September 11 attacks

Summary

The September 11 attacks, commonly known as 9/11, were four coordinated Islamist suicide terrorist attacks carried out by al-Qaeda against the United States on Tuesday, September 11, 2001. That morning, 19 terrorists hijacked four commercial airliners scheduled to travel from the New England and Mid-Atlantic regions of the East Coast to California. The hijackers crashed the first two planes into the Twin Towers of the World Trade Center in New York City, two of the world's five-tallest buildings at the time, and aimed the next two flights toward targets in or near Washington, D.C. in an attack on the nation's capital. The third team succeeded in crashing into the Pentagon, the headquarters of the U.S. Department of Defense in Arlington County, Virginia, while the fourth plane crashed in rural Pennsylvania following a passenger revolt. The attacks killed nearly 3,000 people and instigated the multi-decade global war on terror. The first impact was that of American Airlines Flight 11,

Official source

https://en.wikipedia.org/wiki/September_11_attacks

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Like many other enterprises, humanitarian organizations are investing in their digital transformation. In this sector, an important effort is put into biometric solutions. The ICRC has been exploring new ways of how they could integrate biometrics into their beneficiary programs, as these technologies could improve the efficacy of assistance as well as permit new ways of helping people across the globe. However, despite the potential advantages, the humanitarian imperative of “Do no harm” must be ensured before any new application or implementation of new technologies is integrated into humanitarian operations. As a means to ensure the individual's privacy, dignity and safety, the ICRC needs to evaluate if biometric authentication systems could lead to any harm to individuals. In this work, we assess the different parts of biometric authentication systems and investigate their potential risks and vulnerabilities. We further evaluate existing biometric security frameworks and extend on them by integrating the distribution of components over functional parties. With our framework we managed to construct three different models for general biometric authentication and assessed their individual functional advantages as well as their sensitivity to external and internal attacks. Though our framework provides evaluation of general systems, we hope our research will provide the ICRC a better foothold in this rapidly expanding field.

2021

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The vulnerability of deep neural networks to adversarial attacks has posed significant threats to real-world applications, especially security-critical ones. Given a well-trained model, slight modifications to the input samples can cause drastic changes in the predictions of the model. Many methods have been proposed to mitigate the issue. However, the majority of these defenses have proven to fail to resist all the adversarial attacks. This is mainly because the knowledge advantage of the attacker can help to either easily customize the information of the target model or create a surrogate model as a substitute to successfully construct the corresponding adversarial examples. In this paper, we propose a new defense mechanism that creates a knowledge gap between attackers and defenders by imposing a designed watermarking system into standard deep neural networks. The embedded watermark is data-independent and non-reproducible to an attacker, which improves randomization and security of the defense model without compromising performance on clean data, and thus yields knowledge disadvantage to prevent an attacker from crafting effective adversarial examples targeting the defensive model. We evaluate the performance of our watermarking defense using a wide range of watermarking algorithms against four state-of-the-art attacks on different datasets, and the experimental results validate its effectiveness.

IEEE2021

In recent years we have seen a marked increase in disinformation including as part of a strategy of so-called hybrid warfare. Adversaries not only directly spread misleading content but manipulate social media by employing sophisticated techniques that exploit platform vulnerabilities and avoid detection. It is getting increasingly important to analyze social media manipulation to better understand, detect and defend public dialogue against it.In this thesis, we contribute to the research on social media manipulation by describing and analyzing how adversaries employ compromised social media accounts. We begin by providing a background of social media: we describe the mechanisms and the influence of the platforms to better understand why the adversaries target them. We then give a detailed overview of social media manipulation, and the techniques to detect and counter it. Next, we discuss our contributions in this thesis: 1) an extensive analysis of an attack on social media algorithms using compromised accounts, 2) a study of the implications of compromised bots for bot research through the characterization of retweet bots, 3) a detection method to find compromised accounts that are later repurposed.Firstly, we uncover and analyze a previously unknown, ongoing astroturfing attack on the popularity mechanisms of social media platforms: ephemeral astroturfing attacks. In this attack, a chosen keyword or topic is artificially promoted by coordinated and inauthentic activity to appear popular. Crucially, this activity is removed as part of the attack which facilitates using compromised accounts that are still managed by their original owners. We detected over 19,000 unique fake trends promoted by over 108,000 accounts. Trends astroturfed by these attacks account for at least 20% of the top 10 global trends. We created a Twitter bot to detect the attacks in real-time and inform the public. Secondly, we study the implications of compromised accounts to bot research. We do this by characterizing retweet bots that have been uncovered by purchasing retweets from the black market. We determine that those accounts were compromised as they observe anomalous behavior, or self-state that they are hacked. We then analyze their differences from human-controlled accounts. From our findings on the nature and life-cycle of retweet bots, we point out several inconsistencies between the retweet bots used in this work and bots studied in prior works. Our findings challenge some of the fundamental assumptions related to bots and in particular how to detect them.Thirdly, we define, describe, and provide a detection method for misleading repurposing, in which an adversary changes the identity of a potentially compromised social media accounts via, among other things, changes to the profile attributes in order to use them for a new purpose while retaining their followers. We propose a methodology to flag repurposed accounts, and detected over 100,000 such accounts. We also characterize repurposed accounts and found that they are more likely to be repurposed after a period of inactivity and deleting old tweets. We present a tool to root out accounts that became popular and repurposed later.Our work is significant in presenting how breaches of user security jeopardize platform security and public dialogue. Furthermore, it enhances the knowledge of how the bots and troll accounts work and aid platforms and researchers in building new solutions.

EPFL2022