The proliferation of Internet of Things (IoT) devices has led to a surge in vulnerabilities, with traditional metrics like CVSS and PoC exploits failing to fully explain exploitation patterns. To address this, we leverage features from the-state-of-the-art prediction model EPSS – such as CVSS, CWE, vendors, external references, vulnerability age, and PoCs – and combine it with new features derived from hacking communities. Our study of 23,373 IoT-related CVEs and 25k posts from 25 hacking forums highlights the importance of including insights on attacker behavior from discussions involving vulnerabilities. We identified 38 features with a p-value < 0.05 that impact attackers’ selection of IoT vulnerabilities. We use two metrics to evaluate our model with features from hacking forums: McFadden’s pseudo R2, which showed a 21% improvement in explaining variance, and the Brier score for prediction accuracy, with a 17% improvement over EPSS. These results emphasize that current state-of-the-art methods struggle to capture the distinct nuances and complexity of IoT threats, and incorporating available information such as insights into attacker behavior can enhance the factors influencing the targeting of IoT vulnerability better.

Remotely Triggered Black Hole (RTBH) is a common DDoS mitigation approach that has been in use for the last two decades. Usually, it is implemented close to the attack victim in networks sharing some type of physical connectivity. The Unwanted Traffic Removal Service (UTRS) project offers a free, global, and relatively low-effort-to-join and operate RTBH alternative by removing the requirement of physical connectivity. Given these unique value propositions of UTRS, this paper aims to understand to what extent UTRS is adopted and used to mitigate DDoS attacks. To reach this goal, we collected two DDoS datasets describing amplification and Internet-of-Things-botnet-driven attacks and correlated them with the information from the third dataset containing blackholing requests propagated to the members of UTRS. Our findings suggest that, currently, just a small portion of UTRS members (approximately 10 % ) trigger mitigation attempts: out of 1200+ UTRS members, only 124 triggered blackholing events during our study. Among those, with high probability, 25 Autonomous Systems (ASes) reacted on AmpPot attacks mitigating 0.025 % of them globally or 1.03 % targeting UTRS members; 2 countered IoT-botnet-driven attacks alleviating 0.001 % of them globally or 0.06 % targeting UTRS members. This suggests that UTRS can be a useful tool in mitigating DDoS attacks, but it is not widely used.

Carpet bombing-type DDoS attacks targeting a wide-range network rather than a single IP address have threatened the Internet. Some researchers have investigated the characteristics of single-target DDoS attacks. Still, much less is known about the characteristics of carpet bombing, even the differences between them. In this paper, we profile characteristics of carpet bombing via data from amplification DDoS honeypots and the differences between single-target DRDoS attacks and carpet bombing. We analyze attacks highly concentrated on a specific network on victims, duration, number of packets, ports, and TTLs, and describe the differences between single-target DRDoS attacks and carpet bombing. Our analysis at the level of Autonomous Systems demonstrates that carpet bombing attacks target more hosting networks, including some critical targets, than single-target attacks. We found carpet bombing attacks targeting more “Corporate” networks. We also found that each IP address targeted by carpet bombing receives fewer packets than single-target DRDoS attacks. According to the result of the comparison of attack duration and TTL, carpet bombing lasted longer and referred to having diverse values of TTL in the packets. On the contrary, most single-target DRDoS attacks have a single value of TTL in the packets. This implies carpet bombing has a higher probability of originating from multiple sources. Finally, comparing ports shows that using various ports for Carpet Bombing is highly proportional to single-target DRDoS attacks.

We investigate the potential for abuse of recent AI advances by developing seven malware programs and two attack tools using ChatGPT, OpenAI Playground's "text-davinci-003"model, and Auto-GPT - an open-source AI agent capable of generating automated prompts to accomplish user-defined goals. We confirm that: 1) Under the safety and moderation control of recent AI systems, it is possible to generate the functional malware and attack tools (up to about 400 lines of code) within 90 minutes, including the debugging time. 2) Auto-GPT does not ease the hurdle of generating the right prompts for malware generation, but it evades the safety controls of OpenAI with its automatically generated prompts. When given goals with sufficient details, it writes the code in nine of nine malware and attack tools we tested. 3) There is still room to improve the moderation and safety controls of ChatGPT and text-davinci-003 model, especially for the growing jailbreak prompts. Overall, we find that recent AI advances, including ChatGPT, Auto-GPT, and text-davinci-003, demonstrate the potential for generating malware and attack tools under safety and moderation control, highlighting the need for improved safety measures and enhanced safety controls in AI systems.

For years, attackers have exploited vulnerabilities in Internet of Things (IoT) devices. Previous research has examined target selection in cybercrime, but there has been little investigation into the factors that influence target selection in attacks on IoT. This study aims to better understand how attackers choose their targets by analyzing the frequency of specific exploits in 11,893 IoT malware binaries that were distributed between 2018-2021. Our findings indicate that 78% of these binary files did not specifically target IoT vulnerabilities but rather scanned the Internet for devices with weak authentication. To understand the usage of exploits in the remaining 2,629 binaries, we develop a theoretical model from relevant literature to examine the impact of four latent variables, i.e. exposure, vulnerability, exploitability, and patchability. We collect indicators to measure these variables and find that they can explain to a significant extent (?2=0.38) why some vulnerabilities are more frequently exploited than others. The severity of vulnerabilities does not significantly increase the frequency with which they are targeted, while the presence of Proof-of-Concept exploit code does increase it. We also observe that the availability of a patch reduces the frequency of being targeted, yet that more complex patches are associated with higher frequency. In terms of exposure, more widespread device models are more likely to be targeted by exploits. We end with recommendations to disincentivize attackers from targeting vulnerabilities.

The Internet of things (IoT) is composed by a wide variety of software and hardware components that inherently contain vulnerabilities. Previous research has shown that it takes only a few minutes from the moment an IoT device is connected to the Internet to the first infection attempts. Still, we know little about the evolution of exploit vectors: Which vulnerabilities are being targeted in the wild, how has the functionality changed over time, and for how long are vulnerabilities being targeted? Understanding these questions can help in the secure development, and deployment of IoT networks. We present the first longitudinal study of IoT malware exploits by analyzing 17,720 samples collected from three different sources from 2015 to 2020. Leveraging static and dynamic analysis, we extract exploits from these binaries to then analyze them along the following four dimensions: (1) evolution of infection vectors over the years, (2) exploit lifespan, vulnerability age, and the time-to-exploit of vulnerabilities, (3) functionality of exploits, and (4) targeted IoT devices and manufacturers. Our descriptive analysis uncovers several patterns: IoT malware keeps evolving, shifting from simply leveraging brute force attacks to including dozens of device-specific exploits. Once exploits are developed, they are rarely abandoned. The most recent binaries still target (very) old vulnerabilities. In some cases, new exploits are developed for a vulnerability that has been known for years. We find that the mean time-to-exploit after vulnerability disclosure is around 29 months, much longer than for malware targeting other environments.

Geographically distributed infrastructures, such as buildings, dams, and solar power plants, are commonly maintained via Internet-connected remote management devices. Previous studies on detecting and securing industrial control systems (ICS) have overlooked these remote management devices, as they do not expose ICS-specific services like Modbus and BACnet and thus do not show up in Internet-wide scans for such services. In this paper, we implement and validate a discovery method for these devices via their Web User Interface (WebUI) and detect 890 devices in Japan alone. We also show that many of these devices are highly insecure. Many allow access to the status or even the control over industrial systems without proper authentication. Taking a closer look at three prevalent remote management devices, we discovered 13 0-day vulnerabilities, several of which were rated as medium or high severity. They have been responsibly disclosed to the manufacturers. By using honeypots that imitate these systems, we show that over time, only a small number of attackers enter these systems, but some do change critical parameters. Attackers appear to interact more with the system when more facility information is displayed on the WebUI. Finally, we notified operators of 317 vulnerable remote management devices by email and telephone. We reached 212 persons in charge of the devices and received confirmation that our method had correctly identified the device. 50% of the persons in charge of the devices stated that they mitigated or will mitigate the problem. We confirmed their actions via a followup scan for vulnerable devices and found that measures were taken for 58% of the devices when we could reach the persons in charge of the device.

Large botnets made up of Internet-of-Things (IoT) devices have a steady presence in the threat landscape since 2016. However, it has not explained how attackers maintain control over their botnets. In this paper, we present a long-term analysis of the infrastructure of IoT botnets based on 36 months of data gathered via honeypots and the monitoring of botnet infrastructure. We collected 64,260 IoT malware samples, 35,494 download servers, and 4,736 C&C servers during 2016 to 2021. Not only are most binaries distributed for less than three days, but the connection of bots to the rest of the botnet is also short-lived. To reach the C&C server, the binaries typically contain only a single hard-coded IP address or domain. Long-term dynamic analysis finds no mechanism for the attackers to migrate the bots to a new C&C server. Although malware binaries that use domain names to connect to their C&C servers increased in 2020, the C&C servers themselves have a short lifespan and this tendency has not changed. The picture that emerges is that of highly disposable botnets. IoT botnets are reconstituted from scratch all the time rather than maintained.

Distributed Denial of Service attacks against the application layer (L7 DDoS) are among the most difficult attacks to defend against because they mimic normal user behavior. Some mitigation techniques against L7 DDoS, e.g., IP blacklisting and load balancing using a content delivery network, have been proposed; unfortunately, these are symptomatic treatments rather than fundamental solutions. In this paper, we propose a novel technique to disincentivize attackers from launching a DDoS attack by increasing attack costs. Assuming financially motivated attackers seeking to gain profit via DDoS attacks, their primary goal is to maximize revenue. On the basis of this assumption, we also propose a mitigation solution that requires mining cryptocurrencies to access servers. To perform a DDoS attack, attackers must mine cryptocurrency as a proof-of-work (PoW), and the victims then obtain a solution to the PoW. Thus, relative to attackers, the attack cost increases, and, in terms of victims, the economic damage is compensated by the value of the mined coins. On the basis of this model, we evaluate attacker strategies in a game theory manner and demonstrate that the proposed solution provides only negative economic benefits to attackers. Moreover, we implement a prototype to evaluate performance, and we show that this prototype demonstrates practical performance.

Large botnets made up of Internet-of-Things (IoT) devices have been a steady presence in the threat landscape since 2016. Earlier research has found preliminary evidence that the IoT binaries and C&C infrastructure were only seen for very brief periods. It has not explained how attackers maintain control over their botnets. We present a more comprehensive analysis of the infrastructure of IoT botnets based on 23 months of data gathered via honeypots and the monitoring of botnet infrastructure. We collected 59,884 IoT malware samples, 35,494 download servers, and 2,747 C&C servers. We focuse on three dominant families: Bashlite, Mirai, and Tsunami. The picture that emerges is that of highly disposable botnets. IoT botnet are not so much maintained as reconstituted from scratch all the time. Not only are most binaries distributed for less than three days, the connection of bots to the rest of the botnet is also short-lived. To reach the C&C server, the binaries typically contain only a single hard-coded IP address or domain. The C&C servers themselves also have a short lifespan. Long-term dynamic analysis finds no mechanism for the attackers to migrate the bots to a new C&C server. In other words, bots are used only immediately after capture and then abandoned - -perhaps to be recaptured again via the aggressive scanning practices that these botnets are known for. While IoT botnets appear less advanced than Windows-based botnets, the advantage of being disposable means that they are very resistant to blacklisting and C&C takedown. Most IP addresses are used only once and never seen again. The question that arises is how attackers source these addresses. We speculate that they might be abusing the IP address allocation practices of cloud providers.

Open-source honeypots are a vital component in the protection of networks and the observation of trends in the threat landscape. Their open nature also enables adversaries to identify the characteristics of these honeypots in order to detect and avoid them. In this study, we investigate the prevalence of 14 open-source honeypots running more or less default configurations, making them easily detectable by attackers. We deploy 20 simple signatures and test them for false positives against servers for domains in the Alexa top 10,000, official FTP mirrors, mail servers in real operation, and real IoT devices running telnet. We find no matches, suggesting good accuracy. We then measure the Internet-wide prevalence of default open-source honeypots by matching the signatures with Censys scan data and our own scans. We discovered 19,208 honeypots across 637 Autonomous Systems that are trivially easy to identify. Concentrations are found in research networks, but also in enterprise, cloud and hosting networks.
While some of these honeypots probably have no operational relevance, e.g., they are student projects, this explanation does not fit the wider population. One cluster of honeypots was confirmed to belong to a well-known security center and was in use for ongoing attack monitoring. Concentrations in an another cluster appear to be the result of government incentives. We contacted 11 honeypot operators and received response from 4 operators, suggesting the problem of lack of network hygiene. Finally, we find that some honeypots are actively abused by attackers for hosting malicious binaries. We notified the owners of the detected honeypots via their network operators and provided recommendations for customization to avoid simple signature-based detection. We also shared our results with the honeypot developers.

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 anti-virus 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.

A lot of research has been devoted to understanding the technical properties of amplification DDoS attacks and the emergence of the DDoS-as-a-service economy, especially the so-called booters. Much less is known about the consequences for victimization patterns. We profile victims via data from amplification DDoS honeypots. We develop victimization rates and present explanatory models capturing key determinants of these rates. Our analysis demonstrates that the bulk of the attacks are directed at users in access networks, not at hosting, and even less at enterprise networks. We find that victimization in broadband ISPs is highly proportional to the number of ISP subscribers and that certain countries have significantly higher or lower victim rates which are only partially explained by institutional factors such as ICT development. We also find that victimization rate in hosting networks is proportional to the number of hosted domains and number of routed IP addresses and that content popularity has a minor impact on victimization rates. Finally, we reflect on the implications of these findings for the wider trend of commoditization in cybercrime.