In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Early-stage malware prediction using recurrent neural networks

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

/pdf/dynamic-malware-analysis-with-feature-engineering-and-vryqdsfrqb.pdf

Dynamic Malware Analysis with Feature Engineering and Feature Learning

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.

/pdf/malgra-machine-learning-and-n-gram-malware-feature-3ld1jz7zyg.pdf

MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

DMalNet: Dynamic malware analysis based on API feature engineering and graph learning

Dynamic analysis plays an important role in analyzing malware variants which have used obfuscation, polymorphism and metamorphism techniques. Malware classification is an emerging approach for discriminating different malware families. However, existing malware classification methods have mediocre performance in small scale datasets and some machine learning algorithms have difficulties in handling imbalanced datasets. To solve these issues, we propose an ensemble learning based dynamic malware classification approach aiming at datasets of different scales. Additionally a novel feature selection method is presented to select features with strong discrimination power. In particular, we continue to explore issues in feature representation and feature selection. To verify the efficiency of our approach, we perform a series of comparative experiments with existing feature selection methods, commercial anti-malware tools and current malware classification techniques. The experimental results demonstrate that our approach can classify malware variants in high F1-score while imposing low classification time in datasets of different scales.

A New Malware Classification Approach Based on Malware Dynamic Analysis

Subversion attacks against cryptosystems have already received wide attentions since several decades ago, while the Snowden revelations in 2013 reemphasized the need to further exploring potential avenues for undermining the cryptography in practice. In this work, inspired by the kleptographic attacks introduced by Young and Yung in 1990s [Crypto’96], we initiate a formal study of asymmetric subversion attacks against signature schemes. Our contributions can be summarized as follows.

Asymmetric Subversion Attacks on Signature Schemes

Studies of subversion attack against cryptosystem could be dated to several decades ago, while the Snowden revelation in 2013 has set off a new wave of exploring possible approaches to protect or subvert cryptography primitives in practice. Inspired by kleptographic attacks proposed by Young et al. [Crypto’96], we present the asymmetric subversion attack on signature and identification schemes in this work. Our contributions is summarized as follows: Our subversion attack is demonstrated to be practical and could be mounted on many common schemes, which shows the danger of subversion attacks and spurs the exploring of effective deterrents.

Asymmetric subversion attacks on signature and identification schemes

In this work we resolve the open problem raised by Prabhakaran and Rosulek at CRYPTO 2007, and present the first anonymous, rerandomizable, Replayable-CCA (RCCA) secure public-key encryption scheme. This solution opens the door to numerous privacy-oriented applications with a highly desired RCCA security level. At the core of our construction is a non-trivial extension of smooth projective hash functions (Cramer and Shoup, EUROCRYPT 2002), and a modular generic framework developed for constructing rerandomizable RCCA-secure encryption schemes with receiver-anonymity. The framework gives an enhanced abstraction of the original Prabhakaran and Rosulek’s scheme (which was the first construction of rerandomizable RCCA-secure encryption in the standard model), where the most crucial enhancement is the first realization of the desirable property of receiver-anonymity, essential to privacy settings. It also serves as a conceptually more intuitive and generic understanding of RCCA security, which leads, for example, to new implementations of the notion. Finally, note that (since CCA security is not applicable to the privacy applications motivating our work) the concrete results and the conceptual advancement presented here, seem to substantially expand the power and relevance of the notion of rerandomizable RCCA-secure encryption.

Receiver-Anonymity in Rerandomizable RCCA-Secure Cryptosystems Resolved.

In 2013, the revelation of Edward Snowden rekindled cryptographic researchers’ interest in subversion attacks. Since then, many works have been carried out to explore the power of subversion attack...

Security of Auditing Protocols Against Subversion Attacks

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