Side-channel analysis (SCA) is a serious threat to embedded cryptography. Any SCA has two important components: leakage modeling and distinguisher. Although distinguisher received much research efforts, still lies on couple of classical techniques like Hamming weight or linear regression. In this paper, we propose novel support vector machine based technique for efficient modeling. The called regression (SVR) can be used in both profiled non-profiled settings. We provide proper theoretical...

As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults attacks become a tremendous concern, which potentially could lead to catastrophic consequences. In this paper, we initiate the first study of leveraging physical fault injection on Deep Neural Networks (DNNs), by using laser technique embedded systems. particular, our exploratory targets four used activation functions DNNs...

Modern and future substations are aimed to be more interconnected, leveraging communication standards like IEC 61850-9-2, associated abstract data models services generic object oriented substation event, manufacturing message specification, sampled measured value. Such interconnection would enable fast secure transfer, sharing of the analytics information for various purposes wide area monitoring, faster outage recovery, blackout prevention, distributed state estimation, etc. This require...

Profiled side-channel attacks represent a practical threat to digital devices, thereby having the potential disrupt foundation of e-commerce, Internet Things (IoT), and smart cities.In profiled attack, adversary gains knowledge about target device by getting access cloned device.Though these two devices are different in realworld scenarios, yet, unfortunately, large part research works simplifies setting using only single for both profiling attacking.There, portability issue is conveniently...

Right from its introduction, fault attacks (FA) have been established to be one of the most practical threats both public key and symmetric based cryptosystems. Statistical Ineffective Fault Analysis (SIFA) is a recently proposed class introduced at CHES 2018. The fascinating feature this attack that it exploits correct ciphertexts obtained during injection campaign, instead faulty ciphertexts. SIFA has shown bypass almost all existing countermeasures even when they are combined with masking...

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of device during computation, resulting in a value that is currently being computed. They can realized by various techniques, ranging from clock/voltage glitching application lasers rowhammer. Previous works mostly explored for output misclassification, thus affecting reliability neural networks. In this article, we investigate possibility reverse engineer with Sign...

Laser fault injection is one of the strongest techniques. It offers a precise area positioning and timing, allowing high repeatability experiments.

Over the years, deep learning algorithms have advanced a lot and any innovation in are demonstrated benchmarked for image classification. Several other field including side-channel analysis (SCA) recently adopted with great success. In SCA, typically working 1-dimensional (1-D) data. this work, we propose unique method to improve based by converting measurements from raw-trace of 1-dimension data on float or byte into picture-formatted trace that has information position. We demonstrate why...

Side Channel Attack (SCA) exploits the physical information leakage (such as electromagnetic emanation) from a device that performs some cryptographic operation and poses serious threat in present IoT era. In last couple of decades, there have been large body research works dedicated to streamlining/improving attacks or suggesting novel countermeasures thwart those attacks. However, closer inspection reveals vast majority published context symmetric key cryptography is block ciphers (or...

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To end, consider multilayer perceptron as machine architecture choice and assume non-invasive eavesdropping attacker capable measuring passive leakages like consumption, electromagnetic radiation, reaction time. We conduct all experiments on real data...

Hardware Trojan (HT) has recently drawn much attention in both industry and academia due to the global outsourcing trend semiconductor manufacturing, where a malicious logic can be inserted into security critical ICs at almost any stages. HT severity mainly stems from its low-cost stealthy nature only functions strict condition purposely alter or physical behavior for leaking secrets. This fact makes detection very challenging practice. In this paper, we propose novel technique based on...

The use of deep learning-based side-channel analysis is an effective way performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research papers have reported successful results, they typically focus attacking a single device, assuming that leakages are similar between devices the same type. However, this assumption not always realistic due to variations in hardware measurement setups, creating what known as...

In an effort to circumvent the high cost of standard countermeasures against side-channel attacks in post-quantum cryptography, some works have developed low-cost detection-based countermeasures. These try detect maliciously generated input ciphertexts and react them by discarding ciphertext or secret key. this work, we take a look at two previously proposed countermeasures: sanity check decapsulation failure check, demonstrate successful on these schemes. We show that first countermeasure...

Characterizing the fault space of a cipher to filter out set faults potentially exploitable for attacks (FA), is problem with immense practical value.A quantitative knowledge desirable in several applications, like security evaluation, construction and implementation, design, testing countermeasures etc.In this work, we investigate context block ciphers.The formidable size mandates use an automation strategy solve problem, which should be able characterize each individual instance quickly.On...

Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk misclassification can lead to serious consequences. It necessary know which extent are Neural Networks (DNNs) robust against various types adversarial conditions. In this paper, we experimentally evaluate DNNs implemented embedded device by using laser fault injection, physical attack technique that mostly used security and reliability...

Deep learning approaches have become popular for Side-Channel Analysis (SCA) in the recent years. Especially Convolutional Neural Networks (CNN) due to their natural ability overcome jitter-based as well masking countermeasures. Most of works been focusing on optimising performance given dataset, example finding optimal architecture and using ensemble, bypass need trace pre-processing. However, pre-processing is a long studied topic several proven techniques exist literature. There no...

Recent work has shown that Side-Channel Attacks (SCA) and Fault (FA) can be combined, forming an extremely powerful adversarial model, which bypass even some strongest protections against both FA SCA. However, such form of combined attack comes with practical challenges - 1) a profiled setting multiple fault locations is needed; 2) models are restricted to single-bit set-reset/flips; 3) the input needs repeated several times. In this paper, we propose new strategy called SCA-NFA works in...

As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults attacks become a tremendous concern, which potentially could lead to catastrophic consequences. In this paper, we initiate the first study of leveraging physical fault injection on Deep Neural Networks (DNNs), by using laser technique embedded systems. particular, our exploratory targets four used activation functions DNNs...

We present the first practically realizable side-channel assisted fault attack on any block-ciphers having bit-permutation with optimal diffusion, that can retrieve round key efficiently using random nibble faults. The demonstrates how leakage allow adversary to precisely determine mask resulting from a injection instance. demonstrate viability of such model via analysis experiments top laser-based setup, targeting PRESENT-80 and GIFT-128 (two popular based diffusion) implementation an...