Cryptography and Security (cs.CR)

Abstract: Traditional email encryption schemes are vulnerable to EFail attacks, which exploit the lack of message authentication by manipulating ciphertexts and exfiltrating plaintext via HTML backchannels. Swiss Post's IncaMail, a secure email service for transmitting legally binding, encrypted, and verifiable emails, counters EFail attacks using an authenticated-encryption with associated data (AEAD) encryption scheme to ensure message privacy and authentication between servers. IncaMail relies on a trusted infrastructure backend and encrypts messages per user policy. This paper presents a revised IncaMail architecture that offloads the majority of cryptographic operations to clients, offering benefits such as reduced computational load and energy footprint, relaxed trust assumptions, and per-message encryption key policies. Our proof-of-concept prototype and benchmarks demonstrate the robustness of the proposed scheme, with client-side WebAssembly-based cryptographic operations yielding significant performance improvements (up to ~14x) over conventional JavaScript implementations.

Abstract: Following the increasing trends of malicious applications or cyber threats in general, program analysis has become a ubiquitous technique in extracting relevant features. The current state-of-the-art solutions seem to fall behind new techniques. For instance, dynamic binary instrumentation (DBI) provides some promising results, but falls short when it comes to ease of use and overcoming analysis evasion. In this regard, we propose a two-fold contribution. First, we introduce COBAI (Complex Orchestrator for Binary Analysis and Instrumentation), a DBI framework designed for malware analysis, prioritizing ease-of-use and analysis transparency, without imposing a significant overhead. Second, we introduce an aggregated test suite intended to stand as a benchmark in determining the quality of an analysis solution regarding the protection against evasion mechanisms. The efficiency of our solution is validated by a careful evaluation taking into consideration other DBI frameworks, analysis environments, and the proposed benchmark.

Abstract: We propose a novel approach that utilizes fuzzification theory to perform feature selection on website content for encryption purposes. Our objective is to identify and select the most relevant features from the website by harnessing the principles of fuzzy logic. Fuzzification allows us to transform the crisp website content into fuzzy representations, enabling a more nuanced analysis of their characteristics. By considering the degree of membership of each feature in different fuzzy categories, we can evaluate their importance and relevance for encryption. This approach enables us to prioritize and focus on the features that exhibit higher membership degrees, indicating their significance in the encryption process. By employing fuzzification-based feature selection, we aim to enhance the effectiveness and efficiency of website content encryption, ultimately improving the overall internet security.

Abstract: Secure multi-party computation using a physical deck of cards, often called card-based cryptography, has been extensively studied during the past decade. Many card-based protocols to securely compute various Boolean functions have been developed. As each input bit is typically encoded by two cards, computing an $n$-variable Boolean function requires at least $2n$ cards. We are interested in optimal protocols that use exactly $2n$ cards. In particular, we focus on symmetric functions, where the output only depends on the number of 1s in the inputs. In this paper, we formulate the problem of developing $2n$-card protocols to compute $n$-variable symmetric Boolean functions by classifying all such functions into several NPN-equivalence classes. We then summarize existing protocols that can compute some representative functions from these classes, and also solve some of the open problems by developing protocols to compute particular functions in the cases $n=4$, $5$, $6$, and $7$.

Abstract: Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results. As a result, antivirus developers are incorporating machine learning models into their products. While these models improve malware detection capabilities, they also carry the disadvantage of being susceptible to adversarial attacks. Although this vulnerability has been demonstrated for many models in white-box settings, a black-box attack is more applicable in practice for the domain of malware detection. We present a generator of adversarial malware examples using reinforcement learning algorithms. The reinforcement learning agents utilize a set of functionality-preserving modifications, thus creating valid adversarial examples. Using the proximal policy optimization (PPO) algorithm, we achieved an evasion rate of 53.84% against the gradient-boosted decision tree (GBDT) model. The PPO agent previously trained against the GBDT classifier scored an evasion rate of 11.41% against the neural network-based classifier MalConv and an average evasion rate of 2.31% against top antivirus programs. Furthermore, we discovered that random application of our functionality-preserving portable executable modifications successfully evades leading antivirus engines, with an average evasion rate of 11.65%. These findings indicate that machine learning-based models used in malware detection systems are vulnerable to adversarial attacks and that better safeguards need to be taken to protect these systems.