Entries Tagged "academic papers"

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Decrypting Hive Ransomware Data

Nice piece of research:

Abstract: Among the many types of malicious codes, ransomware poses a major threat. Ransomware encrypts data and demands a ransom in exchange for decryption. As data recovery is impossible if the encryption key is not obtained, some companies suffer from considerable damage, such as the payment of huge amounts of money or the loss of important data. In this paper, we analyzed Hive ransomware, which appeared in June 2021. Hive ransomware has caused immense harm, leading the FBI to issue an alert about it. To minimize the damage caused by Hive Ransomware and to help victims recover their files, we analyzed Hive Ransomware and studied recovery methods. By analyzing the encryption process of Hive ransomware, we confirmed that vulnerabilities exist by using their own encryption algorithm. We have recovered the master key for generating the file encryption key partially, to enable the decryption of data encrypted by Hive ransomware. We recovered 95% of the master key without the attacker’s RSA private key and decrypted the actual infected data. To the best of our knowledge, this is the first successful attempt at decrypting Hive ransomware. It is expected that our method can be used to reduce the damage caused by Hive ransomware.

Here’s the flaw:

The cryptographic vulnerability identified by the researchers concerns the mechanism by which the master keys are generated and stored, with the ransomware strain only encrypting select portions of the file as opposed to the entire contents using two keystreams derived from the master key.

The encryption keystream, which is created from an XOR operation of the two keystreams, is then XORed with the data in alternate blocks to generate the encrypted file. But this technique also makes it possible to guess the keystreams and restore the master key, in turn enabling the decode of encrypted files sans the attacker’s private key.

The researchers said that they were able to weaponize the flaw to devise a method to reliably recover more than 95% of the keys employed during encryption.

Posted on March 1, 2022 at 6:06 AMView Comments

Breaking 256-bit Elliptic Curve Encryption with a Quantum Computer

Researchers have calculated the quantum computer size necessary to break 256-bit elliptic curve public-key cryptography:

Finally, we calculate the number of physical qubits required to break the 256-bit elliptic curve encryption of keys in the Bitcoin network within the small available time frame in which it would actually pose a threat to do so. It would require 317 × 106 physical qubits to break the encryption within one hour using the surface code, a code cycle time of 1 μs, a reaction time of 10 μs, and a physical gate error of 10-3. To instead break the encryption within one day, it would require 13 × 106 physical qubits.

In other words: no time soon. Not even remotely soon. IBM’s largest ever superconducting quantum computer is 127 physical qubits.

Posted on February 9, 2022 at 6:25 AMView Comments

Using EM Waves to Detect Malware

I don’t even know what I think about this. Researchers have developed a malware detection system that uses EM waves: “Obfuscation Revealed: Leveraging Electromagnetic Signals for Obfuscated Malware Classification.”

Abstract: The Internet of Things (IoT) is constituted of devices that are exponentially growing in number and in complexity. They use numerous customized firmware and hardware, without taking into consideration security issues, which make them a target for cybercriminals, especially malware authors.

We will present a novel approach of using side channel information to identify the kinds of threats that are targeting the device. Using our approach, a malware analyst is able to obtain precise knowledge about malware type and identity, even in the presence of obfuscation techniques which may prevent static or symbolic binary analysis. We recorded 100,000 measurement traces from an IoT device infected by various in-the-wild malware samples and realistic benign activity. Our method does not require any modification on the target device. Thus, it can be deployed independently from the resources available without any overhead. Moreover, our approach has the advantage that it can hardly be detected and evaded by the malware authors. In our experiments, we were able to predict three generic malware types (and one benign class) with an accuracy of 99.82%. Even more, our results show that we are able to classify altered malware samples with unseen obfuscation techniques during the training phase, and to determine what kind of obfuscations were applied to the binary, which makes our approach particularly useful for malware analysts.

This seems impossible. It’s research, not a commercial product. But it’s fascinating if true.

Posted on January 14, 2022 at 6:13 AMView Comments

Friday Squid Blogging: Deep-Dwelling Squid

We have discovered a squid — (Oegopsida, Magnapinnidae, Magnapinna sp.) — that lives at 6,000 meters deep.

:They’re really weird,” says Vecchione. “They drift along with their arms spread out and these really long, skinny, spaghetti-like extensions dangling down underneath them.” Microscopic suckers on those filaments enable the squid to capture their prey.

But the squid that Jamieson and Vecchione saw in the footage captured 6,212 meters below the ocean’s surface is a small one. They estimate that its mantle measured 10 centimeters long — ­about a third the size of the largest-known magnapinnid. And the characteristically long extensions observed on other magnapinnids were nowhere to be seen in the video. That could mean, says Vecchione, that this bigfin squid was a juvenile.

Research paper.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Posted on December 31, 2021 at 4:03 PMView Comments

Hiding Vulnerabilities in Source Code

Really interesting research demonstrating how to hide vulnerabilities in source code by manipulating how Unicode text is displayed. It’s really clever, and not the sort of attack one would normally think about.

From Ross Anderson’s blog:

We have discovered ways of manipulating the encoding of source code files so that human viewers and compilers see different logic. One particularly pernicious method uses Unicode directionality override characters to display code as an anagram of its true logic. We’ve verified that this attack works against C, C++, C#, JavaScript, Java, Rust, Go, and Python, and suspect that it will work against most other modern languages.

This potentially devastating attack is tracked as CVE-2021-42574, while a related attack that uses homoglyphs –- visually similar characters –- is tracked as CVE-2021-42694. This work has been under embargo for a 99-day period, giving time for a major coordinated disclosure effort in which many compilers, interpreters, code editors, and repositories have implemented defenses.

Website for the attack. Rust security advisory.

Brian Krebs has a blog post.

EDITED TO ADD (11/12): An older paper on similar issues.

Posted on November 1, 2021 at 10:58 AMView Comments

Security Risks of Client-Side Scanning

Even before Apple made its announcement, law enforcement shifted their battle for backdoors to client-side scanning. The idea is that they wouldn’t touch the cryptography, but instead eavesdrop on communications and systems before encryption or after decryption. It’s not a cryptographic backdoor, but it’s still a backdoor — and brings with it all the insecurities of a backdoor.

I’m part of a group of cryptographers that has just published a paper discussing the security risks of such a system. (It’s substantially the same group that wrote a similar paper about key escrow in 1997, and other “exceptional access” proposals in 2015. We seem to have to do this every decade or so.) In our paper, we examine both the efficacy of such a system and its potential security failures, and conclude that it’s a really bad idea.

We had been working on the paper well before Apple’s announcement. And while we do talk about Apple’s system, our focus is really on the idea in general.

Ross Anderson wrote a blog post on the paper. (It’s always great when Ross writes something. It means I don’t have to.) So did Susan Landau. And there’s press coverage in the New York Times, the Guardian, Computer Weekly, the Financial Times, Forbes, El Pais (English translation), NRK (English translation), and — this is the best article of them all — the Register. See also this analysis of the law and politics of client-side scanning from last year.

Posted on October 15, 2021 at 9:30 AMView Comments

Recovering Real Faces from Face-Generation ML System

New paper: “This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces.

Abstract: Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website http://thispersondoesnotexist.com, taunts users with GAN generated images that seem too real to believe. On the other hand, GANs do leak information about their training data, as evidenced by membership attacks recently demonstrated in the literature. In this work, we challenge the assumption that GAN faces really are novel creations, by constructing a successful membership attack of a new kind. Unlike previous works, our attack can accurately discern samples sharing the same identity as training samples without being the same samples. We demonstrate the interest of our attack across several popular face datasets and GAN training procedures. Notably, we show that even in the presence of significant dataset diversity, an over represented person can pose a privacy concern.

News article. Slashdot post.

Posted on October 14, 2021 at 9:56 AMView Comments

Identifying Computer-Generated Faces

It’s the eyes:

The researchers note that in many cases, users can simply zoom in on the eyes of a person they suspect may not be real to spot the pupil irregularities. They also note that it would not be difficult to write software to spot such errors and for social media sites to use it to remove such content. Unfortunately, they also note that now that such irregularities have been identified, the people creating the fake pictures can simply add a feature to ensure the roundness of pupils.

And the arms race continues….

Research paper.

Posted on September 15, 2021 at 10:31 AMView Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.