Interesting research: “Identifying Unintended Harms of Cybersecurity Countermeasures“:
Abstract: Well-meaning cybersecurity risk owners will deploy countermeasures (technologies or procedures) to manage risks to their services or systems. In some cases, those countermeasures will produce unintended consequences, which must then be addressed. Unintended consequences can potentially induce harm, adversely affecting user behaviour, user inclusion, or the infrastructure itself (including other services or countermeasures). Here we propose a framework for preemptively identifying unintended harms of risk countermeasures in cybersecurity.The framework identifies a series of unintended harms which go beyond technology alone, to consider the cyberphysical and sociotechnical space: displacement, insecure norms, additional costs, misuse, misclassification, amplification, and disruption. We demonstrate our framework through application to the complex,multi-stakeholder challenges associated with the prevention of cyberbullying as an applied example. Our framework aims to illuminate harmful consequences, not to paralyze decision-making, but so that potential unintended harms can be more thoroughly considered in risk management strategies. The framework can support identification and preemptive planning to identify vulnerable populations and preemptively insulate them from harm. There are opportunities to use the framework in coordinating risk management strategy across stakeholders in complex cyberphysical environments.
Security is always a trade-off. I appreciate work that examines the details of that trade-off.
Posted on June 26, 2020 at 7:00 AM •
New research: “Best Practices for IoT Security: What Does That Even Mean?” by Christopher Bellman and Paul C. van Oorschot:
Abstract: Best practices for Internet of Things (IoT) security have recently attracted considerable attention worldwide from industry and governments, while academic research has highlighted the failure of many IoT product manufacturers to follow accepted practices. We explore not the failure to follow best practices, but rather a surprising lack of understanding, and void in the literature, on what (generically) “best practice” means, independent of meaningfully identifying specific individual practices. Confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. How do best practices, good practices, and standard practices differ? Or guidelines, recommendations, and requirements? Can something be a best practice if it is not actionable? We consider categories of best practices, and how they apply over the lifecycle of IoT devices. For concreteness in our discussion, we analyze and categorize a set of 1014 IoT security best practices, recommendations, and guidelines from industrial, government, and academic sources. As one example result, we find that about 70\% of these practices or guidelines relate to early IoT device lifecycle stages, highlighting the critical position of manufacturers in addressing the security issues in question. We hope that our work provides a basis for the community to build on in order to better understand best practices, identify and reach consensus on specific practices, and then find ways to motivate relevant stakeholders to follow them.
Back in 2017, I catalogued nineteen security and privacy guideline documents for the Internet of Things. Our problem right now isn’t that we don’t know how to secure these devices, it’s that there is no economic or regulatory incentive to do so.
Posted on June 25, 2020 at 7:09 AM •
Really interesting research: “An examination of the cryptocurrency pump and dump ecosystem“:
Abstract: The surge of interest in cryptocurrencies has been accompanied by a proliferation of fraud. This paper examines pump and dump schemes. The recent explosion of nearly 2,000 cryptocurrencies in an unregulated environment has expanded the scope for abuse. We quantify the scope of cryptocurrency pump and dump schemes on Discord and Telegram, two popular group-messaging platforms. We joined all relevant Telegram and Discord groups/channels and identified thousands of different pumps. Our findings provide the first measure of the scope of such pumps and empirically document important properties of this ecosystem.
Posted on June 24, 2020 at 6:30 AM •
New research is able to recover sound waves in a room by observing minute changes in the room’s light bulbs. This technique works from a distance, even from a building across the street through a window.
In an experiment using three different telescopes with different lens diameters from a distance of 25 meters (a little over 82 feet) the researchers were successfully able to capture sound being played in a remote room, including The Beatles’ Let It Be, which was distinguishable enough for Shazam to recognize it, and a speech from President Trump that Google’s speech recognition API could successfully transcribe. With more powerful telescopes and a more sensitive analog-to-digital converter, the researchers believe the eavesdropping distances could be even greater.
It’s not expensive: less than $1,000 worth of equipment is required. And unlike other techniques like bouncing a laser off the window and measuring the vibrations, it’s completely passive.
Posted on June 16, 2020 at 10:20 AM •
New research on using specially crafted inputs to slow down machine-learning neural network systems:
Sponge Examples: Energy-Latency Attacks on Neural Networks shows how to find adversarial examples that cause a DNN to burn more energy, take more time, or both. They affect a wide range of DNN applications, from image recognition to natural language processing (NLP). Adversaries might use these examples for all sorts of mischief — from draining mobile phone batteries, though degrading the machine-vision systems on which self-driving cars rely, to jamming cognitive radar.
So far, our most spectacular results are against NLP systems. By feeding them confusing inputs we can slow them down over 100 times. There are already examples in the real world where people pause or stumble when asked hard questions but we now have a dependable method for generating such examples automatically and at scale. We can also neutralize the performance improvements of accelerators for computer vision tasks, and make them operate on their worst case performance.
Posted on June 10, 2020 at 6:31 AM •
New research: “Security Analysis of the Democracy Live Online Voting System“:
Abstract: Democracy Live’s OmniBallot platform is a web-based system for blank ballot delivery, ballot marking, and (optionally) online voting. Three states — Delaware, West Virginia, and New Jersey — recently announced that they will allow certain voters to cast votes online using OmniBallot, but, despite the well established risks of Internet voting, the system has never been the subject of a public, independent security review.
EDITED TO ADD: This post has been translated into Portuguese.
Posted on June 9, 2020 at 6:26 AM •
I just published a new paper with Karen Levy of Cornell: “Privacy Threats in Intimate Relationships.”
Abstract: This article provides an overview of intimate threats: a class of privacy threats that can arise within our families, romantic partnerships, close friendships, and caregiving relationships. Many common assumptions about privacy are upended in the context of these relationships, and many otherwise effective protective measures fail when applied to intimate threats. Those closest to us know the answers to our secret questions, have access to our devices, and can exercise coercive power over us. We survey a range of intimate relationships and describe their common features. Based on these features, we explore implications for both technical privacy design and policy, and offer design recommendations for ameliorating intimate privacy risks.
This is an important issue that has gotten much too little attention in the cybersecurity community.
Posted on June 5, 2020 at 6:13 AM •
This study shows that most people don’t change their passwords after a breach, and if they do they change it to a weaker password.
Abstract: To protect against misuse of passwords compromised in a breach, consumers should promptly change affected passwords and any similar passwords on other accounts. Ideally, affected companies should strongly encourage this behavior and have mechanisms in place to mitigate harm. In order to make recommendations to companies about how to help their users perform these and other security-enhancing actions after breaches, we must first have some understanding of the current effectiveness of companies’ post-breach practices. To study the effectiveness of password-related breach notifications and practices enforced after a breach, we examine — based on real-world password data from 249 participants — whether and how constructively participants changed their passwords after a breach announcement.
Of the 249 participants, 63 had accounts on breached domains;only 33% of the 63 changed their passwords and only 13% (of 63)did so within three months of the announcement. New passwords were on average 1.3× stronger than old passwords (when comparing log10-transformed strength), though most were weaker or of equal strength. Concerningly, new passwords were overall more similar to participants’ other passwords, and participants rarely changed passwords on other sites even when these were the same or similar to their password on the breached domain.Our results highlight the need for more rigorous password-changing requirements following a breach and more effective breach notifications that deliver comprehensive advice.
EDITED TO ADD (6/2): Another news aricle. Slashdot thread.
EDITED TO ADD (7/1): This entry has been translated into Spanish.
Posted on June 1, 2020 at 6:08 AM •
This paper describes a SIGINT and code-breaking alliance between Denmark, Sweden, Germany, the Netherlands and France called Maximator:
Abstract: This article is first to report on the secret European five-partner sigint alliance Maximator that started in the late 1970s. It discloses the name Maximator and provides documentary evidence. The five members of this European alliance are Denmark, Sweden, Germany, the Netherlands, and France. The cooperation involves both signals analysis and crypto analysis. The Maximator alliance has remained secret for almost fifty years, in contrast to its Anglo-Saxon Five-Eyes counterpart. The existence of this European sigint alliance gives a novel perspective on western sigint collaborations in the late twentieth century. The article explains and illustrates, with relatively much attention for the cryptographic details, how the five Maximator participants strengthened their effectiveness via the information about rigged cryptographic devices that its German partner provided, via the joint U.S.-German ownership and control of the Swiss producer Crypto AG of cryptographic devices.
Posted on May 4, 2020 at 6:42 AM •
MIT researchers have built a system that fools natural-language processing systems by swapping words with synonyms:
The software, developed by a team at MIT, looks for the words in a sentence that are most important to an NLP classifier and replaces them with a synonym that a human would find natural. For example, changing the sentence “The characters, cast in impossibly contrived situations, are totally estranged from reality” to “The characters, cast in impossibly engineered circumstances, are fully estranged from reality” makes no real difference to how we read it. But the tweaks made an AI interpret the sentences completely differently.
The results of this adversarial machine learning attack are impressive:
For example, Google’s powerful BERT neural net was worse by a factor of five to seven at identifying whether reviews on Yelp were positive or negative.
Abstract: Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present TextFooler, a simple but strong baseline to generate natural adversarial text. By applying it to two fundamental natural language tasks, text classification and textual entailment, we successfully attacked three target models, including the powerful pre-trained BERT, and the widely used convolutional and recurrent neural networks. We demonstrate the advantages of this framework in three ways: (1) effective — it outperforms state-of-the-art attacks in terms of success rate and perturbation rate, (2) utility-preserving — it preserves semantic content and grammaticality, and remains correctly classified by humans, and (3) efficient — it generates adversarial text with computational complexity linear to the text length.
EDITED TO ADD: This post has been translated into Spanish.
Posted on April 28, 2020 at 10:38 AM •
Sidebar photo of Bruce Schneier by Joe MacInnis.