Port scanning is the de-facto method to enumerate active hosts and potentially exploitable services on the Internet. Over the last years, several studies have quantified the ecosystem of port scanning. Each work has found drastic changes in the threat landscape compared to the previous one, and since the advent of high-performance scanning tools and botnets a lot has changed in this highly volatile ecosystem.
Based on a unique dataset of Internet-wide scanning traffic collected in a large network telescope, we provide an assessment of Internet-wide TCP scanning with measurement periods in the last 10 years (2015 to 2024). We collect over 750 million scanning campaigns sending more than 45 billion packets and report on the evolution and developments of actors, their tooling, and targets. We find that Internet scanning has increased 30-fold over the last ten years, but the number and speed of scans have not developed at the same pace. We report that the ecosystem is extremely volatile, where targeted ports and geographical scanner locations drastically change at the level of weeks or months. We thus find that for an accurate understanding of the ecosystem we need longitudinal assessments. We show that port scanning becomes heavily commoditized, and many scanners target multiple ports. By 2024, well-known scanning institutions are targeting the entire IPv4 space and the entire port range.

Over the past decade, the scanning landscape has significantly changed. Powerful tools such as Masscan or Zmap allow anyone to scan the entire Internet in a matter of hours. Simultaneously, we witnessed the emergence of stealthy scanners, which map the Internet from thousands of vantage points at a low rate attempting to forego detection. As scanning is typically the first step towards later intrusion, organizations need to track, understand and draw intelligence from these scan campaigns. Organizations benefit from obtaining insights into what adversaries are currently looking for, which might reveal some new vulnerabilities. Furthermore, relating IP addresses with each other participating in scan campaigns provides valuable insights into the adversary's capabilities. In this paper, we describe a protocol-agnostic approach to extract commonalities and patterns from UDP scan traffic, relate individual scan packets regardless of whether they are sending static data or randomizing their payloads across destinations, and obtain 97% pattern accuracy with a data coverage of 96%. We apply our methodology on seven years of NTP and DNS scan traffic demonstrating that our automatic clustering provides stable tracking of strategies over time and identifies groups of source IPs with these behavioral characteristics effectively.

The Cyber Threat Intelligence (CTI) field has evolved rapidly and most of its reporting is now fairly stan-dardized. Where the Cyber Kill Chain was its sole reference framework 5 years ago, today ATT&CK is the de facto standard for reporting adversary tactics, techniques and procedures (TTPs). CTI frameworks are effectively abstraction layers of malicious behavior and thus effective CTI dissemination hinges on their ability to accurately represent this behavior. We argue that this is an area with significant opportunity for improvement. The aforementioned models are attacker- and intrusion-centric, while much of the CTI reporting currently is artifact- and malware-centric. In other words, most analysis is performed using artifacts of adversary tools, while in-the-wild evidence of adversary techniques and procedures is limited or lacking. Applying an intrusion model to artifact-based analysis leads to information loss, affecting and potentially misleading CTI-based decision-making. Intelligence analysis naturally builds on imperfect information, but CTI frameworks should be oriented more towards this key premise. In this conceptual work we compare the intrusion-centric ATT&CK with Malware Behavior Catalog (MBC), which is malware-centric. We compare how their application affects reporting of analysis outcomes. For this we reverse a piece of APT malware, replicating how many CTI reports are produced. We find that compared to ATT&CK, the abstraction offered by MBC enhances the information density of our reporting. While currently in most industry malware reports ATT&CK is applied, our analysis shows that on these occasions using MBC, potentially in tandem with ATT&CK, improves reporting. With the daily amount of new malware samples only increasing, accurate behavior labeling is key to the success of CTI sharing and dissemination.

Amplification attacks generate an enormous flood of unwanted traffic towards a victim and are generated with the help of open, unsecured services, to which an adversary sends spoofed service requests that trigger large answer volumes to a victim. However, the actual execution of the packet flood is only one of the activities necessary for a successful attack. Adversaries need, for example, to develop attack tools, select open services to abuse, test them, and adapt the attacks if necessary, each of which can be implemented in myriad ways. Thus, to understand the entire ecosystem and how adversaries work, we need to look at the entire chain of activities. This paper analyzes adversarial techniques, tactics, and procedures (TTPs) based on 549 honeypots deployed in 5 clouds that were rallied to participate in 13,479 attacks. Using a traffic shaping approach to prevent meaningful participation in DDoS activities while allowing short bursts of adversarial testing, we find that adversaries actively test for plausibility, packet loss, and amplification benefits of these servers, and show evidence of a 'memory' of previously exploited servers among attackers. In practice, we demonstrate that even for commonplace amplification attacks, adversaries exhibit differences in how they work.

In their pursuit to maximize their return on investment, cybercriminals will likely reuse as much as possible between their campaigns. Not only will the same phishing mail be sent to tens of thousands of targets, but reuse of the tools and infrastructure across attempts will lower their costs of doing business. This reuse, however, creates an effective angle for mitigation, as defenders can recognize domain names, attachments, tools, or systems used in a previous compromisation attempt, significantly increasing the cost to the adversary as it would become necessary to recreate the attack infrastructure each time. However, generating such cyber threat intelligence (CTI) is resource-intensive, so organizations often turn to CTI providers that commercially sell feeds with such indicators. As providers have different sources and methods to obtain their data, the coverage and relevance of feeds will vary for each of them. To cover the multitude of threats one organization faces, they are best served by obtaining feeds from multiple providers. However, these feeds may overlap, causing an organization to pay for indicators they already obtained through another provider. This paper presents a privacy-preserving protocol that allows an organization to query the databases of multiple data providers to obtain an estimate of their total coverage without revealing the data they store. In this way, a customer can make a more informed decision on their choice of CTI providers. We implement this protocol in Rust to validate its performance experimentally: When performed between three CTI providers who collectively have 20,000 unique indicators, our protocol takes less than 6 seconds to execute. The code for our experiments is freely available.

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target an ecosystem's weakest points. In Bitcoin, the exchange platforms are one of them. Each exchange breach is a threat not only to direct victims, but to the credibility of Bitcoin's entire ecosystem. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We show that while the security regimen of Bitcoin exchanges is subpar compared to other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. Furthermore we show that overall security posture has improved, but still has major flaws. To discover adversarial methods post-breach, we have analyzed two cases of BTC laundering. Through this analysis we provide insight into how exchange platforms with lax cyber security even further increase the intermediary risk introduced by them into the Bitcoin ecosystem.

In order to mount an effective defense, information about likely adversaries, as well as their techniques, tactics and procedures is needed. This so-called cyber threat intelligence helps an organization to better understand its threat profile. Next to this understanding, specialized feeds of indicators about these threats downloaded into a firewall or intrusion detection system allow for a timely reaction to emerging threats. These feeds however only provide an actual benefit if they are of high quality. In other words, if they provide relevant, complete information in a timely manner. Incorrect and incomplete information may even cause harm, for example if it leads an organization to block legitimate clients or if the information is too unspecific and results in an excessive amount of collateral damage. In this paper, we evaluate the quality of 17 open source cyber threat intelligence feeds over a period of 14 months, and 7 additional feeds over 7 months. Our analysis shows that the majority of indicators are active for at least 20 days before they are listed. Additionally, we have found that many list have biases towards certain countries. Finally, we also show that blocking listed IP addresses can yield large amounts of collateral damage.

To compromise a computer, it is first necessary to discover which hosts are active and which services they run. This reconnaissance is typically accomplished through port scanning. Defense systems monitor for these unsolicited packets and raise an alarm if a predefined threshold is exceeded. To remain undetected, adversaries can either slow down the scan, and/or distribute it over multiple hosts. With each source below the threshold, the combination of all may still complete the scan efficiently. It is especially this group that is of concern: with enough resources and knowledge to execute such a coordinated activity, they will pose a more potent threat than the noisy "script kiddie". Correlating which out of 4 billion IPs potentially collaborate is however a challenging task, hence today’s systems do not consider coordination beyond basic subnet aggregation.In this paper, we propose a method to identify and fingerprint distributed scanners based on commonalities in header fields, which are an artifact of the way fast port scanning software is built. We demonstrate that this method can effectively locate groups, and based on the monitoring logs we report on a number of new groups and tools, which have previously not been reported in the academic literature.Fingerprints generated can ultimately be used as Indicators of Compromise to detect and mitigate scanning behavior in order to deny adversaries the possibility to learn about weaknesses of a system.

Over the past decade, focus on the security and privacy aspects of implantable medical devices (IMDs) has intensified, driven by the multitude of cybersecurity vulnerabilities found in various existing devices. However, due to their strict computational, energy and physical constraints, conventional security protocols are not directly applicable to IMDs. Custom-tailored schemes have been proposed instead which, however, fail to cover the full spectrum of security features that modern IMDs and their ecosystems so critically require. In this paper we propose IMDfence, a security protocol for IMD ecosystems that provides a comprehensive yet practical security portfolio, which includes availability, non-repudiation, access control, entity authentication, remote monitoring and system scalability. The protocol also allows emergency access that results in the graceful degradation of offered services without compromising security and patient safety. The performance of the security protocol as well as its feasibility and impact on modern IMDs are extensively analyzed and evaluated. We find that IMDfence achieves the above security requirements at a mere less than 7% increase in total IMD energy consumption, and less than 14 ms and 9 kB increase in system delay and memory footprint, respectively.

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin's market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.

Website fingerprinting aims to identify the web page visited by a victim through the analysis of metadata generated by the encrypted flow between web server and victim. A fingerprinting attack can be performed at several locations and scales, ranging from local adversaries such as employers monitoring their employees browsing behavior to state sponsored actors monitoring civilians to uncover their political views. In this paper we show the feasibility of an attacker performing web page fingerprinting at a large scale by introducing a new twostage fingerprinting method. We evaluate our proposed method using a Wikipedia clone consisting of 828, 907 pages, allowing us to show that attackers are not only able to fingerprint pages from different websites but are also able to fingerprint similar pages belonging to the same website. More so, we show that, even though HTTP2 reduces the available metadata compared to HTTP, attackers using our method can achieve an accuracy of 62.21% when fingerprinting pages from our Wikipedia clone. Finally, we show that an attacker can, when taking browsing behavior into consideration, identify victims searching for specific information with an accuracy of 87.4%.

In order to direct network traffic towards applications, transport layer protocols such as TCP and UDP add the notion of a port number. A share of these numbers is registered for well-known services such as a web or mail, while some is left to be dynamically assigned by the OS to client connections. A special case is port 0 which is reserved but was never assigned. Traffic from and to port 0 is unusual, because it should not occur in the wild. As port 0 is unassigned, there is no common service listing for connections here. Furthermore, operating systems usually interpret the request to open port 0 as the request to allocate and open any currently unused port. Thus, traffic from and to port 0 should not occur, because no application should listen there and applications cannot send from port 0. In practice, we do however see traffic from and to port 0, which indicates that someone makes the effort to bypass the normal operating system network stack to create these unusual packets. As a corner case of network protocols, the aspect of port 0 has basically never been thoroughly investigated. In this paper, we analyze network traffic collected through a /15 network telescope over a period of 3 years to characterize these curious data flows. We find that port 0 traffic seems to be used in the wild by a select few for a variety of purposes, from DDoS attacks to system fingerprinting, and that some of these actors possess a surprisingly sophisticated knowledge of OS behavior.

In SSH brute forcing attacks, adversaries try a lot of different username and password combinations in order to compromise a system. As such activities are easily recognizable in log files, sophisticated adversaries distribute brute forcing attacks over a large number of origins. Effectively finding such distributed campaigns proves however to be a difficult problem. In practice, when adversaries would spread out brute-forcing over multiple sources, they would likely reuse the same kind of software across all of these origins to simplify their operation and reduce cost. This means if we are able to identify the tooling used in these attempts, we could cluster similar tool usage into likely collaborating hosts and thus campaigns. In this paper, we demonstrate that it is possible to utilize cipher suites and SSH version strings to generate a unique fingerprint for a brute-forcing tool used by the attacker. Based on a study using a large honeynet with over 4,500 hosts, which received approximately 35 million compromisation attempts over the period of one month, we are able to identify 49 tools from the collected data, which correspond to off-the-shelf tools, as well as custom implementations. The method is also able to fingerprint individual versions of tools, and by revealing mismatches between advertised and actually implemented features detect hosts that spoof identifying information. Based on the generated fingerprints, we are able to correlate login credentials to distinguish distributed campaigns. We uncovered specific adversarial behaviors, tactics and procedures, frequently exhibiting clear timing patterns and tight coordination.

Ever since the introduction of the domain name system (DNS), attacks on the DNS ecosystem have been a steady companion. Over time, targets and techniques have shifted, and in the recent past a new type of attack on the DNS has emerged. In this paper we report on the DNS random subdomain attack, querying floods of non-existent subdomains, intended to cause a denial-of-service on DNS servers. Based on five major attacks in 2018 obtained through backscatter measurements in a large network telescope, we show the techniques pursued by adversaries, and develop a taxonomy of strategies of this attack.

The release of an efficient browser-based cryptominer, as introduced by Coinhive in 2017, has quickly spread throughout the web either as a new source of revenue for websites or exploited within the context of hacks and malicious advertisements. Several studies have analyzed the Alexa Top 1M and found 380 - 3,200 [5, 15, 18, 30, 31] (0.038% - 0.32%) to be actively mining, with an estimated $41,000 per month revenue for the top 10 perpetrators [18]. While placing a cryptominer on a popular website supplies considerable returns from its visitors' web browsers, it only generates revenue while a client is visiting the page. Even though large popular websites attract millions of visitors, the relatively low number of exploiting websites limits the total revenue that can be made. In this paper, we report on a new attack vector that drastically overshadows all existing cryptojacking activity discovered to date. Through a firmware vulnerability in MikroTik routers, cyber criminals are able to rewrite outgoing user traffic and embed cryptomining code in every outgoing web connection. Thus, every web page visited by any user behind an infected router would mine to profit the criminals. Based on NetFlows recorded in a Tier 1 network, semiweekly crawls and telescope traffic, we followed their activities over a period of 10 months, and report on the modus operandi and coordinating infrastructure of the perpetrators, which were during this period in control of up to 1.4M routers, approximately 70% of all MikroTik devices deployed worldwide. We observed different levels of sophistication among adversaries, ranging from individual installations to campaigns involving large numbers of routers. Our results show that cryptojacking through MITM attacks is highly lucrative, a factor of 30 more than previous attack vectors.

Since the release of a browser-based cryptominer by Coinhive in 2017, the easy use of these miners has skyrocketed illicit cryptomining in 2017 and continued in 2018. This method of monetizing websites attracted website owners, as well as criminals seeking new ways to earn a profit. In this paper, we perform two large studies into the world of cryptojacking, focused on organized cryptomining and the spread of cryptojacking on the Internet. We have identified 204 cryptojacking campaigns, an order of magnitude more than previous work, which indicates that these campaigns are heavily underestimated by previous studies. We discovered that criminals have chosen third-party software - such as WordPress - as their new method for spreading cryptojacking infections efficiently. With a novel method of using NetFlow data we estimated the popularity of mining applications, which showed that while Coinhive has a larger installation base, CoinImp WebSocket proxies were digesting significantly more traffic in the second half of 2018. After crawling a random sample of 49M domains, ~20% of the Internet, we conclude that cryptojacking is present on 0.011% of all domains and that adult content is the most prevalent category of websites affected.

In an ever-changing landscape of adversary tactics, techniques and procedures (TTPs), malware remains the tool of choice for attackers to gain a foothold on target systems. The Mitre ATT&CK framework is a taxonomy of adversary TTPs. It is meant to advance cyber threat intelligence (CTI) by establishing a generic vocabulary to describe post-compromise adversary behavior. This paper discusses the results of automated analysis of a sample of 951 Windows malware families, which have been plotted on the ATT&CK framework. Based on the framework’s tactics and techniques we provide an overview of established techniques within Windows malware and techniques which have seen increased adoption over recent years. Within our dataset we have observed an increase in techniques applied for fileless execution of malware, discovery of security software and DLL side-loading for defense evasion. We also show how a sophisticated technique, command and control (C&C) over IPC named pipes, is getting adopted by less sophisticated actor groups. Through these observations we have identified how malware authors are innovating techniques in order to bypass established defenses.

In order for malicious software to receive configuration information or commands, malware needs to be able to locate and connect to its owner. As hard-coded addresses are easy to block and thus render the malware installation inoperable, malware writers have turned to dynamically generated addresses. Domain generation algorithms (DGA) generate a list of candidate domain names, each valid for only a short time, at which the malware installation searches for its command & control (C&C) server. As DGAs generate a large list of potential domains - out of which one or a few is actually in use -, they leave a characteristic trace of many failed DNS lookups (NXDomain) in the network, and in result most DGAs can be efficiently detected. In this paper we describe an entirely new principle of domain generation, actively deployed in the Cerber ransomware, which finds and coordinates with its owner based on transaction information in the bitcoin blockchain. This allows the malware author to dynamically update the location of the server in realtime, and as the malware directly goes to the right location no longer generates a sequence of NXDomain responses. We describe the concept of coordination via the blockchain, and report results on a year-long observation of the assets used in the Cerber campaign.

LoRaWAN is a MAC-layer protocol for long-range low-power communication. Since its release in 2015, it has experienced a rapid adoption in the field of Internet-of-Things (IoT). However, given that LoRaWAN is fairly novel, its level of security has not been thoroughly analyzed, which is the main objective of this paper. We highlight the security features present in LoRaWAN, namely activation methods, key management, cryptography, counter management, and message acknowledgement. Subsequently, we discover and analyze several vulnerabilities of LoRaWAN. In particular, we design and describe 5 attacks: (1) a replay attack that leads to a selective denial-of-service on individual IoT devices, (2) plaintext recovery, (3) malicious message modification, (4) falsification of delivery reports, and (5) a battery exhaustion attack. As a proof-of-concept, the attacks are implemented and executed in a controlled LoRaWAN environment. Finally, we discuss how these attacks can be mitigated or protected against.

Industrial Control Systems are under increased scrutiny. Their security is historically sub-par, and although measures are being taken by the manufacturers to remedy this, the large installed base of legacy systems cannot easily be updated with state-of-the-art security measures. We propose a system that uses electromagnetic side-channel measurements to detect behavioural changes of the software running on industrial control systems. To demonstrate the feasibility of this method, we show it is possible to profile and distinguish between even small changes in programs on Siemens S7-317 PLCs, using methods from cryptographic side-channel analysis.