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In recent years, there has been a growing interest in and focus on the automatic detection of deceptive behavior. This attention is justified by the wide range of applications that deception detection can have, especially in fields such as criminology. This study specifically aims to contribute to the field of deception detection by capturing transcribed data, analyzing textual data using Natural Language Processing (NLP) techniques, and comparing the performance of conventional models using linguistic features with the performance of Large Language Models (LLMs). In addition, the significance of applied linguistic features has been examined using different feature selection techniques. Through extensive experiments, we evaluated the effectiveness of both conventional and deep NLP models in detecting deception from speech. Applying different models to the Real-Life Trial dataset, a single layer of Bidirectional Long Short-Term Memory (BiLSTM) tuned by early stopping outperformed the other models. This model achieved an accuracy of 93.57% and an F1 score of 94.48%. 

Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and login credentials. Hence, detecting malicious websites before they cause any harm is critical to preventing fraud and monetary loss. To address the increasing number of phishing attacks, protective mechanisms must be highly responsive, adaptive, and scalable. Fortunately, advances in the field of machine learning, coupled with access to vast amounts of data, have led to the adoption of various deep learning models for timely detection of these cyber crimes. This study focuses on the detection of phishing websites using deep learning models such as Multi-Head Attention, Temporal Convolutional Network (TCN), BI-LSTM, and LSTM where URLs of the phishing websites are treated as a sequence. The results demonstrate that Multi-Head Attention and BI-LSTM model outperform some other deep learning-based algorithms such as TCN and LSTM in producing better precision, recall, and F1-scores. 

Phishing websites many a times look-alike to benign websites with the objective being to lure unsuspecting users to visit them. The visits at times may be driven through links in phishing emails, links from web pages as well as web search results. Although the precise motivations behind phishing websites may differ the common denominator lies in the fact that unsuspecting users are mostly required to take some action e.g., clicking on a desired Uniform Resource Locator (URL). To accurately identify phishing websites, the cybersecurity community has relied on a variety of approaches including blacklisting, heuristic techniques as well as content-based approaches among others. The identification techniques are every so often enhanced using an array of methods i.e., honeypots, features recognition, manual reporting, web-crawlers among others. Nevertheless, a number of phishing websites still escape detection either because they are not blacklisted, are too recent or were incorrectly evaluated. It is therefore imperative to enhance solutions that could mitigate phishing websites threats. In this study, the effectiveness of the Bidirectional Encoder Representations from Transformers (BERT) is investigated as a possible tool for detecting phishing URLs. The experimental results detail that the BERT transformer model achieves acceptable prediction results without requiring advanced URLs feature selection techniques or the involvement of a domain specialist. 

Abstract A new measurement protocol, labeled Acoustic Mapping Velocimetry (AMV), has been successfully tested for in‐situ estimation of bedload transport features in sandy beds. The AMV has proven efficient in using the dune‐tracking method (DTM) for characterizing the bedform geometry and dynamics as well as for estimation of the rates of bedload transport. Given the novelty of the AMV protocol and its extensive reliance on multiple site‐specific assumptions and user‐defined parameters, a comparison of this emerging technique with other three non‐intrusive DTM‐based methods and analytical predictors is attempted in this paper. The comparison highlights that the AMV estimates are within 22% of the estimates with the other non‐intrusive protocols and up to 98% different from analytical predictions. The observed differences are related to the possible sources of uncertainty in the AMV workflows and to the means to reduce their impact on the targeted estimations.