We analyze the increasing threats against IoT devices. We show that Telnet-based attacks that target IoT devices have rocketed since 2014. Based on this observation, we propose an IoT honeypot and sandbox, which attracts and analyzes Telnet-based attacks against various IoT devices running on different CPU architectures such as ARM, MIPS, and PPC. By analyzing the observation results of our honeypot and captured malware samples, we show that there are currently at least 5 distinct DDoS malware families targeting Telnet-enabled IoT devices and one of the families has quickly evolved to target more devices with as many as 9 different CPU architectures.

With the rise of IoT botnets, the remediation of infected devices has become a critical task. As over 87% of these devices reside in broadband networks, this task will fall primarily to consumers and the Internet Service Providers. We present the first empirical study of IoT malware cleanup in the wild-more specifically, of removing Mirai infections in the network of a medium-sized ISP. To measure remediation rates, we combine data from an observational study and a randomized controlled trial involving 220 consumers who suffered a Mirai infection together with data from honeypots and darknets. We find that quarantining and notifying infected customers via a walled garden, a best practice from ISP botnet mitigation for conventional malware, remediates 92% of the infections within 14 days. Email-only notifications have no observable impact compared to a control group where no notifications were sent. We also measure surprisingly high natural remediation rates of 58-74% for this control group and for two reference networks where users were also not notified. Even more surprising, reinfection rates are low. Only 5% of the customers who remediated suffered another infection in the five months after our first study. This stands in contrast to our lab tests, which observed reinfection of real IoT devices within minutes-a discrepancy for which we explore various different possible explanations, but find no satisfactory answer. We gather data on customer experiences and actions via 76 phone interviews and the communications logs of the ISP. Remediation succeeds even though many users are operating from the wrong mental model-e.g., they run antivirus software on their PC to solve the infection of an IoT device. While quarantining infected devices is clearly highly effective, future work will have to resolve several remaining mysteries. Furthermore, it will be hard to scale up the walled garden solution because of the weak incentives of the ISPs.

No abstract

Internet Service Providers (ISPs) are getting involved in remediating Internet of Things (IoT) infections of end users. This endeavor runs into serious usability problems. Given that it is usually unknown what kind of device is infected, they can only provide users with very generic cleanup advice, trying to cover all device types and remediation paths. Does this advice work? To what extent do users comply with the instructions? And does more compliance lead to higher cleanup rates? This study is the first to shed light on these questions. In partnership with an ISP, we designed a randomized control experiment followed up by a user survey. We randomly assigned 177 consumers affected by malware from the Mirai family to three different groups: (i) notified via a walled garden (quarantine network), (ii) notified via email, and (iii) no immediate notification, i.e. a control group. The notification asks the user to take five steps to remediate the infection. We conducted a phone survey with 95 of these customers based on communication–human information processing theory. We model the impact of the treatment, comprehension, and motivation on the compliance rate of each customer, while controlling for differences in demographics and infected device types. We also estimate the extent to which compliance leads to successful cleanup of the infected IoT devices. While only 24% of notified users perform all five remediation steps, 92% of notified users perform at least one action. Compliance increases the probability of successful cleanup by 32%, while the presence of competing malware reduces it by 54%. We provide an empirical basis to shape ISP best practices in the fight against IoT malware.

Substantial research has been conducted to develop proactive and reactive countermeasures against malware threats. Gathering and analyzing data are widely accepted approaches for accelerating the research towards understanding malware threats. However, collecting useful data is not an easy task for individuals or new researchers owing to several technical barriers, such as conducting honeypot operations securely. The anti-Malware engineering WorkShop (MWS) was organized in 2008 to fill this gap; since then, we have shared datasets that are useful for accelerating the data-driven anti-malware research in Japan. This paper provides the definitive collection of the MWS Datasets that are a collection of different datasets for use in anti-malware research. We also report the effectiveness of the MWS Datasets from the viewpoint of published research papers and how to empower some of the papers by using the MWS Datasets. Furthermore, our discussion about issues of the MWS Datasets reveal the future directions for accelerating anti-malware research from the perspectives of dataset collection activity and dataset use activity.