Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

Lai, Chun-Ming; Shiu, Hung-Jr; Chapman, Jon W.

doi:10.3390/electronics9122020

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…The biggest disadvantage of the blacklist method is that it cannot detect newly created spam URLs. Studies have been carried out in recent years to cope with this problem by using artificial intelligence and machine learning methods [9,10]. In the machine learning approach, models are first trained using training data.…”

Section: Introductionmentioning

confidence: 99%

Using and Comparing Machine Learning Techniques for Automatic Detection of Spam Website URLs

Yıldırım

2022

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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With the developing technology, the issue of cyber security has become one of the most common and current issues in recent years. Spam URLs are one of the most common and dangerous issues for cybersecurity. Spam URLs are one of the most widely used attacks to defraud users. These attacks cause users to suffer monetary losses, steal private information, and install malicious software on their devices. It is very important to detect such threats promptly and to take precautions against these threats. Detection of malicious URLs is mostly done by using blacklists. However, these lists are insufficient to detect newly created URLs. In recent years, machine learning techniques have been developed to overcome this deficiency. In this study, URL classification was made using different machine learning techniques. In the study, 9 different classifiers were preferred for URL classification. The performances of the classifiers were compared in the URL classification process. In addition, similar studies in the literature have been comprehensively examined and these studies have been discussed. In addition, since the preparation of data sets in the natural language processing process has a great effect on the training of models, these steps are discussed in detail.

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Section: Introductionmentioning

confidence: 99%

Using and Comparing Machine Learning Techniques for Automatic Detection of Spam Website URLs

Yıldırım

2022

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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“…One of the main reasons that fake news is spreading rapidly worldwide is that we rely heavily on the information we acquire from social media or any other news platform. There has been much evidence that the news that receives the most significant reaction is often proven wrong later [10,11]. One of these pieces of evidence would be the spreading of the coronavirus, where incorrect and fake information was spread around the world [12].…”

Section: Introductionmentioning

confidence: 99%

Identifying Fake News Using Real Time Analytics

Borkar¹

2022

IJRASET

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Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TFIDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

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High Performance and High Yield Solution Processed IGZO Thin Film Transistors Fabricated with Low‐Temperature Annealed Hafnium Dioxide Gate Dielectric

Liu,

Yu,

et al. 2023

Adv Elect Materials

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Solution‐processed microelectronics offer advantages, including cost‐effectiveness, higher energy efficiency, and compatibility with rapid prototyping compared to their counterparts fabricated through traditional semiconductor manufacturing processes. Unfortunately, solution‐processed transistors exhibit wide performance variability and low yield. In this work, a solution‐processed transparent indium gallium zinc oxide (IGZO) thin film transistor with a low temperature‐annealed hafnium oxide dielectric layer is described. Post‐annealing temperatures for the sol–gel hafnium dioxide thin film are reduced to below 200 °C, significantly expanding the range of substrates on which the metal oxide dielectric can be deposited. The fabricated devices exhibit excellent characteristics with high field‐effect mobilities of over 85 cm2 V−1 s−1, along with low subthreshold swing below 140 mV dec−1, high on/off ratios, and near‐zero threshold voltages when operating stably at low‐operating voltages of 2 V. The solution processed transparent hafnium dioxide gate dielectric IGZO transistors are shown to exhibit comparatively significantly lower device variation and high yield, allowing for the reproducible fabrication of large‐area and transparent solution processed microelectronics systems.

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Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

Cited by 6 publications

References 26 publications

Using and Comparing Machine Learning Techniques for Automatic Detection of Spam Website URLs

Using and Comparing Machine Learning Techniques for Automatic Detection of Spam Website URLs

Identifying Fake News Using Real Time Analytics

High Performance and High Yield Solution Processed IGZO Thin Film Transistors Fabricated with Low‐Temperature Annealed Hafnium Dioxide Gate Dielectric

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