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

CipherTrace: automatic detection of ciphers from execution traces to neutralize ransomware

In 2021, the largest US pipeline system for refined oil products suffered a 6-day shutdown due to a ransomware attack [1]. In 2023, the sensitive systems of the US Marshals Service were attacked by a ransomware [2]. One of the most effective ways to fight ransomware is to extract the secret keys. The challenge of detecting and identifying cryptographic primitives has been around for over a decade. Many tools have been proposed, but the vast majority of them use templates or signatures, and their support for different operating systems and processor architectures is rather limited; neither have there been enough tools capable of extracting the secret keys. In this paper, we present CipherTrace, a generic and automated system to detect and identify the class of cipher algorithms in binary programs, and additionally, locate and extract the secret keys and cryptographic states accessed by the cipher. We focus on product ciphers, and evaluate CipherTrace using four standard cipher algorithms, four different hashing algorithms, and five of the most recent and popular ransomware specimens. Our results show that CipherTrace is capable of fully dissecting Fixed S-Box block ciphers (e.g. AES and Serpent) and can extract the secret keys and other cryptographic artefacts, regardless of the operating system, implementation, or input- or key-size, and without using signatures or templates. We show a significant improvement in performance and functionality compared to the closely related works. CipherTrace helps in fighting ransomware, and aids analysts in their malware analysis and reverse engineering efforts

Formally based semi-automatic implementation of an open security protocol

International audienceThis paper presents an experiment in which an implementation of the client side of the SSH Transport Layer Protocol (SSH-TLP) was semi-automatically derived according to a model-driven development paradigm that leverages formal methods in order to obtain high correctness assurance. The approach used in the experiment starts with the formalization of the protocol at an abstract level. This model is then formally proved to fulfill the desired secrecy and authentication properties by using the ProVerif prover. Finally, a sound Java implementation is semi-automatically derived from the verified model using an enhanced version of the Spi2Java framework. The resulting implementation correctly interoperates with third party servers, and its execution time is comparable with that of other manually developed Java SSH-TLP client implementations. This case study demonstrates that the adopted model-driven approach is viable even for a real security protocol, despite the complexity of the models needed in order to achieve an interoperable implementation

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction