study this impact from an experimental perspective. We analyze the sensitivity of a model not only from the aspect of white-box and black-box attack setups, but also from the aspect of different types of datasets. Four common optimizers, SGD, RMSprop, Adadelta, and Adam, are investigated on structured and unstructured datasets. Extensive experiment results indicate that an optimization algorithm does pose effects on the DNN model sensitivity to adversarial examples. That is, when training models and generating adversarial examples, Adam optimizer can generate better quality adversarial examples for structured datasets, and Adadelta optimizer can generate better quality adversarial examples for unstructured datasets. In addition, the choice of optimizers does not affect the transferability of adversarial examples.