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Internet of Things

Introduction to IoT Security
IoT devices bridge between the virtual and physical world. Unlike general-purpose computers, they are networked computing devices with narrowly defined physical functionality primarily based on sensors and actuators. Main challenges IoT devices are facing globally include security threats, data privacy as primary issues and compliance requirement, third party data requests, access management and others as secondary issues. Hundreds of different IoT attacks can be broadly clustered into four major categories, as follows –

‘Ignoring the IoT functionality’ attack

Since IoT devices are relatively vulnerable entry points into customers’ domestic and/ or organizational network, this attack ignores the intended functionality the IoT device is designed to achieve.

‘Reducing IoT functionality’ attack

This attack is by degrading the performance of IoT device through Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attack.

‘Misusing the IoT functionality’ attack

This attack replaces the originally intended functionality with another functionality in an unauthorized way.

Extending the IoT functionality’ attack

This attack includes the original functionality of IoT devices and extends the functionality in order to achieve a completely diverse physical impact.
According to the National Institute of Information and Communications Technology (NiCT), a number of domestic cyberattacks have gone from 5.6 billion in 2010 to 54.5 billion in 2015. Another survey made by Varonis stated that only 5% of companies’ folders are properly protected, on average. According to the latest forecast from the Gartner survey, worldwide spending on cybersecurity is forecasted to reach approximately $133.7 billion in 2022, which was $124 billion in 2019.
 

Chain of trust and Secret Protection in IoT – Immensity of Data causes Threat

Implementation of the cryptographic suite for IoT devices relies on the ‘chain of trust’. If a single link in the chain of trust in the security system is affected, the security of the entire system will eventually collapse. ‘Knowledge is power’ is a very well-known phrase in the context of IoT. A complex system works in collaborative fashion where multiple IoT devices work interoperably to perform certain task. Consider the example of smart car manufacturers whose action is capturing data for inter-car communication, smart parking, range estimation, traffic compliance, livelock avoidance and many more. The vast amount of data shared over IoT devices keeps a breach open for theft, falsification, impersonation, hacking, ransomware, denial of service and a lot more.
 

Paradox of Randomness in Cyber-immuned IoT Infrastructure

Most of the hackers and intruders attack IoT functionality through a technique called ‘Bayesian estimation of discrete entropy’, which enables them to deduce the considerable amount of hackable information regarding system specifications and operation details from observing samples. Thus security and privacy are two major considerations and both relying on stronger encryption models which can only be obtained from a reliable source of truly random numbers. Ics used in IoT often give off ‘side-information’ like Radio-Frequency (RF) emissions, rate of variation in power consumption – which is a source of concern as after drawing statistically significant conclusions from Bayesian entropy analysis, physical devices might undergo probing and reverse engineering. This is caused due to a lack of randomness or entropy used for securing the key to encrypt data. Many Random Number Generators (RNGs) used in IoT rely on the time of day which is obtained mathematically by combining digits representing different units of seconds to produce an ‘apparently random’ seed. This is pseudo-randomness, as feeding the exact time of day to the same RNG will return the same sequence of random numbers. Thus, “unpredictability and statistical independence will never be achieved due to insufficient entropy generated and weak entropy source used.” On the contrary, random numbers generated from a true random number generator will never undergo physical, electronic and statistical attacks through observation or manipulation.
 

Quantum hype in IoT marketplace

Evolution beyond classical computing is through the quantum realm of computing in a recent state of technological flux in notable dimensions. Quantum enables envision the future to explore accelerated growth in the marketplace to impact the landscape of machine learning, artificial intelligence, and IoT to a greater extent, despite its development nascency. The following represents the status of quantum computing and its reach in industry and academia in two different time zones.
IoT incorporates controllability, readability, addressability, locatability, and recognizability of things via the internet. Operational units of IoT include devices, sensors, machines, data and interaction among them. The digital economy is currently girdled by IoT devices and IoT infrastructure, where there is amazing snowballing of data and devices. As per a forecast by Business Insider Intelligence, “by 2023 consumers, companies and governments will install 40 billion IoT devices globally.” Cloud-based IoT security is indeed a cardinal issue in growing IoT connectivity. Overcoming security hurdles and ensuring quality services of IoT implies scalability, efficiency, interoperability and responsiveness of IoT devices imposed over trillions of sensors. Securing IoT communication is performed using cryptographic algorithms currently available in the marketplace.
No wonder, security is a pre-eminent issue as encryption relying on the public-key cryptographic model is susceptible to sophisticated hacking in the near-term future using powerful quantum computers. Rqubit is able to provide a competitive advantage to IoT industries with stand out security standards.
Security vendor key factor collected 75 million RSA (most popular public-key cryptographic the algorithm, so far) digital certificates and found that at least 435,000 of these certificates were vulnerable to factoring attacks
 

Security enforcement and safety evaluation tool structure for IoT devices – Rqubit

Random Number seed safety determination Process
 

Correlation between Rqubit and IoT devices – one-stop solution for gaining hackproof IoT infrastructure

Nature is inherently random, so as quantum computing. The underlying principles of quantum physics like a superposition of quantum states to offer quantum parallelism, an entanglement of quantum states to offer interdependency of two qubits kept at a distance have made quantum computing to outperform classical mode of computing relying on algorithms, specifically mathematics. If a the cryptographic key for IoT security is generated using a mathematical random number generator, often termed as Pseudo-Random Number Generator (PRNG), they are always prone to vulnerabilities and cyber threats at least theoretically as they exhibit some pattern which can be detected on simulation over a larger period of time. Here is Rqubit with its Quantum Random Number Generator (QRNG) as a solution for a crypto suite the IoT devices are using.
  • Source of entropy of QRNG of Rqubit is through observation of quantum phenomena from a quantum computer – hence, unpredictability is guaranteed.
  • Completely unpredictable random bits.
  • Zero reliance on the deterministic seed of a classical machine – ensuring unaffected randomness or entropy amount.
  • Unbreakability of generated Rqubit numbers due to unavailability of fully entangled larger quantum hardware – hence, Rqubit is future-proof and quantum-safe.
  • Infeasibility of key copying due to the no-cloning feature of Rqubit – causing the potential barrier to unsafe key generation and easier security infiltration.