Since the outbreak of IoT malware “Mirai,” several incidents have occurred in which IoT devices have been infected with malware. The malware targets IoT devices whose Telnet and SSH services are accessible from the Internet and whose ID/Password settings are not strong enough. Several IoT malware families, including Mirai, are also known that restrict access to Telnet and other services to keep the devices from being infected by other malware after infection. However, tens of thousands of devices in Japan can be still accessed Telnet services over the Internet according to network scan results. Does this imply that these devices can avoid malware infection by setting strong enough passwords, and thus cannot be used as a stepping stone for cyber attacks? In February 2019, we initiated the National Operation Toward IoT Clean Environment (NOTICE) project in Japan to investigate IoT devices with weak credentials and notify the device users. In this study, we analyze the results of the NOTICE project from February 2021 to May 2021 and the results of the large-scale darknet monitoring to reveal whether IoT devices with weak credentials are infected with malware or not. Moreover, we analyze the IoT devices with weak credentials to find out the factors that prevent these devices from being infected with malware and to assess the risk of abuse for cyber attacks. From the results of the analysis, it is discovered that approximately 2,000 devices can be easily logged in using weak credentials in one month in Japan. We also clarify that no device are infected with Mirai and its variants malware due to lack of functions used for malware infection excluding only one host. Finally, even the devices which are logged in by NOTICE project are not infected with Mirai, we find that at least 80% and 93% of the devices can execute arbitrary scripts and can send packets to arbitrary destinations respectively.

As the number of new malware has increased explosively, traditional malware detection approaches based on pattern matching have been less effective. Therefore, it is important to develop a detection method which relies on not signatures but characteristic behaviors of malware. Recently, malware authors have been embedding functions for countermeasure against malware analyses and detections into malware. Accordingly, modern malware often changes their runtime behaviors in each execution to tolerate against malware analyses and detections. For example, when malware copies itself on a file system, it can randomly determine its file name for avoiding the detections. Another example is that when malware tries to connect its command and control server, it randomly chooses a domain name from a hard-coded domain name list to avoid being blocked by a static blacklist of malicious domain names. We assume that such evasive behaviors are unnecessary for benign software. Therefore the behaviors can be the clues to distinguish malware from benign software. In this paper, we propose a novel behavior-based malware detection method which focuses attention on such characteristics. Our proposed method conducts dynamic analysis on an executable file multiple times in same sandbox environment so as to obtain plural lists of API call sequences and plural traffic logs, and then compares the lists and the logs to find the difference between the multiple executions. In the experiments with 5,697 malware samples and 819 benign software samples, we can detect about 70% malware samples and the false positive rate is about 1%. In addition, we can detect about 50% malware samples which were not detected by each Anti-Virus Software engine. Therefore we confirm the possibility the proposed method may be able to improve the accuracy of malware detection utilizing in combination with other existing methods.