Purpose: The authors attempt to examine the work done in the area of Intrusion Detection System in IoT utilizing Machine Learning/Deep Learning technique and various accessible datasets for IoT security in this review of literature. Methodology: The papers in this study were published between 2014 and 2021 and dealt with the use of IDS in IoT security. Various databases such as IEEE, Wiley, Science Direct, MDPI, and others were searched for this purpose, and shortlisted articles used Machine Learning and Deep Learning techniques to handle various IoT vulnerabilities. Findings/Result: In the past few years, the IDS has grown in popularity as a result of their robustness. The main idea behind intrusion detection systems is to detect intruders in a given region. An intruder is a host that tries to connect to other nodes without permission in the world of the Internet of Things. In the field of IDS, there is a research gap. Different ML/DL techniques are used for IDS in IoT. But it does not properly deal with complexity issues. Also, these techniques are limited to some attacks, and it does not provide high accuracy. Originality: A review had been executed from various research works available from online databases and based on the survey derived a structure for the future study. Paper Type: Literature Review.

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Background/Purpose: Telegram is an internationally available, free, instant messaging service. Over the years, Telegram has established itself as a trailblazer and innovative choice in the domain of instant messaging services. Telegram supports a wide range of devices and is available for iOS, Android, macOS, Windows as well as Linux. Telegram’s cross-platform support is one of the reasons why it has achieved over a billion downloads and 500 million monthly active users. This paper investigates the features of the Telegram app, services offered, its corporate background and financial information related to the Telegram as an organization. The authors have also presented a SWOT analysis on Telegram, along with their observations and recommendations for improving Telegram’s services. Objective: To examine the features and services of Telegram in depth and look at the aspect that contributed to its success. Design/Methodology/Approach: Undertaking a case study by collecting data from secondary sources and presenting a comprehensive SWOT analysis on the subject of study. Findings/Result: Telegram's ultimate aim, according to research conducted through numerous resources and analysis of facts and figures, is not to earn money, and it provides a plethora of security features that have made it popular in recent years. Originality/Value: A study based on existing literature and online resources to create a comprehensive overview of the subject of study. Paper Type: Case study analysis.

Purpose: Facebook has far more than 2.91 billion users worldwide, as of October 2021 by following its commitment to “Give people the power to build community and bring the world closer together”. Machine learning as well as applied Machine learning helps people to get new contents and related stories. Machine learning (ML) is a technique for recognizing and drawing conclusions from data connections. Speech recognition systems can caption videos on Facebook using machine learning, making them more accessible. The Facebook research team is working with Machine learning technology to give its users the best services. Here an analysis has been done on the history of Facebook, major acquisition of Facebook, applications of AI in Facebook and Financial plans and challenges. The entire Facebook company will now be known as Meta. The Facebook app, on the other hand, will keep its name, and other apps will not be affected. Facebook is now known as 'Meta' because of its new focus which means 'after' or 'beyond' in Greek. However, Instagram, Facebook, WhatsApp, and Messenger are now all part of the 'Meta' company, much like Google's products are all part of the Alphabet company. Artificial intelligence, AI is really essential to Facebook. Approach: For this analysis, a sophisticated survey strategy based on secondary data was applied. Findings: Based on the findings, it is clear that to provide high-quality service, Facebook uses leading edge artificial intelligence/machine learning technologies. Originality: To identify the methodologies employed in the organization's services, a study is conducted based on the existing resources. Paper Type: Descriptive Case Study Research

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