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Resource constraints in smart devices demand an efficient cryptosystem that allows for low power and memory consumption. This has led to popularity of comparatively efficient Elliptic curve cryptog-raphy (ECC). Prior to this paper, much of…
One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. These flaws are highly likely ex-ploited and lead to system compromise, data leakage, or denial of…
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of DNNs expands, protecting user input privacy has become…
In the recent past, we have witnessed the shift towards attacks on the microarchitectural CPU level. In particular, cache side-channels play a predominant role as they allow an attacker to exfiltrate secret information by exploiting the CPU…
A Distributed Denial-of-service (DDoS) attack is a malicious attempt to disrupt the regular traffic of a targeted server, service, or network by sending a flood of traffic to overwhelm the target or its surrounding infrastructure. As…
Side-channel attacks exploit variations in non-functional behaviors to expose sensitive information across security boundaries. Existing methods leverage side-channels based on power consumption, electromagnetic radiation, silicon substrate…
Industrial Control Systems are under increased scrutiny. Their security is historically sub-par, and although measures are being taken by the manufacturers to remedy this, the large installed base of legacy systems cannot easily be updated…
Model extraction attacks have been widely applied, which can normally be used to recover confidential parameters of neural networks for multiple layers. Recently, side-channel analysis of neural networks allows parameter extraction even for…
Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the…
The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which…
Accelerators used for machine learning (ML) inference provide great performance benefits over CPUs. Securing confidential model in inference against off-chip side-channel attacks is critical in harnessing the performance advantage in…
Side-channel attacks (SCAs), which infer secret information (for example secret keys) by exploiting information that leaks from the implementation (such as power consumption), have been shown to be a non-negligible threat to modern…
Detection and quantification of information leaks through timing side channels are important to guarantee confidentiality. Although static analysis remains the prevalent approach for detecting timing side channels, it is computationally…
In this work we discuss the resistance of atomic pattern algorithms for elliptic curve point multiplication against simple side channel analysis attacks using our own implementation as an example. The idea of the atomicity principle is to…
Side-channel attacks are a security exploit that take advantage of information leakage. They use measurement and analysis of physical parameters to reverse engineer and extract secrets from a system. Power analysis attacks in particular,…
Contemporary computing employs cache hierarchy to fill the speed gap between processors and main memories. In order to optimise system performance, Last Level Caches(LLC) are shared among all the cores. Cache sharing has made them an…
Side-Channel Attacks (SCAs) are a powerful method to attack implementations of cryptographic algorithms. State-of-the-art techniques such as template attacks and stochastic models usually require a lot of manual preprocessing and feature…
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of…
Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and…
Attestation is a strong tool to verify the integrity of an untrusted system. However, in recent years, different attacks have appeared that are able to mislead the attestation process with treacherous practices as memory copy, proxy, and…