Small Scale Variants of the AES

Abstract. In this paper we define small scale variants of the AES. These variants inherit the design features of the AES and provide a suitable framework for comparing different cryptanalytic methods. In particular, we provide some preliminary results and insights when using off-theshelf computational algebra techniques to solve the systems of equations arising from these small scale variants.

Small Scale AES Toolbox: Algebraic and Propositional Formulas, Circuit-Implementations and Fault Equations

Cryptography is one of the key technologies ensuring security in the digital domain. As such, its primitives and implementations have been extensively analyzed both from a theoretical, cryptoanalytical perspective, as well as regarding their capabilities to remain secure in the face of various attacks. One of the most common ciphers, the Advanced Encryption Standard (AES) (thus far) appears to be secure in the absence of an active attacker. To allow for the testing and development of new attacks or countermeasures a small scale version of the AES with a variable number of rounds, number of rows, number of columns and data word size, and a complexity ranging from trivial up to the original AES was developed. In this paper we present a collection of various implementations of the relevant small scale AES versions based on hardware (VHDL and gate-level), algebraic representations (Sage and CoCoA) and their translations into propositional formulas (in CNF). Additionally, we present fault attack equations for each version. Having all these resources available in a single and well structured package allows researchers to combine these different sources of information which might reveal new patterns or solving strategies. Additionally, the fine granularity of difficulty between the different small scale AES versions allows for the assessment of new attacks or the comparison of different attacks

CryptoKnight:generating and modelling compiled cryptographic primitives

Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio

Potential role of immunotherapy in advanced non-small-cell lung cancer

Immuno checkpoint inhibitors have ushered in a new era with respect to the treatment of advanced non-small-cell lung cancer. Many patients are not suitable for treatment with epidermal growth factor receptor tyrosine kinase inhibitors (eg, gefitinib, erlotinib, and afatinib) or with anaplastic lymphoma kinase inhibitors (eg, crizotinib and ceritinib). As a result, anti-PD- 1/PD-L1 and CTLA-4 inhibitors may play a novel role in the improvement of outcomes in a metastatic setting. The regulation of immune surveillance, immunoediting, and immunoescape mechanisms may play an interesting role in this regard either alone or in combination with current drugs. Here, we discuss advances in immunotherapy for the treatment of metastatic non-small-cell lung cancer as well as future perspectives within this framework.Pierre Fabr

Obtaining and solving systems of equations in key variables only for the small variants of AES

This work is devoted to attacking the small scale variants of the Advanced Encryption Standard (AES) via systems that contain only the initial key variables. To this end, we introduce a system of equations that naturally arises in the AES, and then eliminate all the intermediate variables via normal form reductions. The resulting system in key variables only is solved then. We also consider a possibility to apply our method in the meet-in-the-middle scenario especially with several plaintext/ciphertext pairs. We elaborate on the method further by looking for subsystems which contain fewer variables and are overdetermined, thus facilitating solving the large system

Triplet-Tuning: A Novel Family of Non-Empirical Exchange-Correlation Functionals

In the framework of DFT, the lowest triplet excited state, T$_1$, can be evaluated using multiple formulations, the most straightforward of which are UDFT and TDDFT. Assuming the exact XC functional is applied, UDFT and TDDFT provide identical energies for T$_1$ ($E_{\rm T}$), which is also a constraint that we require our XC functionals to obey. However, this condition is not satisfied by most of the popular XC functionals, leading to inaccurate predictions of low-lying, spectroscopically and photochemically important excited states, such as T$_1$ and S$_1$. Inspired by the optimal tuning strategy for frontier orbital energies [Stein, Kronik, and Baer, {\it J. Am. Chem. Soc.} {\bf 2009}, 131, 2818], we proposed a novel and non-empirical prescription of constructing an XC functional in which the agreement between UDFT and TDDFT in $E_{\rm T}$ is strictly enforced. Referred to as "triplet tuning", our procedure allows us to formulate the XC functional on a case-by-case basis using the molecular structure as the exclusive input, without fitting to any experimental data. The first triplet tuned XC functional, TT-$\omega$PBEh, is formulated as a long-range-corrected hybrid of PBE and HF functionals [Rohrdanz, Martins, and Herbert, {\it J. Chem. Phys.} {\bf 2009}, 130, 054112] and tested on four sets of large organic molecules. Compared to existing functionals, TT-$\omega$PBEh manages to provide more accurate predictions for key spectroscopic and photochemical observables, including but not limited to $E_{\rm T}$, $E_{\rm S}$, $\Delta E_{\rm ST}$, and $I$, as it adjusts the effective electron-hole interactions to arrive at the correct excitation energies. This promising triplet tuning scheme can be applied to a broad range of systems that were notorious in DFT for being extremely challenging