Lifeboat Foundation

Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers—a crucial step for making these devices practical—using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections.

The research is published in the journal Physical Review Letters.

In contrast to classical computers, which operate on bits that can only take the basic values 0 and 1, quantum computers operate on “qubits”, which can assume any superposition of the computational basis states. In combination with quantum entanglement, another quantum characteristic that connects different qubits beyond classical means, this enables quantum computers to perform entirely new operations, giving rise to potential advantages in some computational tasks, such as large-scale searches, optimization problems, and cryptography.

Perovskite light-emitting diode is used as the light source for a quantum random number generator used in encryption.

Encryption plays an important role in protecting information in this digital era, and a random number generator plays a vital part in this by providing keys that are used to both encrypt and unlock the information at the receiving end.

Now, a team of researchers has made use of light-emitting diodes made from the crystal-like material perovskite to devise a new type of Quantum Random Number Generator (QRNG) that can be used for encryption but also for betting and computer simulations.

O.o!!!

Polymorphic malware leverages an encryption key to alter its shape, signature, and behavioral pattern. Using a mutation engine and a self-propagated code strain, it encrypts its code and changes how physical files are created. Many traditional cybersecurity solutions that rely on signature-based detection—a technique in which security systems identify a malware based on its known characteristics—fail to recognize or detect polymorphic threats.

A polymorphic attack typically involves the following stages.

Continue reading “What Is Polymorphic Malware?” »

Digital information exchange can be safer, cheaper and more environmentally friendly with the help of a new type of random number generator for encryption developed at Linköping University, Sweden. The researchers behind the study believe that the new technology paves the way for a new type of quantum communication.

In an increasingly connected world, cybersecurity is becoming increasingly important to protect not just the individual, but also, for example, national infrastructure and banking systems. And there is an ongoing race between hackers and those trying to protect information. The most common way to protect information is through encryption. So when we send emails, pay bills and shop online, the information is digitally encrypted.

To encrypt information, a random number generator is used, which can either be a computer program or the hardware itself. The random number generator provides keys that are used to both encrypt and unlock the information at the receiving end.

Researchers at Los Alamos National Laboratory have successfully developed a new way to produce a specific type of photon that could prove critical for quantum data exchange, notably encryption. The specific kind of photons, called “circularly polarized light,” have thus far proved challenging to create and control, but this new technique makes the process easier and, importantly, cheaper. This was achieved, the team explains, by stacking two different, atomically thin materials to “twist” (polarize) photons in a predictable fashion.

Encoded, “twisted,” photons

Continue reading “New technique opens door for encoding data on single photons” »

• Encryption and segmentation: These operate on the assumption some fraction of the network is already compromised. Restricting the reach and utility of any captured data and accessible networks will mitigate the damage even on breached systems.

• SBOM documentation: Regulatory compliance can be driven by industry organizations and the government, but it will take time to establish standards. SBOM documentation is an essential foundation for best practices.

If “democracy dies in darkness,” and that includes lies of omission in reporting, then cybersecurity suffers the same fate with backdoors. The corollary is “don’t roll your own crypto” even if well-intentioned. The arguments for weakening encryption to make law enforcement easier falls demonstrably flat, with TETRA just the latest example. Secrets rarely stay that way forever, and sensitive data is more remotely accessible than at any time in history. Privacy and global security affect us all, and the existence of these single points of failure in our cybersecurity efforts are unsustainable and will have unforeseeable consequences. We need to innovate and evolve the internet away from this model to have durable security assurances.

The term “virtual private network,” or VPN for short, has become almost synonymous with “online privacy and security.” VPNs function by creating an encrypted tunnel through which your data may transit as it moves over the internet. They are designed to protect your privacy and make it impossible for anyone to monitor or access your activity while you are online. But what happens if the same instrument that was supposed to keep your privacy safe turns out to be a conduit for attacks? Introduce yourself to “TunnelCrack,” a frightening discovery that has sent shockwaves across the world of cybersecurity. Nian Xue from New York University, Yashaswi Malla and Zihang Xia from New York University Abu Dhabi, Christina Popper from New York University, and Mathy Vanhoef from KU Leuven University were the ones that carried out the study.

Two serious vulnerabilities in virtual private networks (VPNs) have been discovered by a research team. These vulnerabilities had been dormant since 1996. It is possible to leak and read user traffic, steal information, or even conduct attacks on user devices by exploiting these vulnerabilities, which are present in practically every VPN product across all platforms. TunnelCrack is a combination of two common security flaws found in virtual private networks (VPNs). Even though a virtual private network (VPN) is designed to safeguard all of the data that a user sends, these attacks are able to circumvent this security. An enemy, for example, may take advantage of the security flaws to steal information from users, read their communications, attack their devices, or even just spill it all. Regardless of the security protocol that is utilized by the VPN, the uncovered flaws may be exploited and used maliciously.

An interdisciplinary team of mathematicians, engineers, physicists, and medical scientists have uncovered an unexpected link between pure mathematics and genetics, that reveals key insights into the structure of neutral mutations and the evolution of organisms.

Number theory, the study of the properties of positive integers, is perhaps the purest form of mathematics. At first sight, it may seem far too abstract to apply to the natural world. In fact, the influential American number theorist Leonard Dickson wrote ‘Thank God that number theory is unsullied by any application.’

And yet, again and again, number theory finds unexpected applications in science and engineering, from leaf angles that (almost) universally follow the Fibonacci sequence, to modern encryption techniques based on factoring prime numbers. Now, researchers have demonstrated an unexpected link between number theory and evolutionary genetics.

Through a scattering medium such as ground glass? Traditionally, this would be considered impossible. When light passes through an opaque substance, the information carried within the light becomes “jumbled up”, almost as if undergoes complex encryption.

Recently, a remarkable scientific breakthrough by Professor Choi Wonshik’s team from the IBS Center for Molecular Spectroscopy and Dynamics (IBS CMSD) has unveiled a method to leverage this phenomenon in the fields of optical computing and machine learning.

Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.