Recent years have witnessed the rise of Internet-of-Things
(IoT) based cyber attacks. These attacks, as expected, are
launched from compromised IoT devices by exploiting security
flaws already known. Less clear, however, are the fundamental
causes of the pervasiveness of IoT device vulnerabilities
and their security implications, particularly in how
they affect ongoing cybercrimes. To better understand the
problems and seek effective means to suppress the wave of
IoT-based attacks, we conduct a comprehensive study based
on a large number of real-world attack traces collected from
our honeypots, attack tools purchased from the underground,
and information collected from high-profile IoT attacks. This
study sheds new light on the device vulnerabilities of today’s
IoT systems and their security implications: ongoing cyber
attacks heavily rely on these known vulnerabilities and the
attack code released through their reports; on the other hand,
such a reliance on known vulnerabilities can actually be used
against adversaries. The same bug reports that enable the development
of an attack at an exceedingly low cost can also be
leveraged to extract vulnerability-specific features that help
stop the attack. In particular, we leverage Natural Language
Processing (NLP) to automatically collect and analyze more
than 7,500 security reports (with 12,286 security critical IoT
flaws in total) scattered across bug-reporting blogs, forums,
and mailing lists on the Internet. We show that signatures
can be automatically generated through an NLP-based report
analysis, and be used by intrusion detection or firewall systems
to effectively mitigate the threats from today’s IoT-based
attacks.
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Microarchitectural Attacks in Heterogeneous Systems: A Survey
With the increasing proliferation of hardware accelerators and the predicted continued increase in the heterogeneity of future computing systems, it is necessary to understand the security properties of such systems. In this survey article, we consider the security of heterogeneous systems against microarchitectural attacks, with a focus on covert- and side-channel attacks, as well as fault injection attacks. We review works that have explored the vulnerability of the individual accelerators (such as Graphical Processing Units, GPUs and Field Programmable Gate Arrays, FPGAs) against these attacks, as well as efforts to mitigate them. We also consider the vulnerability of other components within a heterogeneous system such as the interconnect and memory components. We believe that this survey is especially timely, as new accelerators and heterogeneous systems are being designed such that these designs understand the security threats and develop systems that are not only performant but also secure.
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- NSF-PAR ID:
- 10342982
- Date Published:
- Journal Name:
- ACM Computing Surveys
- ISSN:
- 0360-0300
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Recent years have witnessed the rise of Internet-of-Things (IoT) based cyber attacks. These attacks, as expected, are launched from compromised IoT devices by exploiting security flaws already known. Less clear, however, are the fundamental causes of the pervasiveness of IoT device vulnerabilities and their security implications, particularly in how they affect ongoing cybercrimes. To better understand the problems and seek effective means to suppress the wave of IoT-based attacks, we conduct a comprehensive study based on a large number of real-world attack traces collected from our honeypots, attack tools purchased from the underground, and information collected from high-profile IoT attacks. This study sheds new light on the device vulnerabilities of today's IoT systems and their security implications: ongoing cyber attacks heavily rely on these known vulnerabilities and the attack code released through their reports; on the other hand, such a reliance on known vulnerabilities can actually be used against adversaries. The same bug reports that enable the development of an attack at an exceedingly low cost can also be leveraged to extract vulnerability-specific features that help stop the attack. In particular, we leverage Natural Language Processing (NLP) to automatically collect and analyze more than 7,500 security reports (with 12,286 security critical IoT flaws in total) scattered across bug-reporting blogs, forums, and mailing lists on the Internet. We show that signatures can be automatically generated through an NLP-based report analysis, and be used by intrusion detection or firewall systems to effectively mitigate the threats from today's IoT-based attacks.more » « less
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Schmorrow, D. ; Fidopiastis, C. (Ed.)As security measures to protect against cyberattacks increase, hackers have begun to target the weakest link in the cybersecurity chain–people. Such attacks are categorized as Social Engineering and rely on the manipulation and deception of people rather than technical security flaws [4]. This study attempts to examine the relationship between people and their vulnerability to Social Engineering attacks by posing the following questions: (1) what relationship, if any, exists between personality traits and Social Engineering vulnerability, and (2) what relationship, if any, exists between personality traits and the speed at which an individual makes cybersecurity-related decisions. To answer these questions, 79 undergraduate students at the University of Hawaii were surveyed to measure their personality traits and cybersecurity awareness. The survey results indicated that there was no significant correlation between the measured personality traits and measured vulnerability. The relationship between different personality traits and the elapsed time to complete the survey was slightly more significant; how-ever, it was still statistically insignificant overall.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3544102
