25,964 research outputs found
Execution Integrity with In-Place Encryption
Instruction set randomization (ISR) was initially proposed with the main goal
of countering code-injection attacks. However, ISR seems to have lost its
appeal since code-injection attacks became less attractive because protection
mechanisms such as data execution prevention (DEP) as well as code-reuse
attacks became more prevalent.
In this paper, we show that ISR can be extended to also protect against
code-reuse attacks while at the same time offering security guarantees similar
to those of software diversity, control-flow integrity, and information hiding.
We present Scylla, a scheme that deploys a new technique for in-place code
encryption to hide the code layout of a randomized binary, and restricts the
control flow to a benign execution path. This allows us to i) implicitly
restrict control-flow targets to basic block entries without requiring the
extraction of a control-flow graph, ii) achieve execution integrity within
legitimate basic blocks, and iii) hide the underlying code layout under
malicious read access to the program. Our analysis demonstrates that Scylla is
capable of preventing state-of-the-art attacks such as just-in-time
return-oriented programming (JIT-ROP) and crash-resistant oriented programming
(CROP). We extensively evaluate our prototype implementation of Scylla and show
feasible performance overhead. We also provide details on how this overhead can
be significantly reduced with dedicated hardware support
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
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