Tatsuya Mori

Taiga Ono, Takeshi Sugawara, Jun Sakuma et al.

This work aims to assess the reality and feasibility of the adversarial attack against cardiac diagnosis system powered by machine learning algorithms. To this end, we introduce adversarial beats, which are adversarial perturbations tailored specifically against electrocardiograms (ECGs) beat-by-beat classification system. We first formulate an algorithm to generate adversarial examples for the ECG classification neural network model, and study its attack success rate. Next, to evaluate its feasibility in a physical environment, we mount a hardware attack by designing a malicious signal generator which injects adversarial beats into ECG sensor readings. To the best of our knowledge, our work is the first in evaluating the proficiency of adversarial examples for ECGs in a physical setup. Our real-world experiments demonstrate that adversarial beats successfully manipulated the diagnosis results 3-5 times out of 40 attempts throughout the course of 2 minutes. Finally, we discuss the overall feasibility and impact of the attack, by clearly defining motives and constraints of expected attackers along with our experimental results.

Ryo Kawaoka, Daiki Chiba, Takuya Watanabe et al.

This work takes a first look at domain names related to COVID-19 (Cov19doms in short), using a large-scale registered Internet domain name database, which accounts for 260M of distinct domain names registered for 1.6K of distinct top-level domains. We extracted 167K of Cov19doms that have been registered between the end of December 2019 and the end of September 2020. We attempt to answer the following research questions through our measurement study: RQ1: Is the number of Cov19doms registrations correlated with the COVID-19 outbreaks?, RQ2: For what purpose do people register Cov19doms? Our chief findings are as follows: (1) Similar to the global COVID-19 pandemic observed around April 2020, the number of Cov19doms registrations also experienced the drastic growth, which, interestingly, pre-ceded the COVID-19 pandemic by about a month, (2) 70 % of active Cov19doms websites with visible content provided useful information such as health, tools, or product sales related to COVID-19, and (3) non-negligible number of registered Cov19doms was used for malicious purposes. These findings imply that it has become more challenging to distinguish domain names registered for legitimate purposes from others and that it is crucial to pay close attention to how Cov19doms will be used/misused in the future.

Hiroaki Suzuki, Daiki Chiba, Yoshiro Yoneya et al.

The internationalized domain name (IDN) is a mechanism that enables us to use Unicode characters in domain names. The set of Unicode characters contains several pairs of characters that are visually identical with each other; e.g., the Latin character 'a' (U+0061) and Cyrillic character 'a' (U+0430). Visually identical characters such as these are generally known as homoglyphs. IDN homograph attacks, which are widely known, abuse Unicode homoglyphs to create lookalike URLs. Although the threat posed by IDN homograph attacks is not new, the recent rise of IDN adoption in both domain name registries and web browsers has resulted in the threat of these attacks becoming increasingly widespread, leading to large-scale phishing attacks such as those targeting cryptocurrency exchange companies. In this work, we developed a framework named "ShamFinder," which is an automated scheme to detect IDN homographs. Our key contribution is the automatic construction of a homoglyph database, which can be used for direct countermeasures against the attack and to inform users about the context of an IDN homograph. Using the ShamFinder framework, we perform a large-scale measurement study that aims to understand the IDN homographs that exist in the wild. On the basis of our approach, we provide insights into an effective counter-measure against the threats caused by the IDN homograph attack.

Takuya Watanabe, Eitaro Shioji, Mitsuaki Akiyama et al.

This paper presents a practical side-channel attack that identifies the social web service account of a visitor to an attacker's website. Our attack leverages the widely adopted user-blocking mechanism, abusing its inherent property that certain pages return different web content depending on whether a user is blocked from another user. Our key insight is that an account prepared by an attacker can hold an attacker-controllable binary state of blocking/non-blocking with respect to an arbitrary user on the same service; provided that the user is logged in to the service, this state can be retrieved as one-bit data through the conventional cross-site timing attack when a user visits the attacker's website. We generalize and refer to such a property as visibility control, which we consider as the fundamental assumption of our attack. Building on this primitive, we show that an attacker with a set of controlled accounts can gain a complete and flexible control over the data leaked through the side channel. Using this mechanism, we show that it is possible to design and implement a robust, large-scale user identification attack on a wide variety of social web services. To verify the feasibility of our attack, we perform an extensive empirical study using 16 popular social web services and demonstrate that at least 12 of these are vulnerable to our attack. Vulnerable services include not only popular social networking sites such as Twitter and Facebook, but also other types of web services that provide social features, e.g., eBay and Xbox Live. We also demonstrate that the attack can achieve nearly 100% accuracy and can finish within a sufficiently short time in a practical setting. We discuss the fundamental principles, practical aspects, and limitations of the attack as well as possible defenses.

Mika Juuti, Bo Sun, Tatsuya Mori et al.

Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this work (char-LSTM ) has one drawback: it has difficulties staying in context, i.e. when it generates a review for specific target entity, the resulting review may contain phrases that are unrelated to the target, thus increasing its detectability. In this work, we present and evaluate a more sophisticated technique based on neural machine translation (NMT) with which we can generate reviews that stay on-topic. We test multiple variants of our technique using native English speakers on Amazon Mechanical Turk. We demonstrate that reviews generated by the best variant have almost optimal undetectability (class-averaged F-score 47%). We conduct a user study with skeptical users and show that our method evades detection more frequently compared to the state-of-the-art (average evasion 3.2/4 vs 1.5/4) with statistical significance, at level α = 1% (Section 4.3). We develop very effective detection tools and reach average F-score of 97% in classifying these. Although fake reviews are very effective in fooling people, effective automatic detection is still feasible.

Seita Maruyama, Satohiro Wakabayashi, Tatsuya Mori

We present a novel proof-of-concept attack named Trojan of Things (ToT), which aims to attack NFC- enabled mobile devices such as smartphones. The key idea of ToT attacks is to covertly embed maliciously programmed NFC tags into common objects routinely encountered in daily life such as banknotes, clothing, or furniture, which are not considered as NFC touchpoints. To fully explore the threat of ToT, we develop two striking techniques named ToT device and Phantom touch generator. These techniques enable an attacker to carry out various severe and sophisticated attacks unbeknownst to the device owner who unintentionally puts the device close to a ToT. We discuss the feasibility of the attack as well as the possible countermeasures against the threats of ToT attacks.