Malicious cryptography techniques for unreversable (malicious or not) binaries

Fighting against computer malware require a mandatory step of reverse engineering. As soon as the code has been disassemblied/decompiled (including a dynamic analysis step), there is a hope to understand what the malware actually does and to implement a detection mean. This also applies to protection of software whenever one wishes to analyze them. In this paper, we show how to amour code in such a way that reserse engineering techniques (static and dymanic) are absolutely impossible by combining malicious cryptography techniques developped in our laboratory and new types of programming (k-ary codes). Suitable encryption algorithms combined with new cryptanalytic approaches to ease the protection of (malicious or not) binaries, enable to provide both total code armouring and large scale polymorphic features at the same time. A simple 400 Kb of executable code enables to produce a binary code and around $2^{140}$ mutated forms natively while going far beyond the old concept of decryptor.Comment: 17 pages, 2 figures, accepted for presentation at H2HC'1

Software Protection and Secure Authentication for Autonomous Vehicular Cloud Computing

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC. In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our vision of a layer-based approach to thoroughly study state-of-the-art literature in the realm of AVs. Particularly, we examined some cyber-attacks and compared their promising mitigation strategies from our perspective. Then, we focused on two security issues involving AVCC: software protection and authentication. For the first problem, our concern is protecting client’s programs executed on remote AVCC resources. Such a usage scenario is susceptible to information leakage and reverse-engineering. Hence, we proposed compiler-based obfuscation techniques. What distinguishes our techniques, is that they are generic and software-based and utilize the intermediate representation, hence, they are platform agnostic, hardware independent and support different high level programming languages. Our results demonstrate that the control-flow of obfuscated code versions are more complicated making it unintelligible for timing side-channels. For the second problem, we focus on protecting AVCC from unauthorized access or intrusions, which may cause misuse or service disruptions. Therefore, we propose a strong privacy-aware authentication technique for users accessing AVCC services or vehicle sharing their resources with the AVCC. Our technique modifies robust function encryption, which protects stakeholder’s confidentiality and withstands linkability and “known-ciphertexts” attacks. Thus, we utilize an authentication server to search and match encrypted data by performing dot product operations. Additionally, we developed another lightweight technique, based on KNN algorithm, to authenticate vehicles at computationally limited charging stations using its owner’s encrypted iris data. Our security and privacy analysis proved that our schemes achieved privacy-preservation goals. Our experimental results showed that our schemes have reasonable computation and communications overheads and efficiently scalable

When Are Opaque Predicates Useful?

Opaque predicates are a commonly used technique in program obfuscation, intended to add complexity to control flow and to insert dummy code or watermarks. However, there are many attacks known to detect opaque predicates and remove dummy code. We survey these attacks and argue that many types of programs cannot be securely obfuscated using opaque predicates. In particular we explain that most previous works on control flow obfuscation have introduced predicates that are easily distinguished from naturally occurring predicates in code, and hence easily removed by an attacker. We state two conditions that are necessary for a program to be suitable for control flow obfuscation. We give an integrated approach to control flow obfuscation that simultaneously obfuscates real predicates and introduces opaque predicates. The opaque predicates are indistinguishable from the obfuscated real predicates in the program. If an attacker applies the usual approaches (both static and dynamic) to identify and remove opaque predicates then they are likely to remove critical functionality and introduce errors. We have implemented our obfuscator in LLVM. We provide an analysis of the performance of the resulting obfuscated code

Conditionals in Homomorphic Encryption and Machine Learning Applications

Homomorphic encryption aims at allowing computations on encrypted data without decryption other than that of the final result. This could provide an elegant solution to the issue of privacy preservation in data-based applications, such as those using machine learning, but several open issues hamper this plan. In this work we assess the possibility for homomorphic encryption to fully implement its program without relying on other techniques, such as multiparty computation (SMPC), which may be impossible in many use cases (for instance due to the high level of communication required). We proceed in two steps: i) on the basis of the structured program theorem (Bohm-Jacopini theorem) we identify the relevant minimal set of operations homomorphic encryption must be able to perform to implement any algorithm; and ii) we analyse the possibility to solve -- and propose an implementation for -- the most fundamentally relevant issue as it emerges from our analysis, that is, the implementation of conditionals (requiring comparison and selection/jump operations). We show how this issue clashes with the fundamental requirements of homomorphic encryption and could represent a drawback for its use as a complete solution for privacy preservation in data-based applications, in particular machine learning ones. Our approach for comparisons is novel and entirely embedded in homomorphic encryption, while previous studies relied on other techniques, such as SMPC, demanding high level of communication among parties, and decryption of intermediate results from data-owners. Our protocol is also provably safe (sharing the same safety as the homomorphic encryption schemes), differently from other techniques such as Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical section on polynomial approximatio