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Using Machine Learning to Detect IP Hijacking

This is interesting research:

In a BGP hijack, a malicious actor convinces nearby networks that the best path to reach a specific IP address is through their network. That’s unfortunately not very hard to do, since BGP itself doesn’t have any security procedures for validating that a message is actually coming from the place it says it’s coming from.

[…]

To better pinpoint serial attacks, the group first pulled data from several years’ worth of network operator mailing lists, as well as historical BGP data taken every five minutes from the global routing table. From that, they observed particular qualities of malicious actors and then trained a machine-learning model to automatically identify such behaviors.

The system flagged networks that had several key characteristics, particularly with respect to the nature of the specific blocks of IP addresses they use:

  • Volatile changes in activity: Hijackers’ address blocks seem to disappear much faster than those of legitimate networks. The average duration of a flagged network’s prefix was under 50 days, compared to almost two years for legitimate networks.

  • Multiple address blocks: Serial hijackers tend to advertise many more blocks of IP addresses, also known as “network prefixes.”

  • IP addresses in multiple countries: Most networks don’t have foreign IP addresses. In contrast, for the networks that serial hijackers advertised that they had, they were much more likely to be registered in different countries and continents.

Note that this is much more likely to detect criminal attacks than nation-state activities. But it’s still good work.

Academic paper.

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Factoring 2048-bit Numbers Using 20 Million Qubits

This theoretical paper shows how to factor 2048-bit RSA moduli with a 20-million qubit quantum computer in eight hours. It’s interesting work, but I don’t want overstate the risk.

We know from Shor’s Algorithm that both factoring and discrete logs are easy to solve on a large, working quantum computer. Both of those are currently beyond our technological abilities. We barely have quantum computers with 50 to 100 qubits. Extending this requires advances not only in the number of qubits we can work with, but in making the system stable enough to read any answers. You’ll hear this called “error rate” or “coherence” — this paper talks about “noise.”

Advances are hard. At this point, we don’t know if they’re “send a man to the moon” hard or “faster-than-light travel” hard. If I were guessing, I would say they’re the former, but still harder than we can accomplish with our current understanding of physics and technology.

I write about all this generally, and in detail, here. (Short summary: Our work on quantum-resistant algorithms is outpacing our work on quantum computers, so we’ll be fine in the short run. But future theoretical work on quantum computing could easily change what “quantum resistant” means, so it’s possible that public-key cryptography will simply not be possible in the long run. That’s not terrible, though; we have a lot of good scalable secret-key systems that do much the same things.)

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More Cryptanalysis of Solitaire

In 1999, I invented the Solitaire encryption algorithm, designed to manually encrypt data using a deck of cards. It was written into the plot of Neal Stephenson’s novel Cryptonomicon, and I even wrote an afterward to the book describing the cipher.

I don’t talk about it much, mostly because I made a dumb mistake that resulted in the algorithm not being reversible. Still, for the short message lengths you’re likely to use a manual cipher for, it’s still secure and will likely remain secure.

Here’s some new cryptanalysis:

Abstract: The Solitaire cipher was designed by Bruce Schneier as a plot point in the novel Cryptonomicon by Neal Stephenson. The cipher is intended to fit the archetype of a modern stream cipher whilst being implementable by hand using a standard deck of cards with two jokers. We find a model for repetitions in the keystream in the stream cipher Solitaire that accounts for the large majority of the repetition bias. Other phenomena merit further investigation. We have proposed modifications to the cipher that would reduce the repetition bias, but at the cost of increasing the complexity of the cipher (probably beyond the goal of allowing manual implementation). We have argued that the state update function is unlikely to lead to cycles significantly shorter than those of a random bijection.

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On Cybersecurity Insurance

Good paper on cybersecurity insurance: both the history and the promise for the future. From the conclusion:

Policy makers have long held high hopes for cyber insurance as a tool for improving security. Unfortunately, the available evidence so far should give policymakers pause. Cyber insurance appears to be a weak form of governance at present. Insurers writing cyber insurance focus more on organisational procedures than technical controls, rarely include basic security procedures in contracts, and offer discounts that only offer a marginal incentive to invest in security. However, the cost of external response services is covered, which suggests insurers believe ex-post responses to be more effective than ex-ante mitigation. (Alternatively, they can more easily translate the costs associated with ex-post responses into manageable claims.)

The private governance role of cyber insurance is limited by market dynamics. Competitive pressures drive a race-to-the-bottom in risk assessment standards and prevent insurers including security procedures in contracts. Policy interventions, such as minimum risk assessment standards, could solve this collective action problem. Policy-holders and brokers could also drive this change by looking to insurers who conduct rigorous assessments. Doing otherwise ensures adverse selection and moral hazard will increase costs for firms with responsible security postures. Moving toward standardised risk assessment via proposal forms or external scans supports the actuarial base in the long-term. But there is a danger policyholders will succumb to Goodhart’s law by internalising these metrics and optimising the metric rather than minimising risk. This is particularly likely given these assessments are constructed by private actors with their own incentives. Search-light effects may drive the scores towards being based on what can be measured, not what is important.

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Attacking the Intel Secure Enclave

Interesting paper by Michael Schwarz, Samuel Weiser, Daniel Gruss. The upshot is that both Intel and AMD have assumed that trusted enclaves will run only trustworthy code. Of course, that’s not true. And there are no security mechanisms that can deal with malicious enclaves, because the designers couldn’t imagine that they would be necessary. The results are predictable.

The paper: “Practical Enclave Malware with Intel SGX.”

Abstract: Modern CPU architectures offer strong isolation guarantees towards user applications in the form of enclaves. For instance, Intel’s threat model for SGX assumes fully trusted enclaves, yet there is an ongoing debate on whether this threat model is realistic. In particular, it is unclear to what extent enclave malware could harm a system. In this work, we practically demonstrate the first enclave malware which fully and stealthily impersonates its host application. Together with poorly-deployed application isolation on personal computers, such malware can not only steal or encrypt documents for extortion, but also act on the user’s behalf, e.g., sending phishing emails or mounting denial-of-service attacks. Our SGX-ROP attack uses new TSX-based memory-disclosure primitive and a write-anything-anywhere primitive to construct a code-reuse attack from within an enclave which is then inadvertently executed by the host application. With SGX-ROP, we bypass ASLR, stack canaries, and address sanitizer. We demonstrate that instead of protecting users from harm, SGX currently poses a security threat, facilitating so-called super-malware with ready-to-hit exploits. With our results, we seek to demystify the enclave malware threat and lay solid ground for future research on and defense against enclave malware.

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AI Emotion-Detection Arms Race

Voice systems are increasingly using AI techniques to determine emotion. A new paper describes an AI-based countermeasure to mask emotion in spoken words.

Their method for masking emotion involves collecting speech, analyzing it, and extracting emotional features from the raw signal. Next, an AI program trains on this signal and replaces the emotional indicators in speech, flattening them. Finally, a voice synthesizer re-generates the normalized speech using the AIs outputs, which gets sent to the cloud. The researchers say that this method reduced emotional identification by 96 percent in an experiment, although speech recognition accuracy decreased, with a word error rate of 35 percent.

Academic paper.

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How Privacy Laws Hurt Defendants

Rebecca Wexler has an interesting op-ed about an inadvertent harm that privacy laws can cause: while law enforcement can often access third-party data to aid in prosecution, the accused don’t have the same level of access to aid in their defense:

The proposed privacy laws would make this situation worse. Lawmakers may not have set out to make the criminal process even more unfair, but the unjust result is not surprising. When lawmakers propose privacy bills to protect sensitive information, law enforcement agencies lobby for exceptions so they can continue to access the information. Few lobby for the accused to have similar rights. Just as the privacy interests of poor, minority and heavily policed communities are often ignored in the lawmaking process, so too are the interests of criminal defendants, many from those same communities.

In criminal cases, both the prosecution and the accused have a right to subpoena evidence so that juries can hear both sides of the case. The new privacy bills need to ensure that law enforcement and defense investigators operate under the same rules when they subpoena digital data. If lawmakers believe otherwise, they should have to explain and justify that view.

For more detail, see her paper.

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Another Attack Against Driverless Cars

In this piece of research, attackers successfully attack a driverless car system — Renault Captur’s “Level 0” autopilot (Level 0 systems advise human drivers but do not directly operate cars) — by following them with drones that project images of fake road signs in 100ms bursts. The time is too short for human perception, but long enough to fool the autopilot’s sensors.

Boing Boing post.

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