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The evolution of social media and online platforms created more opportunities to express one's thoughts without any burden. This emergence paved the way to construct more intelligent systems but by taking advantage of these platforms some people display their vengeance by harassing or abusing comments. The attackers leak sensitive information about an individual or an organization through the internet using abusive words, hate comments, threatening information, fake news, etc. So, online shaming detection with an optimum classification mechanism becomes necessary to avoid such negative consequences. In this paper, a novel online shaming detection mechanism using a Convolutional Neural Network Bidirectional Gated Recurrent Unit-based Hybrid Archimedes Tasmanian Devil (CNNBiGRU-HATD) approach is proposed to detect and classify the offensive comments and non-offensive comments. If the comment seems offensive, it is further classified into six categories according to shaming types such as abusive, doxing, Passing Judgment, Ethnic/Religious, Joke/Sarcasm, and Whataboutery. The utilized four Twitter-based datasets are initially preprocessed and feature extracted to improve the classification performance of the CNNBiGRU-HATD approach. After accurate classification, the information from the classified shaming types is collected and authenticated. The proposed approach is simulated using the Python platform. To validate the potential capability of the proposed CNNBiGRU-HATD approach, it is related to the existing methods by various parameters. The experimental result reveals that the proposed CNNBiGRU-HATD method achieves higher accuracy of 96.5% for the labeled hate speech detection dataset. [ABSTRACT FROM AUTHOR]

Phishing is an attack that deceit online users by means of masquerading as a genuine website to pilfer their classified or personal information. This is one among the recognized cybercrime. Disparate phishing website detection systems were recently developed for the purpose of detecting the phishing websites. However, they fail to attain the desired output and are suffered from countless drawbacks like lower accuracy and higher training time. For trouncing such drawbacks, this paper proposes an effectual Hybrid Deep Learning (HDL)-centric Phishing Detection System (PDS) using the MCS-DNN classifier. At first, pre-processing is done on the input dataset for ameliorating its quality. Subsequently, clustering and feature selection (FS) are performed to lessen the processing time and elevate the accuracy using CoK-means and CM-WOA, respectively. The features which are chosen during FS are fed into the MCS-DNN classifier, which classifies the legitimate websites and phishing websites. Lastly, the K-fold cross-validations (KCV) are employed for effectively predicting the proposed system's accurateness. The outcomes highlight the robustness and predictive ability of the proposed PDS to distinguish the phishing as well as legitimate sites. [ABSTRACT FROM AUTHOR]

Learning vector quantization (LVQ) constitutes a very popular machine learning technology with applications, for example, in biomedical data analysis, predictive maintenance/quality as well as product individualization. Albeit probabilistic LVQ variants exist, its deterministic counterparts are often preferred due to their better efficiency. The latter do not allow an immediate probabilistic interpretation of its output; hence, a rejection of classification based on confidence values is not possible. In this contribution, we investigate different schemes how to extend and integrate pairwise LVQ schemes to an overall probabilistic output, in comparison with a recent heuristic surrogate measure for the security of the classification, which is directly based on LVQ's multi-class classification scheme. Furthermore, we propose a canonic way how to fuse these values over a given time window in case a possibly disrupted measurement is taken over a longer time interval to counter the uncertainty of a single point in time. Experimental results indicate that an explicit probabilistic treatment often yields superior results as compared to a standard deterministic LVQ method, but metric learning is able to annul this difference. Fusion over a short time period is beneficial in case of an unclear classification. [ABSTRACT FROM AUTHOR]