Anonymity networks are becoming increasingly popular in today's online world as more users attempt to safeguard their online privacy. Tor is currently the most popular anonymity network in use and provides anonymity to both users and services (hidden services). However, the anonymity provided by Tor is also being misused in various ways. Hosting illegal sites for selling drugs, hosting command and control servers for botnets, and distributing censored content are but a few such examples. As a result, various parties, including governments and law enforcement agencies, are interested in attacks that assist in de-anonymising the Tor network, disrupting its operations, and bypassing its censorship circumvention mechanisms. In this survey paper, we review known Tor attacks and identify current techniques for the de-anonymisation of Tor users and hidden services. We discuss these techniques and analyse the practicality of their execution method. We conclude by discussing improvements to the Tor framework that help prevent the surveyed de-anonymisation attacks.

Tiny Machine Learning (TinyML) is an upsurging research field that proposes to democratize the use of Machine Learning and Deep Learning on highly energy-efficient frugal Microcontroller Units. Considering the general assumption that TinyML can only run inference, growing interest in the domain has led to work that makes them reformable, i.e., solutions that permit models to improve once deployed. This work presents a survey on reformable TinyML solutions with the proposal of a novel taxonomy. Here, the suitability of each hierarchical layer for reformability is discussed. Furthermore, we explore the workflow of TinyML and analyze the identified deployment schemes, available tools and the scarcely available benchmarking tools. Finally, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges, and future directions, and it’s fusion with next-generation AI.

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The COVID-19 pandemic era will be remembered as a uniquely disruptive period that altered the lives of billions of citizens globally, resulting in new-normal for the way people live and work. With the coronavirus pandemic, businesses, governments, and educational institutes adapted to the "work or study from home" operating model that has not only transformed our online lives but has also exponentially increased the use of cyberspace. Concurrently, there has been a huge spike in the usage of social media platforms such as Facebook and Twitter during the COVID-19 lockdown periods.These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise users of social media platforms in times of fear, uncertainty, and doubt. The threats range from running phishing campaigns and malicious domains to extracting private information about victims for malicious purposes. To this end, it is vital to analyse the impact of drastic transformations that were taken during lockdown periods on the security and privacy of users.This research paper performs a large-scale study to investigate the impact of lockdown periods during COVID-19 pandemic on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users' security and privacy behaviour in three different periods (i.e., before, during, and after lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing building name, nearby coronavirus testing location) in their tweets. The privacy risk reaches to 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared in social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries' strategies to instigate malicious activities change with the country's pandemic situation.

There has been a huge spike in the usage of social media platforms during the COVID-19 lockdowns. These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise social media users with a range of threats. This paper performs a largescale study to investigate the impact of a pandemic and the lockdown periods on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users' security and privacy behaviour in three different periods (i.e., before, during, and after the lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing nearby coronavirus testing locations) in their tweets. The privacy risk reaches 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared on social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries' strategies to instigate malicious activities change with the country's pandemic situation.

The rapid miniaturization of Machine Learning (ML) for low powered processing has opened gateways to provide cognition at the extreme edge (E.g., sensors and actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research field proposes to democratize the use of Machine Learning (ML) and Deep Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly energy-efficient pervasive devices capable of operating with less than a few Milliwatts of power. Nevertheless, many solutions assume that TinyML can only run inference. Despite this, growing interest in TinyML has led to work that makes them reformable, i.e., work that permits TinyML to improve once deployed. In line with this, roadblocks in MCU based solutions in general, such as reduced physical access and long deployment periods of MCUs, deem reformable TinyML to play a significant part in more effective solutions. In this work, we present a survey on reformable TinyML solutions with the proposal of a novel taxonomy for ease of separation. Here, we also discuss the suitability of each hierarchical layer in the taxonomy for allowing reformability. In addition to these, we explore the workflow of TinyML and analyze the identified deployment schemes and the scarcely available benchmarking tools. Furthermore, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges and future directions.