Monthly Archives: January 2020

Identifying Unintended Harms of Cybersecurity Countermeasures

In this paper (winner of the eCrime 2019 Best Paper award), we consider the types of things that can go wrong when you intend to make things better and more secure. Consider this scenario. You are browsing through Internet and see a news headline on one of the presidential candidates. You are unsure if the headline is true. What you can do is to navigate to a fact-checking website and type in the headline of interest. Some platforms also have fact-checking bots that would update periodically on false information. You do some research through three fact-checking websites and the results consistently show that the news contains false information. You share the results as a comment on the news article. Within two hours, you receive hundreds of notifications with comments countering your resources with other fact-checking websites. 

Such a scenario is increasingly common as we rely on the Internet and social media platforms for information and news. Although they are meant to increase security, these cybersecurity countermeasures can result in confusion and frustration among users due to the incorporation of additional actions as part of users’ daily online routines. As seen, fact-checking can easily be used as a mechanism for attacks and demonstration of in-group/out-group distinction which can contribute further to group polarisation and fragmentation. We identify these negative effects as unintended consequences and define it as shifts in expected burden and/or effort to a group. 

To understand unintended harms, we begin with five scenarios of cyber aggression and deception. We identify common countermeasures for each scenario, and brainstorm potential unintended harms with each countermeasure. The unintended harms are inductively organized into seven categories: 1) displacement, 2) insecure norms, 3) additional costs, 4) misuse, 5) misclassification, 6) amplification and 7) disruption. Applying this framework to the above scenario, insecure norms, miuse, and amplification are both unintended consequences of fact-checking. Fact-checking can foster a sense of complacency where checked news are automatically seen as true. In addition, fact-checking can be used as tools for attacking groups of different political views. Such misuse facilitates amplification as fact-checking is being used to strengthen in-group status and therefore further exacerbate the issue of group polarisation and fragmentation. 

To allow for a systematic application to existing or new cybersecurity measures by practitioners and stakeholders, we expand the categories into a functional framework by developing prompts for each harm category. During this process, we identify the underlying need to consider vulnerable groups. In other words, practitioners and stakeholders need to take into consideration the impacts of countermeasures on at-risk groups as well as the possible creation of new vulnerable groups as a result of deploying a countermeasure. Vulnerable groups refer to user groups who may suffer while others are unaffected or prosper from the countermeasure. One example is older adult users where their non-familiarity and less frequent interactions with technologies means that they are forgotten or hidden when assessing risks and/or countermeasures within a system. 

It is important to note the framework does not propose measurements for the severity or the likelihood of unintended harm occurring. Rather, the emphasis of the framework is in raising stakeholders’ and practitioners’ awareness of possible unintended consequences. We envision this framework as a common-ground tool for stakeholders, particularly for coordinating approaches in complex, multi-party services and/or technology ecosystems.  We would like to extend a special thank you to Schloss Dagstuhl and the organisers of Seminar #19302 (Cybersafety Threats – from Deception to Aggression). It brought all of the authors together and laid out the core ideas in this paper. A complimentary blog post by co-author Dr Simon Parkin can be found at UCL’s Benthams Gaze blog. The accepted manuscript for this paper is available here.

From Playing Games to Committing Crimes: A Multi-Technique Approach to Predicting Key Actors on an Online Gaming Forum

I recently travelled to Pittsburgh, USA, to present the paper “From Playing Games to Committing Crimes: A Multi-Technique Approach to Predicting Key Actors on an Online Gaming Forum” at eCrime 2019, co-authored with Ben Collier and Alice Hutchings. The accepted version of the paper can be accessed here.

The structure and content of various underground forums have been studied in the literature, from threat detection to the classification of marketplace advertisements. These platforms can provide a mechanism for knowledge sharing and a marketplace between cybercriminals and other members.

However, gaming-related activity on underground hacking forums have been largely unexplored. Meanwhile, UK law enforcement believe there is a potential link between playing online games and committing cybercrime—a possible cybercrime pathway. A small-scale study by the NCA found that users looking for gaming cheats on these types of forums can lead to interactions with users involved in cybercrime, leading to a possible first offences, followed by escalating levels of offending. Also, there has been interest from UK law enforcement in exploring intervention activity which aim to deter gamers from becoming involved in cybercrime activity.

We begin to explore this by presenting a data processing pipeline framework, used to identify potential key actors on a gaming-specific forum, using predictive and clustering methods on an initial set of key actors. We adapt open-source tools created for use in analysis of an underground hacking forum and apply them to this forum. In addition, we add NLP features, machine learning models, and use group-based trajectory modelling.

From this, we can begin to characterise key actors, both by looking at the distributions of predictions, and from inspecting each of the models used. Social network analysis, built using author-replier relationships, shows key actors and predicted key actors are well connected, and group-based trajectory modelling highlights a much higher proportion of key actors are contained in both a high-frequency super-engager trajectory in the gaming category, and in a high-frequency super-engager posting activity in the general category.

This work provides an initial look into a perceived link between playing online games and committing cybercrime by analysing an underground forum focused on cheats for games.

Honware: A Virtual Honeypot Framework for Capturing CPE and IoT Zero Days

Existing defenses are slow to detect zero day exploits and capture attack traffic targeting inadequately secured Customer Premise Equipment (CPE) and Internet of Things (IoT) devices. This means that attackers have considerable periods of time to find and compromise vulnerable devices before the attack vectors are well understood and mitigation is in place.

About a month ago we presented honware at eCrime 2019, a new honeypot framework that enables the rapid construction of honeypots for a wide range of CPE and IoT devices. The framework automatically processes a standard firmware image (as is commonly provided for updates) and runs the system with a special pre-built Linux kernel without needing custom hardware. It then logs attacker traffic and records which of their actions led to a compromise.

We provide an extensive evaluation and show that our framework is scalable and significantly better than existing emulation strategies in emulating the devices’ firmware applications. We were able to successfully process close to 2000 firmware images across a dozen brands (TP-Link, Netgear, D-Link…) and run them as honeypots. Also, as we use the original firmware images, the honeypots are not susceptible to fingerprinting attacks based on protocol deviations or self-revealing properties.

By simplifying the process of deploying realistic honeypots at Internet scale, honware supports the detection of malware types that often go unnoticed by users and manufactures. We hope that honware will be used at Internet scale by manufacturers setting up honeypots for all of their products and firmware versions or by researchers looking for new types of malware.

The paper is available here.