Deep Learning based Side Channel Attacks in Practice

A recent line of research has investigated a new profiling technique based on deep learning as an alternative to the well-known template attack. The advantage of this new profiling approach is twofold: $(1)$ the approximation of the information leakage by a multivariate Gaussian distribution is relaxed (leading to a more generic approach) and $(2)$ the pre-processing phases such as the traces realignment or the selection of the Points of Interest (PoI) are no longer mandatory, in some cases, to succeed the key recovery (leading to a less complex security evaluation roadmap). The related published works have demonstrated that Deep Learning based Side-Channel Attacks (DL-SCA) are very efficient when targeting cryptographic implementations protected with the common side-channel countermeasures such as masking, jitter and random delays insertion. In this paper, we assess the efficiency of this new profiling attack under different realistic and practical scenarios. First, we study the impact of the intrinsic characteristics of the manipulated data-set (\emph{i.e.} distance in time samples between the PoI, the dimensionality of the area of interest and the pre-processing of the data) on the robustness of the attack. We demonstrate that the deep learning techniques are sensitive to these parameters and we suggest some practical recommendations that can be followed to enhance the profiling and the key recovery phases. Second, we discuss the tolerance of DL-SCA with respect to a deviation from the idealized leakage models and provide a comparison with the well-known stochastic attack. Our results show that DL-SCA are still efficient in such a context. Then, we target a more complex masking scheme based on Shamir\u27s secret sharing and prove that this new profiling approach is still performing well. Finally, we conduct a security evaluation of a batch of several combinations of side-channel protections using simulations and real traces captured on the ChipWhisperer board. The experimental results obtained confirm that DL-SCA are very efficient even when a cryptographic implementation combines several side-channel countermeasures

Deep Learning based Side-Channel Attack: a New Profiling Methodology based on Multi-Label Classification

Deep Learning based Side-Channel Attacks (DL-SCA) are an emerging security assessment method increasingly being adopted by the majority of certification schemes and certification bodies to assess the resistance of cryptographic implementations. The related published investigations have demonstrated that DL-SCA are very efficient when targeting cryptographic designs protected with the common side-channel countermeasures. Furthermore, these attacks allow to streamline the evaluation process as the pre-processing of the traces (\emph{e.g.} alignment, dimensionality reduction, \dots) is no longer mandatory. In practice, the DL-SCA are applied following the divide-and-conquer strategy such that the target, for the training and the attack phases, only depends on $8$ key bits at most (to avoid high time complexity especially during the training). Then, the same process is repeated to recover the remaining bits of the key. To mitigate this practical issue, we propose in this work a new profiling methodology for DL-SCA based on the so-called multi-label classification. We argue that our new profiling methodology allows applying DL-SCA to target a bigger chunk of the key (typically $16$ bits) without introducing a learning time overhead and while guaranteeing a similar attack efficiency compared to the commonly used training strategy. As a side benefit, we demonstrate that our leaning strategy can be applied as well to train several intermediate operations at once. Interestingly, we show that, in this context, our methodology is even faster than the classical training and leads to a more efficient key recovery phase. We validated the soundness of our proposal on simulated traces and experimental data-sets; amongst them, some are publicly available side-channel databases. The obtained results have proven that our profiling methodology is of great practical interest especially in the context of performing penetration tests with high attack potential (\emph{e.g.} Common Criteria, EMVCO) where the time required to perform the attack has an impact on its final rating

Revisiting Higher-Order Computational Attacks against White-Box Implementations

White-box cryptography was first introduced by Chow et al. in $2002$ as a software technique for implementing cryptographic algorithms in a secure way that protects secret keys in an untrusted environment. Ever since, Chow et al.\u27s design has been subject to the well-known Differential Computation Analysis (DCA). To resist DCA, a natural approach that white-box designers investigated is to apply the common side-channel countermeasures such as masking. In this paper, we suggest applying the well-studied leakage detection methods to assess the security of masked white-box implementations. Then, we extend some well-known side-channel attacks (i.e. the bucketing computational analysis, the mutual information analysis, and the collision attack) to the higher-order case to defeat higher-order masked white-box implementations. To illustrate the effectiveness of these attacks, we perform a practical evaluation against a first-order masked white-box implementation. The obtained results have demonstrated the practicability of these attacks in a real-world scenario

Breaking Cryptographic Implementations Using Deep Learning Techniques

Template attack is the most common and powerful profiled side channel attack. It relies on a realistic assumption regarding the noise of the device under attack: the probability density function of the data is a multivariate Gaussian distribution. To relax this assumption, a recent line of research has investigated new profiling approaches mainly by applying machine learning techniques. The obtained results are commensurate, and in some particular cases better, compared to template attack. In this work, we propose to continue this recent line of research by applying more sophisticated profiling techniques based on deep learning. Our experimental results confirm the overwhelming advantages of the resulting new attacks when targeting both unprotected and protected cryptographic implementations

There is Wisdom in Harnessing the Strengths of your Enemy: Customized Encoding to Thwart Side-Channel Attacks -- Extended Version --

Side-channel attacks are an important concern for the security of cryptographic algorithms. To counteract it, a recent line of research has investigated the use of software encoding functions such as dual-rail rather than the well known masking countermeasure. The core idea consists in encoding the sensitive data with a fixed Hamming weight value and perform all operations following this fashion. This new set of countermeasures applies to all devices that leak a function of the Hamming weight of the internal variables. However when the leakage model deviates from this idealized model, the claimed security guarantee vanishes. In this work, we introduce a framework that aims at building customized encoding functions according to the precise leakage model based on stochastic profiling. We specifically investigate how to take advantage of adversary\u27s knowledge of the physical leakage to select the corresponding optimal encoding. Our solution has been evaluated within several security metrics, proving its efficiency against side-channel attacks in realistic scenarios. A concrete experimentation of our proposal to protect the PRESENT Sbox confirms its practicability. In a realistic scenario, our new custom encoding achieves a hundredfold reduction in leakage compared to the dual-rail, although using the same code length

Linear Repairing Codes and Side-Channel Attacks

International audienceTo strengthen the resistance of countermeasures based on secret sharing, several works have suggested to use the scheme introduced by Shamir in 1978, which proposes to use the evaluation of a random d-degree polynomial into n d+1 public points to share the sensitive data. Applying the same principles used against the classical Boolean sharing, all these works have assumed that the most efficient attack strategy was to exploit the minimum number of shares required to rebuild the sensitive value; which is d + 1 if the reconstruction is made with Lagrange's interpolation. In this paper, we highlight first an important difference between Boolean and Shamir's sharings which implies that, for some signal-to-noise ratio, it is more advantageous for the adversary to observe strictly more than d + 1 shares. We argue that this difference is related to the existence of so-called exact linear repairing codes, which themselves come with reconstruction formulae that need (much) less information (counted in bits) than Lagrange's interpolation. In particular, this result implies that, contrary to what was believed, the choice of the public points in Shamir's sharing has an impact on the countermeasure strength. As another contribution, we exhibit a positive impact of the existence of linear exact repairing schemes; we indeed propose to use them to improve the state-of-the-art multiplication algorithms dedicated to Shamir's sharing. We argue that the improvement can be effective when the multiplication operation in the base field is at least two times smaller than in its sub-fields

Orthogonal Direct Sum Masking: A Smartcard Friendly Computation Paradigm in a Code, with Builtin Protection against Side-Channel and Fault Attacks

Secure elements, such as smartcards or trusted platform modules (TPMs), must be protected against implementation-level attacks. Those include side-channel and fault injection attacks. We introduce ODSM, Orthogonal Direct Sum Masking, a new computation paradigm that achieves protection against those two kinds of attacks. A large vector space is structured as two supplementary orthogonal subspaces. One subspace (called a code $\mathcal{C}$) is used for the functional computation, while the second subspace carries random numbers. As the random numbers are entangled with the sensitive data, ODSM ensures a protection against (monovariate) side-channel attacks. The random numbers can be checked either occasionally, or globally, thereby ensuring a fine or coarse detection capability. The security level can be formally detailed: it is proved that monovariate side-channel attacks of order up to $d_\mathcal{C}-1$, where $d_\mathcal{C}$ is the minimal distance of $\mathcal{C}$, are impossible, and that any fault of Hamming weight strictly less than $d_\mathcal{C}$ is detected. A complete instantiation of ODSM is given for AES. In this case, all monovariate side-channel attacks of order strictly less than $5$ are impossible, and all fault injections perturbing strictly less than $5$ bits are detected

On the Bright Side of Darkness: Side-Channel Based Authentication Protocol Against Relay Attacks

Relay attacks are nowadays well known and most designers of secure authentication protocols are aware of them. At present, the main methods to prevent these attacks are based on the so-called distance bounding technique which consists in measuring the round-trip time of the exchanged authentication messages between the prover and the verifier to estimate an upper bound on the distance between these entities. Based on this bound, the verifier checks if the prover is sufficiently close by to rule out an unauthorized entity. Recently, a new work has proposed an authentication protocol that surprisingly uses the side-channel leakage to prevent relay attacks. In this paper, we exhibit some practical and security issues of this protocol and provide a new one that fixes all of them. Then, we argue the resistance of our proposal against both side-channel and relay attacks under some realistic assumptions. Our experimental results show the efficiency of our protocol in terms of false acceptance and false rejection rates

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

Masking countermeasures against HO-DPA : security evaluation and enhancement by specific mask encodings

Les circuits électroniques réalisés avec les méthodes de conception assisté par ordinateur usuelles présentent une piètre résistance par rapport aux attaques physiques. Parmi les attaques physiques les plus redoutables figurent les attaques sur les canaux cachés, comme la ``timing attack'' ou la DPA, qui consistent à enregistrer une quantité physique (temps, consommation) fuie par le circuit pendant qu’il calcule. Cette information peut être exploité pour remonter aux secrets utilisés dans des calculs de chiffrement ou de signature. Plusieurs méthodes de durcissement des circuits contre les attaques sur les canaux cachés ont été proposées. On peut en distinguer deux catégories : Les contre-mesures par dissimulation (ou par logique différentielle), visant à rendre la fuite constante, donc statiquement indépendante des secrets. Les contre-mesures par masquage, visant à rendre la fuite aléatoire, donc statistiquement indépendante des secrets. La contre-mesure par masquage est la moins complexe et la plus simple à mettre en oeuvre, car elle peut s’appliquer au niveau algorithmique comme au niveau logique. Idéalement, le concepteur s’affranchit donc d’un placement-routage manuel, comme cela est le cas des contre-mesures statiques. En revanche elle est la cible d’attaques du second ordre, voire d’ordre plus élevé, permettant d’exhiber le secret en attaquant plusieurs variables simultanément. Cette thèse se fixe comme objectifs l'analyse en robustesse et complexité des implémentations de contre-mesures par masquage et la proposition des nouvelles structures de masquage qui permettent de faire face aux attaques d'ordre élevé.Side channel attacks take advantage of the fact that the power consumption of a cryptographic device depends on the internally used secret key. A very common countermeasure against side channel attacks is masking. It consists in splitting the sensitive variable of cryptographic algorithms into random shares (the masked data and the random mask) so that the knowledge on a subpart of the shares does not give information on the sensitive data itself. However, other attacks, such as higher-order side channel attacks, can defeat masking schemes. These attacks consist in combining the shares in order to cancel (at least partially) the effects of the mask. The overall goal of this thesis is to give a deep analysis of higher-order attacks and to improve the robustness of masking schemes.The first part of this thesis focuses on higher-order attacks. We propose three novel distinguishers. Theoretical and experimental results show the advantages of these attacks when applied to a masking countermeasure. The second part of this thesis is devoted to a formal security evaluation of hardware masking schemes. We propose a new side channel metric to jointly cover the attacks efficiency and the leakage estimation.In the last part, we propose three novel masking schemes remaining more efficient than the state-of-the-art masking. They remove (or at least reduce) the dependency between the leakage and the sensitive variable when the leakage function is known e.g. the Hamming weight or the Hamming distance leakage model). The new solutions have been evaluated within a security framework proving their excellent resistance against higher-order attacks