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The metaverse, which amalgamates physical and virtual realms for diverse social activities, has been the focus of extensive application development by organizations, research institutes, and companies. However, these applications are often isolated, employing distinct authentication methods across platforms. Achieving interoperable authentication is crucial for when avatars traverse different metaverses to mitigate security concerns like impersonation, mutual authentication, replay, and server spoofing. To address these issues, we propose a blockchain-enabled secure and interoperable authentication scheme. This mechanism uniquely identifies users in the physical world as well as avatars, facilitating seamless navigation across verses. Our proposal is substantiated through informal security analyses, employing automated verification of internet security protocols and applications (AVISPA), the real-or-random (ROR) model, and Burrows–Abadi–Needham (BAN) logic and showcasing effectiveness against a broad spectrum of security threats. Comparative assessments against similar schemes demonstrate our solution’s superiority in terms of communication costs, computation costs, and security features. Consequently, our blockchain-enabled, interoperable, and secure authentication scheme stands as a robust solution for ensuring security in metaverse environments.

Phishing attacks cause companies and individuals huge economic as well as intangible damages. Phishing attacks employ a litany of attack vectors. To deal with such attacks, counter work needs to be done in several areas. In this paper, the authors have presented a survey of literature on phishing detection and the current trends in phishing. The authors have also mentioned that phishing detection can be classified into three main categories namely, disallowing attacks to reach the users, user training, and more useful user interfaces. The goal of this paper is to cover all important aspects involved in phishing detection as compared to existing surveys on phishing detection that have focused on individual aspects. There has been a continuous increase in phishing attacks, with a sharp rise in Spear phishing and attacks over Social Media.

Today's large amount of variety multimedia information like audio, video and images are massively produced through mic, digital camera, mobile phones and photo editing software etc. In this article, the authors have proposed a solution for a large database of images which provides secure, efficient and effective search and retrieve the similar images of Query image from the database with the help of a novel technique of Local Tetra Pattern for Content Based Image Retrieval, which carries the interrelationship in between the center pixels and its surrounded neighbours of center pixel by computing difference of gray level. The images which are stored on the database are private or contain private information of particular user and these digital images should not be accessed by others except the user. The user wish for a privacy and security for their images while storing and retrieving the images from the database.

A reverse dictionary takes a target word's description as input and returns the words that fit the description. Reverse Dictionaries are useful for new language learners, anomia patients, and for solving common tip-of-the-tongue problems (lethologica). Currently, there does not exist any Reverse Dictionary provider with support for any Indian Language. We present a novel open-source cross-lingual reverse dictionary system with support for Indian languages. In this paper, we propose a transformer-based deep learning approach to tackle the limitations faced by the existing systems using the mT5 model. This architecture uses the Translation Language Modeling (TLM) technique, rather than the conventional BERT's Masked Language Modeling (MLM) technique.

Signature-based malware detectors have proven to be insufficient as even a small change in malignant executable code can bypass these signature-based detectors. Many machine learning-based models have been proposed to efficiently detect a wide variety of malware. Many of these models are found to be susceptible to adversarial attacks - attacks that work by generating intentionally designed inputs that can force these models to misclassify. Our work aims to explore vulnerabilities in the current state of the art malware detectors to adversarial attacks. We train a Transformers-based malware detector, carry out adversarial attacks resulting in a misclassification rate of 23.9% and propose defenses that reduce this misclassification rate to half. An implementation of our work can be found at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.

A reverse dictionary takes a target word's description as input and returns the words that fit the description. Reverse Dictionaries are useful for new language learners, anomia patients, and for solving common tip-of-the-tongue problems (lethologica). Currently, there does not exist any Reverse Dictionary provider with support for any Indian Language. We present a novel open-source cross-lingual reverse dictionary system with support for Indian languages. In this paper, we propose a transformer-based deep learning approach to tackle the limitations faced by the existing systems using the mT5 model. This architecture uses the Translation Language Modeling (TLM) technique, rather than the conventional BERT's Masked Language Modeling (MLM) technique.

Capsule endoscopy is a highly advanced, newer technology to look for small bowel diseases. But it has certain contraindications such as bowel narrowing, strictures that have to be ruled out on Barium studies or with computed tomography. We present a rare case of retention of endoscopic capsule even after ruling out stricture or bowel thickening on radiological imaging.

[...]this technique was useful in identifying the 'unsatisfactory' pockets in the PHC, when the overall coverage was satisfactory (86.01% ).This will help the local managers to initiate appropriate corrective action since the overall coverage value may mask these low coverage areas. [...]coverage levels are therefore a key process indicator of performance [18l Operational units with poorer coverage should be identified to improve the performance.