A Natural Language Processing Approach to Malware Classification

Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a form of feature engineering that is somewhat analogous to techniques that are commonly employed in Natural Language Processing (NLP). We find that this NLP-based approach outperforms other popular techniques on a challenging malware dataset, with an HMM-Random Forrest model yielding the best results

Yet Another Algebraic Cryptanalysis of Small Scale Variants of AES

This work presents new advances in algebraic cryptanalysis of small scale derivatives of AES. We model the cipher as a system of polynomial equations over GF(2), which involves only the variables of the initial key, and we subsequently attempt to solve this system using Gröbner bases. We show, for example, that one of the attacks can recover the secret key for one round of AES-128 under one minute on a contemporary CPU. This attack requires only two known plaintexts and their corresponding ciphertexts. We also compare the performance of Gröbner bases to a SAT solver, and provide an insight into the propagation of diffusion within the cipher

Tests for generators of pseudorandom numbers

In this work we focus on tests for generators of pseudorandom bits. Generators of pseudorandom bits are one of the most important cryptographic tools. In the first part of this work we introduce statistical theory related for randomness testing. Then we present some basic definitions and facts from cryptography. In the second part of the work we describe ten different statistical tests and their modifications. We also present results of tests performed on Decim stream cipher, Geffe generator and Blum Blum Shub generator.

Tests for generators of pseudorandom numbers

V předložené práci se zabýváme testy generátorů pseudonáhodných bitů. Ge- nerátory pseudonáhodných bitů jsou jedním z nejdůležitějších kryptografických nástrojů. V první části této práce uvádíme základní definice a tvrzení z teorie pravděpodobnosti a statistiky potřebné k testování náhodnosti. Dále uvedeme některé základní pojmy a fakta z kryptografie. V druhé části této práce popíšeme deset různých statistických testů a jejich modifikace. Také uvádíme výsledky testů provedených na proudové šifře Decim, Geffe generátoru a Blum Blum Shub ge- nerátoru. 1In this work we focus on tests for generators of pseudorandom bits. Generators of pseudorandom bits are one of the most important cryptographic tools. In the first part of this work we introduce statistical theory related for randomness testing. Then we present some basic definitions and facts from cryptography. In the second part of the work we describe ten different statistical tests and their modifications. We also present results of tests performed on Decim stream cipher, Geffe generator and Blum Blum Shub generator. 1Department of AlgebraKatedra algebryMatematicko-fyzikální fakultaFaculty of Mathematics and Physic