Reviewerly/exHarmony huggingface dataset:
Towards logical specification of adversarial examples in machine learning

The use of Artificial Intelligence (AI)-based systems, using particularly Machine Learning (ML) classifiers, is growing rapidly and finding uses in many industries. Most of these industries have critical safety, security, and dependability requirements. Despite this rapid growth, interest in the security of these systems has only arisen in the last few years and it is not yet well-studied. There is a want for a formal notion of security for ML systems, similar to that used in classical information security. We took this statement toward security threat modeling and analysis in ML-based systems, focusing on the adversarial example threat. An adversarial example threat is an input of the classifier that was maliciously modified to induce a misclassification. Identifying this threat at the architecture design stage before proceeding with system development is a critical milestone in the development process of secure ML systems. In this paper, we propose an approach to adversarial example threat specification and detection in component-based software architecture models. We use first-order and modal logic as an abstract and technology-independent formalism. The general idea of the approach is to specify the threat as property of a modeled system such that the violation of the specified property indicates the presence of the threat. We demonstrate the applicability of the method through a classifier used in a recommendation system.
Towards Adversarial Robustness with Multidimensional Perturbations via Contrastive Learning

Recent works have demonstrated that neural networks are vulnerable to adversarial attacks, while adversarial training is promising for improving robustness of deep networks. However, these models still remain vulnerable to new types of attacks not seen due to representative general samples may not be provided during training. Moreover, substantially larger datasets are necessary in adversarial robust models than those required for standard training where labeled data is expensive. In this work, we propose a novel approach to adversarial robustness, which establishes on the insights from min-max optimization that more powerful adversarial perturbations lead to more robust defense. Our algorithm is called Adversarial Training with Multidimensional Perturbations (ATMP), aims at guiding networks learn strong representations through minimizing the distance between differently augmented views via adopting an innovative contrastive learning objective function in the latent space. By perturbing the representations corresponding to key robust features, more powerful adversarial perturbations could be obtained in self-supervised form during adversarial training. Besides, we can avoid label leaking to some extent because no label information is required in generating adversarial examples. Extensive experimental results on common benchmarks show that our method can achieve high robustness against various of representative adversarial attacks. We also compare it with the existing state-of-the-art techniques, and the experiments indicate that our method is superior.
Quantity-Simulation-Analysis Method based Novel RSA Timing Attack Algorithm for Single-Chip Microcomputer Platform

There are limitations in storage and computational capacity on the single-chip microcomputer platform under the secure edge computing paradigm. A higher success rate is possible via collecting sensitive information on the time side channel by multivariate statistical analysis to crack the RSA private key when attackers decrypt ciphertexts. We proposed a quantity-simulation-analysis (QSA) method to construct Markov model for RSA timing attack tasks, which firstly quantizes the decrypt process to obtain the time-consuming characteristics, then simulates the machine instruction cycles through parallel computing to analyze Markov model with more precise state transition matrix. On this basis, a novel timing attack algorithm with fuzzy clustering state transition probability matrix of the higher order Markov model on different step sizes is proposed, compared with some algorithms from other literatures taking an exhaustive search attack algorithm as a benchmark. Experimental results show that the algorithm achieves better results in terms of success rate.
Cache Design Effect on Microarchitecture Security: A Contrast between Xuantie-910 and BOOM

Modern processors make use of optimization techniques such as cache and speculation mechanisms to greatly improve performance. But recent research has found that these techniques can also be exploited by attackers to perform powerful side-channel attacks. A large number of powerful cache-based attacks have been replicated and enhanced over Intel X86- and ARM-based architectures, but there is a relative lack of research on RISC-V-based architectures. Xuantie-910 and BOOM are both RISC-V-based processors. So far, cache-side channels in the unprivileged case of Xuantie-910 have not been proven, while cache attacks against BOOM are proliferating. There are two types of caches, including physically-indexed physically-tagged (PIPT) cache (adopted by Xuantie-910) and virtually-indexed physically-tagged (VIPT) cache (adopted by BOOM), corresponding to two different cache addressing forms. VIPT has higher addressing performance than PIPT, since it can directly obtain cache line index from virtual address. In this paper, we study Xuantie-910 and BOOM to explore the impact of cache design on the security of RISC-V-based microarchitecture. Specifically, we compare the impact of their cache addressing forms on precise flushing of cache lines at specified locations, which plays an important role in cache side-channel attacks. Experimental results show that for the VIPT cache in BOOM, the location-specified cache lines can be accurately flushed, and Spectre attack can be successfully carried out by using the cache side-channel. On the other hand, for the PIPT cache in Xuantie-910, it is impossible for attackers to directly and accurately flush the specified location of cache without affecting performance, which hinders the success of cache side-channel attacks. This provides us with an insight that one can adopt a VIPT-based cache with a mechanism similar to PIPT for preventing the accurate access of cache line index, which can not only keep the advantage of high-performance addressing in VIPT but also improve chip security.
Privacy-Preserving Top-k Spatio-Textual Similarity Join

With the development of location-based services, spatio-textual similarity join has attracted much research attention from academic and industrial communities following the study of spatio-textual data processing. To offload the computation and storage burden of spatio-textual similarity join, outsourcing the data processing and storage to the public cloud can achieve great cost savings, however, may cause serious privacy concerns. To this end, in this paper, we first define and solve the privacy-preserving spatio-textual similarity join problem and propose two novel secure similarity join schemes. As the baseline, we first present a straightforward scheme applying Asymmetric Inner Product Encryption (AIPE) to facilitate the data encryption and similarity calculation in ciphertext. To improve the efficiency of the basic scheme, we further propose an optimized secure top-k spatio-textual similarity join scheme by constructing a secure index based on the hybrid Locality-Sensitive Hashing (LSH). Through matching the encrypted hash values over the secure index to narrow down the candidates, the similarity join results can be efficiently retrieved from the candidate pairs. Comprehensive analysis of the proposed schemes is provided in terms of computational complexity and security guarantees, and extensive experimental results on real and synthetic datasets show the performance of our schemes.
SEEKER: A Root Cause Analysis Method Based on Deterministic Replay for Multi-Type Network Protocol Vulnerabilities

Various types of network protocol software vulnerabilities often result in considerable damage. However, existing root cause analysis methods, which rely on symbolic path tracing and the hardware processor tracing (PT) function, cannot be applied in protocol software. They are also limited by the restricted resources of embedded platforms and symbolic execution ability. Additionally, manually analysing vulnerabilities is typically labour intensive. To solve this problem, we propose SEEKER, the first root cause analysis method based on deterministic replay for multi-type network protocol vulnerabilities to automatically generate vulnerability analysis reports. By proposing a multilayer semantic model, SEEKER extracts fine-grained semantics, compares the extracted semantics with predefined vulnerability rules and finally generates an analysis report.We implemented and evaluated SEEKER against 7 vulnerability types, across 4 real-world software programs, covering 2 different platforms. The experimental results show that SEEKER can identify the root causes of multi-type vulnerabilities and even find 3 new 0-day vulnerabilities. Meanwhile, SEEKER demonstrates impressive adaptability and scalability. It can analyse one execution path that involves up to 135,437,793 instructions and upwards of 15,893,356 memory access requests.
A Hardware Security Isolation Architecture for Intelligent Accelerator

AI systems face potential hardware security threats. Existing AI systems generally use the heterogeneous architecture of CPU + Intelligent Accelerator, with PCIe bus for communication between them. Security mechanisms are implemented on CPUs based on the hardware security isolation architecture. But the conventional hardware security isolation architecture does not include the intelligent accelerator on the PCIe bus. Therefore, from the perspective of hardware security, data offloaded to the intelligent accelerator face great security risks. In order to effectively integrate intelligent accelerator into the CPU’s security mechanism, a novel hardware security isolation architecture is presented in this paper. The PCIe protocol is extended to be security-aware by adding security information packaging and unpacking logic in the PCIe controller. The hardware resources on the intelligent accelerator are isolated in fine-grained. The resources classified into the secure world can only be controlled and used by the software of CPU’s trusted execution environment. Based on the above hardware security isolation architecture, a security isolation spiking convolutional neural network accelerator is designed and implemented in this paper. The experimental results demonstrate that the proposed security isolation architecture has no overhead on the bandwidth and latency of the PCIe controller. The architecture does not affect the performance of the entire hardware computing process from CPU data offloading, intelligent accelerator computing, to data returning to CPU. With low hardware overhead, this security isolation architecture achieves effective isolation and protection of input data, model, and output data. And this architecture can effectively integrate hardware resources of intelligent accelerator into CPU’s security isolation mechanism.
Short Signatures via Multiple Hardware Security Modules with Key Splitting in Circuit Breaking Environments

A Circuit Breaking Environment (CBE) for Connected Railway Infrastructures (CRI) requires that high sensitive cargos are bound to the transportation train carriges. This implies a continous verification of the connectivity and rapid identification of potenital disconnections. For that purpose we consider signatures run on devices with multiple Hardware Security Modules (HSM) architectures. We propose a modification of BLS signatures with an additive key split augumented with a refresh technique. This protects against a powerful adversary that can control distinct HSMs in different signing sessions. Thus, we consider our scheme to be secure even if the adversary switches between chosen HSMs for leakage of partial secrets, from session to session. Finally, we provide promising results from a proof-of- concept implementation, tested on several different type of low- powered devices for comparison. These indicate the feasibility of our constructions.
DeepRTest: A Vulnerability-Guided Robustness Testing and Enhancement Framework for Deep Neural Networks

Effective testing methods have been proposed to verify the reliability and robustness of Deep Neural Networks (DNNs). However, enhancing their adversarial robustness against various attacks and perturbations through testing remains a key issue for their further applications. Therefore, we propose DeepRTest, a white-box testing framework for DNNs guided by vulnerability to effectively test and improve the adversarial robustness of DNNs. Specifically, the test input generation algorithm based on joint optimization fully induces the misclassification of DNNs. The generated high neuron coverage inputs near classification boundaries expose vulnerabilities to test adversarial robustness comprehensively. Then, retraining based on the generated inputs effectively optimize the classification boundaries and fix the vulnerabilities to improve the adversarial robustness against perturbations. The experimental results indicate that DeepRTest achieved higher neuron coverage and classification accuracy than baseline methods. Moreover, DeepRTest could improve the adversarial robustness by 39% on average, which was 12.56% higher than other methods.
Truthfully Negotiating Usage Policy for Data Sovereignty

To realize data sovereignty, the International Data Space (IDS), adopting usage policies to determine how, when and where other enterprises or individuals may use data, has been proposed by the IDS association and widely received attention from academia and industry. However, because data in the IDS are transferred across domains, existing policy creation approaches for a single domain cannot be applied in the IDS. To address this problem, in this paper, we propose a negotiation scheme to create usage policies in the IDS. In detail, we formulate usage policy negotiation as a combinatorial auction problem and adopt the Vickrey-Clarke-Groves (VCG) mechanism to incentivize potential data providers to truthfully negotiate usage policies. Both theoretical and simulation results show that our scheme maintains truthfulness on data providers and is cost-efficient.
Squeezing Network Performance for Secure and Efficient PoW with Better Difficulty Adjustment

Difficulty adjustment algorithm (DAA) is critical for PoW blockchain systems in currency issuance, transaction throughput, and blockchain security. Most DAAs concentrate on the unique goal of block-making stability from seconds to minutes. In contrast, we propose a new DAA with the goal of stabilizing orphan rates among the blocks made recently, observing that the orphan rate is more indicative since too high orphan rate indicates poor blockchain security and waste of honest hash power whereas too low orphan rate indicates poor blockchain throughout and wast of time awaiting other blocks. We notice a security defect of the recent orphan-rate DAA (NDSS 2022) in which an attacker may continually create orphan blocks and mislead miners to wrongly increase the difficulty. To fix this defect, our enhanced DAA takes into the orphan rate variation during the historical slots with smooth exponential decay affection on current difficulty adjustment. Theoretical and experimental analyses show that our DAA is effective and secure.
An Empirical Study on Software Defect Prediction using Function Point Analysis

The software defect prediction method based on requirement specification is proposed to address the defect prediction needs in the requirements phase when the organization adopts the W-model of software development. The theoretical synthesis presents that the function point and the number of defects should be positively correlated. The theory’s correctness is verified by analyzing the correlation between function point and defect distribution of eight software applications. Then, the mathematical equations for software configuration testing defects are derived, and the specific meaning of the equation is explained. Finally, the shortcomings of this study and the subsequent research directions are pointed out.
Kgastor: a Privacy By Design Knowledge Graph anonymized Store

Regulations such as GDPR and CCPA are making the management of personal information an increasingly sensitive issue for businesses and organizations. We consider that data protection mechanisms must intervene upstream as recommended by the principles of privacy by design. Our view is that database management systems should be equipped with anonymization mechanisms. By relying on a partitioned data storage scheme, support for data update operations and query rewriting, our approach offers a trade-off between data privacy and utility. Some advantages of our approach are a low overhead of storing partitioned data, the performance of CRUD operations and the efficiency of a batch delivery approach to data updates. We evaluate our prototype on an open source RDF store and on synthetic datasets.
SCEVD: Semantic-enhanced Code Embedding for Vulnerability Discovery

Source code vulnerability detection is a major goal in security research. In recent years, deep learning methods have been applied to this end, however the task of embedding code into vector representations as input for deep learning models has yet to be definitively solved. The use of graphs, specifically Abstract Syntax Trees and Code Property Graphs, is a promising research direction for this task, however learning from graphs grows prohibitively computationally expensive for large graphs. No close examination of intelligent ways to prune this input to only vulnerability-relevant information has yet been performed. Additionally, most existing works focus largely on structural information from graphs, often neglecting information contained within the nodes themselves. We address these gaps in the prior research by proposing SCEVD: a deep learning model for vulnerability discovery which utilises semantic information to intelligently select features in source code graphs for learning. It uses information contained within code graph nodes, as well as information about their relationships with one another to select the code graph features which are most relevant to code vulnerability. We implement SCEVD and conduct experiments using the SARD Juliet test suite, finding that we are able to improve vulnerability discovery results using this process of semantic-enhanced code graph feature selection.
Fuzzy Keyword Search over Encrypted Cloud Data with Dynamic Fine-grained Access Control

Due to the increasing popularity of cloud computing and privacy preservation concerns, sensitive data should be encrypted before outsourcing to the cloud and data utilization becomes a challenging issue. Searchable encryption (SE) is a promising technique to address this problem. Most existing searchable encryption schemes only support accurate keyword but fuzzy keyword search schemes are appreciated in practice. Moreover, in some application scenarios like the video on demand systems, data owners only hope to give the access of their data to those who have payed but authenticated user identity may often change. Therefore, dynamic user attribute updating should be considered. To solve about issues, we propose a fuzzy secure keyword search scheme over encrypted cloud data with dynamic fine-grained access control. We design a novel fuzzy keyword index to retrieve corresponding documents. To reduce the computation cost, the cloud server selects most relevant top-k documents and return them to the data users. The ciphertext-policy attribute based encryption (CP-ABE) technique is introduced to implement fine-grained access control. Meanwhile, the basic CP-ABE is improved to meet the practical need of user attribute updating. Extensive security and performance analysis demonstrates that our proposed scheme is highly efficient and can satisfy the security requirements for fuzzy keyword search over encrypted cloud data.
DRSN with Simple Parameter-Free Attention Module for Specific Emitter Identification

Current specific emitter identification methods based on deep learning face many challenges such as high computational effort, weak anti-interference capability, unstable signal feature extraction, and limited improvement in classification performance. To solve these challenges, a new deep learning method is proposed, namely Deep Residual Shrinking Networks with Simple Parameter-Free Attention Module (DRSN-SimAM). The advantages of the parameter-free attention module and deep residual shrinkage network are integrated by DRSN-SimAM. The parameter-free attention module is introduced into the residual shrinkage building unit for adaptively learning thresholds, filtering out redundant information irrelevant to the signal fingerprint. We used the simple parameter-free attention module to enhance the ability of the model to learn features in strong noisy signals, with the goal of improving the classification of wireless devices. Experimental results reveal that the classification accuracy is as high as 98.8%. The number of parameters of the proposed DRSN-SimAM is 12.29% less than the number of parameters of the DRSN when the number of units is 11. Our proposed DRSN-SimAM is more effective in terms of improved wireless device identification performance and model size compression as compared to existing specific emitter identification methods based on deep learning.
CDEdit: Redactable Blockchain with Cross-audit and Diversity Editing

Redactable blockchain allows modifiers or voting committees with modification privileges to edit the data on the chain. Among them, trapdoor holders in chameleon-based hash redactable blockchains can quickly compute hash collisions for arbitrary data without breaking the link of the hash-chain. However, chameleon-based hash redactable blockchain schemes have difficulty solving issues such as editing operations with different granularity or conflicts and auditing modifiers that abuse editing privileges. To address the above challenges, we propose a redactable blockchain with Cross-audit and Diversity Editing (CDEdit). The proposed scheme distributes subdivided transaction-level and block-level tokens to the matching modifier committee to weaken the influence of central power. A number of modifiers are unpredictably selected based on reputation value proportions and the mapping of the consistent hash ring to enable diversity editing operations, and resist Sybil attacks. Meanwhile, an adaptive cross-auditing protocol is proposed to adjust the roles of modifiers and auditors dynamically. This protocol imposes a reputation penalty on the modifiers of illegal edits and solves the problems of abuse of editing privileges and collusion attacks. In addition, We used ciphertext policy attribute-based encryption (CP-ABE) and chameleon hashes with ephemeral trapdoor (CHET) for data modification, and present a system steps and security analysis of CDEdit. Finally, the extensive comparisons and evaluations show that our scheme costs less time overhead than other schemes and is suitable for complex application scenarios, e.g. IoT data management.