763 research outputs found

    The Threat of Offensive AI to Organizations

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    AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 32 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a panel survey spanning industry, government and academia, we rank the AI threats and provide insights on the adversaries

    The Future of Cybercrime: AI and Emerging Technologies Are Creating a Cybercrime Tsunami

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    This paper reviews the impact of AI and emerging technologies on the future of cybercrime and the necessary strategies to combat it effectively. Society faces a pressing challenge as cybercrime proliferates through AI and emerging technologies. At the same time, law enforcement and regulators struggle to keep it up. Our primary challenge is raising awareness as cybercrime operates within a distinct criminal ecosystem. We explore the hijacking of emerging technologies by criminals (CrimeTech) and their use in illicit activities, along with the tools and processes (InfoSec) to protect against future cybercrime. We also explore the role of AI and emerging technologies (DeepTech) in supporting law enforcement, regulation, and legal services (LawTech)

    Dataset Construction and Analysis of Screenshot Malware

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    Among the various types of spyware, screenloggers are distinguished by their ability to capture screenshots. This gives them considerable nuisance capacity, giving rise to theft of sensitive data or, failing that, to serious invasions of the privacy of users. Several examples of attacks relying on this screen capture feature have been documented in recent years. However, there is not sufficient empirical and experimental evidence on this topic. Indeed, to the best of our knowledge, there is no dataset dedicated to screenshot-taking malware until today. The lack of datasets or common testbed platforms makes it difficult to analyse and study their behaviour in order to develop effective countermeasures. The screenshot feature is often a smart feature that does not activate automatically once the malware has infected the machine; the activation mechanisms of this function are often more complex. Consequently, a dataset which is completely dedicated to them would make it possible to better understand the subtleties of triggering screenshots and even to learn to distinguish them from the legitimate applications widely present on devices. The main purpose of this paper is to build such a dataset and analyse the behaviour of screenloggers

    A systematic review of ethical challenges and opportunities of addressing domestic violence with AI-technologies and online tools

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    Domestic violence remains a pressing complex social problem of people of any gender, age, socio-economic status, and ethno-cultural background, an issue that worsened worldwide during the COVID-19 pandemic. Digital, online, or artificial intelligence-based smart technological services, applications, and tools provide novel approaches in addressing domestic violence, including intimate partner violence. This systematic literature review analyses the ethical challenges and opportunities these (protective) digital and smart technologies provide to the stakeholders involved. Our results highlight that the public health and societal issue are the leading narratives of domestic violence, which is predominantly interpreted as gender-based violence. The review highlights an emerging trend of the role of machine learning- and artificial intelligence-based approaches in identifying and preventing domestic violence. However, we argue that little recommendation is available to professionals about how to use these approaches in a responsible way, and that the smartness of high-tech technologies is often challenged by basic-level technologies from perpetrators, creating an imbalance that also limits an impactful development of a comprehensive socio-technical regime that serves the safety and resilience of families in their communal setting

    Analyzing and Estimating Cyberattack Trends by Performing Data Mining on a Cybersecurity Data Set

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    More than five billion personal information has been compromised over the past eight years through data breaches from notable companies, and the damage related to cybercrime is expected to reach six trillion USD annually by the year of 2021. Interestingly, recent cyberattacks were aimed specifically at credit agencies and companies that hold credit information of their customers and employees. The question is: “Why is it difficult to protect against or evade cyberattacks even for these prestigious companies?”. The purpose of this research is to bring the notion of notorious, rapidly-multiplying cyberthreats. Hence, the research focuses on analyzing cyberattack techniques and finding effectiveness of surveillance methods that companies utilize to protect themselves from cyberattacks. In order to achieve this, we selected cyberattacks information and analyzed the data set through data mining, and the research findings suggest a future trend of cyberattacks efficient countermeasures. From the information gathered through data mining, the research findings suggest a future trend of cyberattacks and efficient countermeasures

    Privacy Preserving Internet Browsers: Forensic Analysis of Browzar

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    With the advance of technology, Criminal Justice agencies are being confronted with an increased need to investigate crimes perpetuated partially or entirely over the Internet. These types of crime are known as cybercrimes. In order to conceal illegal online activity, criminals often use private browsing features or browsers designed to provide total browsing privacy. The use of private browsing is a common challenge faced in for example child exploitation investigations, which usually originate on the Internet. Although private browsing features are not designed specifically for criminal activity, they have become a valuable tool for criminals looking to conceal their online activity. As such, Technological Crime units often focus their forensic analysis on thoroughly examining the web history on a computer. Private browsing features and browsers often require a more in-depth, post mortem analysis. This often requires the use of multiple tools, as well as different forensic approaches to uncover incriminating evidence. This evidence may be required in a court of law, where analysts are often challenged both on their findings and on the tools and approaches used to recover evidence. However, there are very few research on evaluating of private browsing in terms of privacy preserving as well as forensic acquisition and analysis of privacy preserving internet browsers. Therefore in this chapter, we firstly review the private mode of popular internet browsers. Next, we describe the forensic acquisition and analysis of Browzar, a privacy preserving internet browser and compare it with other popular internet browser
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