Blockchain Papers

Mohd Danish, Mohammad Amjad, Tanvir Ahmad, Ayodeji Olalekan Salau

Natural Language Processing (NLP) has greatly improved the ability to analyze and understand textual data, which is crucial for understanding user attitudes. Evaluating reviews from top bitcoin apps in India and Twitter data, this study applies advanced natural language processing techniques including BERT, RoBERTa, and the ChatGPT model to determine user sentiment. Using a diverse dataset consisting of 7,197 reviews, the author compared the models’ performance using metrics such as accuracy, precision, recall, and F1-score. According to the results, RoBERTa achieved the highest accurac y and F1 score (90%), followed by BERT (89.37% accuracy, 89% F1 score) and ChatGPT (90.00% accuracy, 88% F1 score). The performance metrics of conventional models, including Naive Bayes and Support Vector Machine (SVM), were poorer, showing that advanced natural language processing (NLP) models handled sentiment analysis better. Comparison bar charts and a confusio n matrix are two visualizations that help to further explain the findings. This research has real-world implications for understanding how people feel about bitcoin apps by revealing commonalities and missing features. To further enhance performance and accuracy, future research will optimize model parameters and explore sentiment analysis on a broader range of platforms.

Pavel Pantiukhov, Dmitrii Koriakov, Tatiana V. Petrova, Jeovane Honório Alves · 6 authors

Blockchain technology has consistently attracted attention for its transparency, decentralization, and security. More everyday users, without technical expertise, are now using blockchain and cryptocurrencies to store funds. As decentralized finance (DeFi) rises, security and privacy challenges have emerged, particularly the risk of losing funds due to leaked private keys. Consequently, enhancing security with user-friendly authentication methods is crucial. Voice authentication is a promising solution to add a security layer to the blockchain, but it is difficult to implement in DeFi without compromising decentralization and data confidentiality. Furthermore, advances in deep learning in voice cloning pose risks to voice-based systems. To address these issues, we propose the ZK Verify Voice Authentication System, which enables spoofing-aware speaker verification for the XRP Ledger (XRPL). Confidentiality is ensured by integrating voice embeddings with zero-knowledge proofs (zk-SNARKs). Voice embeddings serve as digital signatures, with only their hash stored on XRPL. Our system improves security and user experience by allowing individuals to prove their identity without exposing voice data. This approach provides robust security and privacy for DeFi participants, while remaining accessible to those without technical expertise.

Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Olga Kolesnikova · 5 authors

This study performs analysis of Predictive statements, Hope speech, and Regret Detection behaviors within cryptocurrency-related discussions, leveraging advanced natural language processing techniques. We introduce a novel classification scheme named "Prediction statements," categorizing comments into Predictive Incremental, Predictive Decremental, Predictive Neutral, or Non-Predictive categories. Employing GPT-4o, a cutting-edge large language model, we explore sentiment dynamics across five prominent cryptocurrencies: Cardano, Binance, Matic, Fantom, and Ripple. Our analysis reveals distinct patterns in predictive sentiments, with Matic demonstrating a notably higher propensity for optimistic predictions. Additionally, we investigate hope and regret sentiments, uncovering nuanced interplay between these emotions and predictive behaviors. Despite encountering limitations related to data volume and resource availability, our study reports valuable discoveries concerning investor behavior and sentiment trends within the cryptocurrency market, informing strategic decision-making and future research endeavors.

Michael Cary

Abstract Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, “wagmi” (We’re all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts’ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (“wagmi”). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.

Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olšák · 6 authors

The development of strong CDCL-based propositional (SAT) solvers has greatly advanced several areas of automated reasoning (AR). One of the directions in AR is therefore to make use of SAT solvers in expressive formalisms such as first-order logic, for which large corpora of general mathematical problems exist today. This is possible due to Herbrand's theorem, which allows reduction of first-order problems to propositional problems by instantiation. The core challenge is synthesizing the appropriate instances from the typically infinite Herbrand universe. In this work, we develop a machine learning system targeting this task, addressing its combinatorial and invariance properties. In particular, we develop a GNN2RNN architecture based on a graph neural network (GNN) that learns from problems and their solutions independently of many symmetries and symbol names (addressing the abundance of Skolems), combined with a recurrent neural network (RNN) that proposes for each clause its instantiations. The architecture is then combined with an efficient ground solver and, starting with zero knowledge, iteratively trained on a large corpus of mathematical problems. We show that the system is capable of solving many problems by such educated guessing, finding proofs for 32.12% of the training set. The final trained system solves 19.74% of the unseen test data on its own. We also observe that the trained system finds solutions that the iProver and CVC5 systems did not find.

Marco Stronati, Denis Firsov, Antonio Locascio, Benjamin Livshits

Plonkish is a popular circuit format for developing zero-knowledge proof systems that powers a number of major projects in the blockchain space, responsible for holding billions of dollars and processing millions of transactions per day. These projects, including zero-knowledge rollups, rely on highly hand-optimized circuits whose correctness comes at the cost of time-consuming testing and auditing. In this paper, we present Clap, the first Rust eDSL with a proof system agnostic circuit format, facilitating extensibility, automatic optimizations, and formal assurances for the resultant constraint system. Clap casts the problem of producing Plonkish constraint systems and their witness generators as a semantic-preserving compilation problem. Soundness and completeness of the transformation guarantees the absence of subtle bugs caused by under- or over-constraining. Our experimental evaluation shows that its automatic optimizations achieve better performance compared to manual circuit optimization. The optimizer can also be used to automatically derive custom gates from circuit descriptions.

Worasait Suwannik

Multiple sequence alignment (MSA) is a fundamental algorithm in bioinformatics. In a situation when the alignment might need to be protected while revealing the other information such the input sequences and the alignment score, zero knowledge proof can be used. In this paper, a validator checks the consistency between the input sequence and the alignment, and between the alignment and the alignment score. The validator is written in Circom language which will be compile into a circuit. Using a zero knowledge prove system called zkSNARK, a cryptographic proof is generates for the circuit and its input. This proof demonstrates that all inputs are consistent without revealing the actual alignment.

Haochen Sun, J. Li, Change Institutions to: University of Waterloo

The recent surge in artificial intelligence (AI), characterized by the prominence of large language models (LLMs), has ushered in fundamental transformations across the globe. However, alongside these advancements, concerns surrounding the legitimacy of LLMs have grown, posing legal challenges to their extensive applications. Compounding these concerns, the parameters of LLMs are often treated as intellectual property, restricting direct investigations. In this study, we address a fundamental challenge within the realm of AI legislation: the need to establish the authenticity of outputs generated by LLMs. To tackle this issue, we present zkLLM, which stands as the inaugural specialized zero-knowledge proof tailored for LLMs to the best of our knowledge. Addressing the persistent challenge of non-arithmetic operations in deep learning, we introduce tlookup, a parallelized lookup argument designed for non-arithmetic tensor operations in deep learning, offering a solution with no asymptotic overhead. Furthermore, leveraging the foundation of tlookup, we introduce zkAttn, a specialized zero-knowledge proof crafted for the attention mechanism, carefully balancing considerations of running time, memory usage, and accuracy. Empowered by our fully parallelized CUDA implementation, zkLLM emerges as a significant stride towards achieving efficient zero-knowledge verifiable computations over LLMs. Remarkably, for LLMs boasting 13 billion parameters, our approach enables the generation of a correctness proof for the entire inference process in under 15 minutes. The resulting proof, compactly sized at less than 200 kB, is designed to uphold the privacy of the model parameters, ensuring no inadvertent information leakage.

Biagio Boi, Christian Esposito, Sokjoon Lee

Smart contracts are self-executing programs that can run on a blockchain. Due to the fact of being immutable after their deployment on blockchain, it is crucial to ensure their correctness. For this reason, various approaches for static analysis of smart contracts have been proposed, but they may be on the one hand imprecise or on the other hand difficult to train. In this paper, we propose a novel approach for detecting smart contract vulnerabilities using OpenAI's Generative Pre-trained Transformer 3 (GPT-3) language model. Our approach, called VulntHunt-GPT, uses GPT-3 to examine Ethereum smart contracts in order to identify the most popular vulnerabilities according to OWASP. We train VulntHunt-GPT on a dataset of smart contract functions and vulnerabilities to improve its accuracy. Our experiments show that VulntHunt-GPT outperforms almost all the existing state-of-the-art approaches in detecting a variety of vulnerabilities, including reentrancy attacks, integer overflow, and uninitialized storage. In addition, we conduct a case study to demonstrate the effectiveness of VulntHunt-GPT in detecting real-world smart contract vulnerabilities. We show that VulntHunt-GPT can identify previously unknown vulnerabilities in popular smart contracts, highlighting its potential for improving smart contract security. Our approach provides a promising direction for using natural language processing techniques to improve smart contract security and reduce the risk of smart contract exploits.

Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu

We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of current QA applications: (i) unfaithful hallucination that is inconsistent with real-time or domain facts and (ii) weak reasoning performance over compositional information. Our key contribution is a novel reasoning-retrieval mechanism that decomposes a complex question into a reasoning chain via systematic prompting and pre-designed actions. Methodologically, we propose three types of domain-adaptable `Plug-and-Play' actions for retrieving real-time information from heterogeneous sources. We also propose a multi-reference faith score (MRFS) to verify and resolve conflicts in the answers. Empirically, we exploit both public benchmarks and a Web3 case study to demonstrate the capability of CoA over other methods.

Boyi Zeng, Wang, Lizheng, Hu, Yuncong, Xu, Yi · 8 authors

Protecting the copyright of large language models (LLMs) has become crucial due to their resource-intensive training and accompanying carefully designed licenses. However, identifying the original base model of an LLM is challenging due to potential parameter alterations. In this study, we introduce HuRef, a human-readable fingerprint for LLMs that uniquely identifies the base model without interfering with training or exposing model parameters to the public. We first observe that the vector direction of LLM parameters remains stable after the model has converged during pretraining, with negligible perturbations through subsequent training steps, including continued pretraining, supervised fine-tuning, and RLHF, which makes it a sufficient condition to identify the base model. The necessity is validated by continuing to train an LLM with an extra term to drive away the model parameters' direction and the model becomes damaged. However, this direction is vulnerable to simple attacks like dimension permutation or matrix rotation, which significantly change it without affecting performance. To address this, leveraging the Transformer structure, we systematically analyze potential attacks and define three invariant terms that identify an LLM's base model. Due to the potential risk of information leakage, we cannot publish invariant terms directly. Instead, we map them to a Gaussian vector using an encoder, then convert it into a natural image using StyleGAN2, and finally publish the image. In our black-box setting, all fingerprinting steps are internally conducted by the LLMs owners. To ensure the published fingerprints are honestly generated, we introduced Zero-Knowledge Proof (ZKP). Experimental results across various LLMs demonstrate the effectiveness of our method. The code is available at https://github.com/LUMIA-Group/HuRef.

J. Leon Zhao, Xiang Chen, Guang Yang, Yiheng Shen

The previous smart contract code comment (SCC) generation approaches can be divided into two categories: fine-tuning paradigm-based approaches and information retrieval-based approaches. However, for the fine-tuning paradigm-based approaches, the performance may be limited by the quality of the gathered dataset for the downstream task and they may have knowledge-forgetting issues. While for the information retrieval-based approaches, it is difficult for them to generate high-quality comments if similar code does not exist in the historical repository. Therefore we want to utilize the domain knowledge related to SCC generation in large language models (LLMs) to alleviate the disadvantages of these two types of approaches. In this study, we propose an approach SCCLLM based on LLMs and in-context learning. Specifically, in the demonstration selection phase, SCCLLM retrieves the top-k code snippets from the historical corpus by considering syntax, semantics, and lexical information. In the in-context learning phase, SCCLLM utilizes the retrieved code snippets as demonstrations, which can help to utilize the related knowledge for this task. We select a large corpus from a smart contract community Etherscan.io as our experimental subject. Extensive experimental results show the effectiveness of SCCLLM when compared with baselines in automatic evaluation and human evaluation.

Keerthi Nelaturu, Eric Keilty, Andreas Veneris

Significant efforts have been dedicated to employing model-checking as a formal verification approach in the context of smart contracts. The utilization of these tools necessitates an in-depth knowledge on the part of the developer regarding both the programming language and the implementation of model-checking techniques. To provide accessibility to developers with basic language proficiency, we present a technique for developing a conversational application framework that can be seamlessly linked with any model-checking tool for the purpose of creating a smart contract. This architecture offers a robust and effective approach to the development of safe and dependable smart contracts. The utilization of natural language processing techniques in conjunction with neural networks is employed for this objective. Using this methodology, a prototype implementation for Move smart contracts has been created and is used with the VeriMove model-checking tool. Using the offered graphical user interface, we were able to successfully build, compile and test Move smart contracts across four different classes of smart contracts. This strategy effectively decreases the amount of time and effort needed for manual coding and debugging. In addition, the use of the VeriMove model-checking tool guarantees that the smart contracts produced are devoid of any potential vulnerabilities and flaws.

Aigerim Mansurova, Aliya Nugumanova, Zhansaya Makhambetova

Large Language Models (LLMs), such as ChatGPT, have transformed the field of natural language processing with their capacity for language comprehension and generation of human-like, fluent responses for many downstream tasks. Despite their impressive capabilities, they often fall short in domain-specific and knowledge-intensive domains due to a lack of access to relevant data. Moreover, most state-of-art LLMs lack transparency as they are often accessible only through APIs. Furthermore, their application in critical real-world scenarios is hindered by their proclivity to produce hallucinated information and inability to leverage external knowledge sources. To address these limitations, we propose an innovative system that enhances LLMs by integrating them with an external knowledge management module. The system allows LLMs to utilize data stored in vector databases, providing them with relevant information for their responses. Additionally, it enables them to retrieve information from the Internet, further broadening their knowledge base. The research approach circumvents the need to retrain LLMs, which can be a resource-intensive process. Instead, it focuses on making more efficient use of existing models. Preliminary results indicate that the system holds promise for improving the performance of LLMs in domain-specific and knowledge-intensive tasks. By equipping LLMs with real-time access to external data, it is possible to harness their language generation capabilities more effectively, without the need to continually strive for larger models.

Mitsuaki Ueno, Takashi Sano, Hirotada Honda, Shugo Nakamura

Cryptocurrencies are highly anonymous, poorly regulated in many countries, and can issue tokens at nearzero cost using existing platforms. As a result, there is no shortage of fraudulent cryptocurrencies that raise large sums of money through hype, then disappear and do little actual project development. The prevalence of fraudulent cryptocurrencies not only harms investors but can also prevent sound companies from raising funds. To remedy this situation, it would be useful to develop a method to determine whether a particular cryptocurrency is fraudulent or not. The information in cryptocurrency whitepapers could be useful in detecting fraudulent cryptocurrency, but there are no clear criteria to evaluate the reliability and feasibility of their content. Besides, most studies analyzing whitepapers focus on the success or failure of ICO ”fundraising” and fail to adequately consider the ongoing development and operation of the project. On the other hand, a few studies have attempted to detect fraudulent cryptocurrencies from whitepapers, but their results suggest the possibility of identifying fraud with high accuracy. The objective of this paper is to build a model to detect fraudulent cryptocurrencies from whitepapers using natural language processing and machine learning techniques, and to verify whether the model has sufficient predictive accuracy in detecting fraud, after solving the problems of previous studies. We collected 250 cryptocurrency whitepapers consisting of 150 frauds and 100 controls, extracted features, and applied multiple machine learning methods to classify frauds and controls. Then analyzed the feature differences between the fraud and control groups, and examined the tendency of fraudulent cryptocurrency whitepapers. We observed 0.841 F1 Score for the best prediction model, which outperforms previous studies. Furthermore, the performance of K-Means, which is unsupervised learning, was not significantly lower than that of other machine learning methods, and a certain level of accuracy was confirmed. Therefore, there is a possibility that K-Means can be used in cases where fraud criteria cannot be clearly defined. We also found that fraudulent cryptocurrency whitepapers used relatively more business and finance-related words. On the other hand, whitepapers in the control group tended to use more blockchain-related technical terms.

Xinyi Zhu

As social media has played an essential role in expressing individual ideas, connecting, communicating, and interacting with one another on the digital platform, it has also provided a novel set of data to understand our daily life (Kakulapati, 2021). Non-fungible token (NFT) is an extension of digital token transactions that emerged in 2021. It has also inherited a much more advanced set of data and provides an alternative for information and data communication. As NFT differs from fungible tokens, it is the presentation of the digital and unique transactions of tokens. Based on the blockchain system, NFT is intrinsically the demonstration of decentralised system applied in the transaction of digital work. Each property can be traded freely with the customized value that is set by the NFT creator (Wang et al., 2021). As the value is being set and generated through the smart contract, here the smart contract refers to the adapted contracts that are run by programs that are originally set by individuals, this means that every NFT transaction has complete freedom of setting its own contract conditions, with external interference. NFT transaction is also a transparent process (Wang et al., 2021). While the NFT transaction has been finished, there is a smart contract that has been generated through machines automatically and it is not reversible or editable. The whole transaction process and related information are all documented fully by the machine. Due to the existing transparent framework and the decentralised system, the open data on NFT transactions and predictions are mushrooming ever since 2021.

Mohamed Elhoseny, Abdelaziz Darwiesh, A. H. El-Baz, Joel J. P. C. Rodrigues

With the help of social media indicators, this study offers a brand-new intelligent risk management model to enhance the security of cryptocurrency. Based on surveying the previous studies, we found most of them focused on employing many techniques to enhance virtual currencies' security. However, there is no study concentrated on mining threats depending on investors' perceptions. These perceptions can give us a clear overview about the critical risks and threats. This model employs natural language processing techniques to perform risk analysis for the interactions of users on social media platforms. Additionally, a case study on investors of virtual currencies in the USA is presented where the findings of the obtained results refer to almost a quarter of the sample includes risk indications that can be classified as not only technological risks but also financial, operational, and geopolitical risks. Furthermore, performance metrics are calculated to show the new model's capabilities such that the mean accuracy for risk analysis, risk identification, and risk assessment is 77%.

Manoel Fernando Alonso Gadi, Miguel‐Ángel Sicilia

Well labeled natural language corpus data is essential for most natural language processing techniques, especially in specialized fields. However, cohort biases remain a significant challenge in machine learning. The narrow origin of data sampling or human annotators in cohorts is a prevalent issue for machine learning researchers due to its potential to induce bias in the final product. During the development of the CryptoLin corpus for another research project, the authors became concerned about the potential influence of cohort bias on the selection of annotators. Therefore, this paper addresses the question of whether cohort diversity improves the labeling result through the implementation of a repeated annotator process, involving two annotator cohorts and a statistically robust comparison methodology. The utilization of statistical tests, such as the Chi-Square Independence test for absolute frequency tables, and the construction of confidence intervals for Kappa point estimates, facilitates a rigorous analysis of the differences between Kappa estimates. Furthermore, the application of a two-proportion z-test to compare the accuracy scores of UTAD and IE annotators for various pre-trained models, including Vader Sentiment Analysis, TextBlob Sentiment Analysis, Flair NLP library, and FinBERT Financial Sentiment Analysis with BERT, contributes to the advancement of knowledge in this field. The paper utilizes Cryptocurrency Linguo (CryptoLin), a corpus containing 2683 cryptocurrency-related news articles spanning more than three years,and compares two different selection criteria for the annotators. CryptoLin was annotated twice with discrete values representing negative, neutral, and positive news respectively. The first annotation was done by twenty-seven annotators from the same cohort. Each news title was randomly assigned and blindly annotated by three human annotators. The second annotation was carried out by eighty-three annotators from three cohorts. Each news title was randomly assigned and blindly annotated by three human annotators, one in each different cohort. In both annotations, a consensus mechanism using simple voting was applied. The first annotation used the same cohort with students from the same nationality and background. The second used three cohorts with students from a very diverse set of nationalities and educational backgrounds. The results demonstrate that manual labeling done by both groups was acceptable according to inter-rater reliability coefficients Fleiss’s Kappa, Krippendorff’s Alpha, and Gwet’s AC1. Preliminary analysis utilizing Vader, Textblob, Flair, and FinBERT confirmed the utility of the data set labeling for further refinement of sentiment analysis algorithms. Our results also highlight that the more diverse annotator pool performed better in all measured aspects.

Jinghao Li, Hexiao Li, Na Cheng, Wuqing Zhang · 7 authors

With the prevalent adoption of blockchain in the financial system, there has been an increase in phishing scams on cryptocurrency platforms such as Ethereum, and an effective anomaly detection method is urgently required. The latest studies have focused on anomaly identification using natural language processing techniques or constructing simple static graphs. However, the existing methods are insufficient to convey the diversity of connectivity patterns in the Ethereum transaction network concerning amount and time. To this end, we proposed a novel transaction network embedding algorithm transE based on the multi-channel random walk to model the detection of Ethereum phishing scam accounts as a multigraph node classification task. Specifically, we first model the Ethereum transaction as a time-amount directed multigraph. Then, the hybrid feature representation of network nodes is learned via transE from their local and global neighbours, which uses the attention mechanism to maximize the probability of preserving node network neighbours. Ultimately, we employ visualization techniques and machine learning models to validate the effectiveness of the algorithms, and the model with the top performance is picked for Ethereum account classification. Experimental results indicate that the embedding vector extracted by transE improves the detection accuracy of Ethereum phishing accounts in the different classification tasks.

Duckworth, Peter

Distributed ledger technology (DLT) facilitates a new chapter for the internet, one known as Web3. It is a back-end upgrade (Voshmgir, 2020, p. 28) that drives particularly rapid innovation in the finance industry. Its trajectory will be in part determined by the role of developer communities, innovators, and technology companies. It will be shaped by government supervisors and decisions by policymakers on how to foster innovation, control risk, or compete internationally. It is no secret that the ideals of DLT developers regarding decentralization and democratization are partly social in nature; indeed, their efforts have been recognized as the ultimate form of protest (Russo, 2020). Despite this, discussion of DLT developers as belonging to social movements or civil society is a research gap in the academic literature. In seeking a better political context for the social efforts of DLT developers, the first contribution of this thesis is a definitional distinction between DLT Automation and DLT Activism. The null hypothesis that DLT activism must exist as a theme in DLT narratives, presented by the mainstream written media, is then developed. This is done by tying together interdisciplinary literature on DLT, banking, environmental and social governance, as well as civil society, and social movements. The importance of decision framing in the media is discussed in the research methods section, along with natural language processing techniques used to test the hypothesis. The research methodology begins with a multi-label classification prediction model built using machine-learning packages available in Python. Predicted descriptive labels for a large sample of articles from The Economist magazine suggest the null hypothesis should be rejected. This outcome is subsequently validated more comprehensively using ProQuest command-line queries and a larger sample, which suggests that at a 5% confidence level there is evidence to accept the expanded hypothesis; that DLT activism exists as a minor theme in mainstream DLT narratives. The thesis concludes with discussion about the risk of divided partisan views about DLT. Technology is used to create decentralized organizations and facilitate widespread contractual cooperation. One could say that political polarization regarding DLT could result in extreme collective organization within, rather than across, group lines thereby exacerbating social cleavages. As democracy faces the growing challenge of political polarization, the inclusive and open ecosystems that DLT communities have nurtured so far should be studied more deeply with a view to strengthening cross-cutting ties. DLT communities should not be left to develop in an isolated and insular manner.

Darius Galiş, Ciprian Pungilă, Viorel Negru

No abstract is available for this record.

Siddhant Gupta, Siddharth Sethi, Srishti Chaudhary, Anshul Arora

Android mobile devices are a prime target for a huge number of cyber-criminals as they aim to create malware for disrupting and damaging the servers, clients, or networks. Android malware are in the form of malicious apps, that get downloaded on mobile devices via the Play Store or third-party app markets. Such malicious apps pose serious threats like system damage, information leakage, financial loss to user, etc. Thus, predicting which apps contain malicious behavior will help in preventing malware attacks on mobile devices. Identifying Android malware has become a major challenge because of the ever-increasing number of permissions that applications ask for, to enhance the experience of the users. And most of the times, permissions and other features defined in normal and malicious apps are generally the same. In this paper, we aim to detect Android malware using machine learning, deep learning, and natural language processing techniques. To delve into the problem, we use the Android manifest files which provide us with features like permissions which become the basis for detecting Android malware. We have used the concept of information value for ranking permissions. Further, we have proposed a consensus-based blockchain framework for making more concrete predictions as blockchain have high reliability and low cost. The experimental results demonstrate that the proposed model gives the detection accuracy of 95.44% with the Random Forest classifier. This accuracy is achieved with top 45 permissions ranked according to Information Value.

Swarnadeep Saha, Prateek Yadav, Mohit Bansal

We focus on a type of linguistic formal reasoning where the goal is to reason\nover explicit knowledge in the form of natural language facts and rules (Clark\net al., 2020). A recent work, named PRover (Saha et al., 2020), performs such\nreasoning by answering a question and also generating a proof graph that\nexplains the answer. However, compositional reasoning is not always unique and\nthere may be multiple ways of reaching the correct answer. Thus, in our work,\nwe address a new and challenging problem of generating multiple proof graphs\nfor reasoning over natural language rule-bases. Each proof provides a different\nrationale for the answer, thereby improving the interpretability of such\nreasoning systems. In order to jointly learn from all proof graphs and exploit\nthe correlations between multiple proofs for a question, we pose this task as a\nset generation problem over structured output spaces where each proof is\nrepresented as a directed graph. We propose two variants of a proof-set\ngeneration model, multiPRover. Our first model, Multilabel-multiPRover,\ngenerates a set of proofs via multi-label classification and implicit\nconditioning between the proofs; while the second model, Iterative-multiPRover,\ngenerates proofs iteratively by explicitly conditioning on the previously\ngenerated proofs. Experiments on multiple synthetic, zero-shot, and\nhuman-paraphrased datasets reveal that both multiPRover models significantly\noutperform PRover on datasets containing multiple gold proofs.\nIterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios\nwhere all examples have single correct proofs. It also generalizes better to\nquestions requiring higher depths of reasoning where multiple proofs are more\nfrequent. Our code and models are publicly available at\nhttps://github.com/swarnaHub/multiPRover\n

Emmanuel Arrighi, Henning Fernau, Stefan Hoffmann, Markus Holzer · 7 authors

In the Intersection Non-Emptiness problem, we are given a list of finite automata $A_1,A_2,\dots,A_m$ over a common alphabet $Σ$ as input, and the goal is to determine whether some string $w\in Σ^*$ lies in the intersection of the languages accepted by the automata in the list. We analyze the complexity of the Intersection Non-Emptiness problem under the promise that all input automata accept a language in some level of the dot-depth hierarchy, or some level of the Straubing-Thérien hierarchy. Automata accepting languages from the lowest levels of these hierarchies arise naturally in the context of model checking. We identify a dichotomy in the dot-depth hierarchy by showing that the problem is already NP-complete when all input automata accept languages of the levels zero or one half and already PSPACE-hard when all automata accept a language from the level one. Conversely, we identify a tetrachotomy in the Straubing-Thérien hierarchy. More precisely, we show that the problem is in AC$^0$ when restricted to level zero; complete for LOGSPACE or NLOGSPACE, depending on the input representation, when restricted to languages in the level one half; NP-complete when the input is given as DFAs accepting a language in from level one or three half; and finally, PSPACE-complete when the input automata accept languages in level two or higher. Moreover, we show that the proof technique used to show containment in NP for DFAs accepting languages in the Straubing-Thérien hierarchy levels one ore three half does not generalize to the context of NFAs. To prove this, we identify a family of languages that provide an exponential separation between the state complexity of general NFAs and that of partially ordered NFAs. To the best of our knowledge, this is the first superpolynomial separation between these two models of computation.