Lei Pan

Bio: Lei Pan is an academic researcher from Deakin University. The author has contributed to research in topics: Computer science & Quantum entanglement. The author has an hindex of 17, co-authored 142 publications receiving 2205 citations. Previous affiliations of Lei Pan include University of Technology, Sydney & Anhui University.

Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

Abstract: With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m-bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding. To be exact, we employ entangled states prepared by m-bonacci sequences to detect eavesdropping. Meanwhile, we encode m-bonacci sequences in Lagrange interpolation polynomials to generate the shares of a secret with reverse Huffman-Fibonacci-tree coding. The advantages of the proposed scheme is that it can detect eavesdropping without joint quantum operations, and permits secret sharing for an arbitrary but no less than threshold-value number of classical participants with much lower bandwidth. Also, in comparison with existing quantum secret sharing schemes, it still works when there are dynamic changes, such as the unavailability of some quantum channel, the arrival of new participants and the departure of participants. Finally, we provide security analysis of the new hybrid quantum secret sharing scheme and discuss its useful features for modern applications.

Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

Abstract: With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m-bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding. To be exact, we employ entangled states prepared by m -bonacci sequences to detect eavesdropping. Meanwhile, we encode m -bonacci sequences in Lagrange interpolation polynomials to generate the shares of a secret with reverse Huffman-Fibonacci-tree coding. The advantages of the proposed scheme is that it can detect eavesdropping without joint quantum operations, and permits secret sharing for an arbitrary but no less than threshold-value number of classical participants with much lower bandwidth. Also, in comparison with existing quantum secret sharing schemes, it still works when there are dynamic changes, such as the unavailability of some quantum channel, the arrival of new participants and the departure of participants. Finally, we provide security analysis of the new hybrid quantum secret sharing scheme and discuss its useful features for modern applications.

A Survey of Android Malware Detection with Deep Neural Models

Abstract: Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber security research. Deep learning models have many advantages over traditional Machine Learning (ML) models, particularly when there is a large amount of data available. Android malware detection or classification qualifies as a big data problem because of the fast booming number of Android malware, the obfuscation of Android malware, and the potential protection of huge values of data assets stored on the Android devices. It seems a natural choice to apply DL on Android malware detection. However, there exist challenges for researchers and practitioners, such as choice of DL architecture, feature extraction and processing, performance evaluation, and even gathering adequate data of high quality. In this survey, we aim to address the challenges by systematically reviewing the latest progress in DL-based Android malware detection and classification. We organize the literature according to the DL architecture, including FCN, CNN, RNN, DBN, AE, and hybrid models. The goal is to reveal the research frontier, with the focus on representing code semantics for Android malware detection. We also discuss the challenges in this emerging field and provide our view of future research opportunities and directions.

Cross-Project Transfer Representation Learning for Vulnerable Function Discovery

Abstract: Machine learning is now widely used to detect security vulnerabilities in the software, even before the software is released. But its potential is often severely compromised at the early stage of a software project when we face a shortage of high-quality training data and have to rely on overly generic hand-crafted features. This paper addresses this cold-start problem of machine learning, by learning rich features that generalize across similar projects. To reach an optimal balance between feature-richness and generalizability, we devise a data-driven method including the following innovative ideas. First, the code semantics are revealed through serialized abstract syntax trees (ASTs), with tokens encoded by Continuous Bag-of-Words neural embeddings. Next, the serialized ASTs are fed to a sequential deep learning classifier (Bi-LSTM) to obtain a representation indicative of software vulnerability. Finally, the neural representation obtained from existing software projects is then transferred to the new project to enable early vulnerability detection even with a small set of training labels. To validate this vulnerability detection approach, we manually labeled 457 vulnerable functions and collected 30 000+ nonvulnerable functions from six open-source projects. The empirical results confirmed that the trained model is capable of generating representations that are indicative of program vulnerability and is adaptable across multiple projects. Compared with the traditional code metrics, our transfer-learned representations are more effective for predicting vulnerable functions, both within a project and across multiple projects.

Security Analysis Methods on Ethereum Smart Contract Vulnerabilities: A Survey.

Abstract: Smart contracts are software programs featuring both traditional applications and distributed data storage on blockchains. Ethereum is a prominent blockchain platform with the support of smart contracts. The smart contracts act as autonomous agents in critical decentralized applications and hold a significant amount of cryptocurrency to perform trusted transactions and agreements. Millions of dollars as part of the assets held by the smart contracts were stolen or frozen through the notorious attacks just between 2016 and 2018, such as the DAO attack, Parity Multi-Sig Wallet attack, and the integer underflow/overflow attacks. These attacks were caused by a combination of technical flaws in designing and implementing software codes. However, many more vulnerabilities of less severity are to be discovered because of the scripting natures of the Solidity language and the non-updateable feature of blockchains. Hence, we surveyed 16 security vulnerabilities in smart contract programs, and some vulnerabilities do not have a proper solution. This survey aims to identify the key vulnerabilities in smart contracts on Ethereum in the perspectives of their internal mechanisms and software security vulnerabilities. By correlating 16 Ethereum vulnerabilities and 19 software security issues, we predict that many attacks are yet to be exploited. And we have explored many software tools to detect the security vulnerabilities of smart contracts in terms of static analysis, dynamic analysis, and formal verification. This survey presents the security problems in smart contracts together with the available analysis tools and the detection methods. We also investigated the limitations of the tools or analysis methods with respect to the identified security vulnerabilities of the smart contracts.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Teaching for quality learning at university

Abstract: by J. Biggs and C. Tang, Maidenhead, England, Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4