Biblio

In order to meet the needs of intellectual property protection and controlled sharing of scientific research sensitive data, a mechanism is proposed for security protection throughout “transfer, store and use” process of sensitive data which based on blockchain. This blockchain bottom layer security is reinforced. First, the encryption algorithm used is replaced by the national secret algorithm and the smart contract is encapsulated as API at the gateway level. Signature validation is performed when the API is used to prevent illegal access. Then the whole process of data up-chain, storage and down-chain is encrypted, and a mechanism of data structure query and data query condition construction based on blockchain smart is provided to ensure that the data is “usable and invisible”. Finally, data access control is ensured through role-based and hierarchical protection, and the blockchain base developed has good extensibility, which can meet the requirement of sensitive data security protection in scientific research filed and has broad application prospects.

Hardware IPs are assumed to be roots-of-trust in complex SoCs. However, their design and security verification are still heavily dependent on manual expertise. Extensive research in this domain has shown that even cryptographic modules may lack information flow security, making them susceptible to remote attacks. Further, when an SoC is in the hands of the attacker, physical attacks such as fault injection are possible. This paper introduces EISec, a novel tool utilizing symbolic execution for exhaustive analysis of hardware IPs. EISec operates at the pre-silicon stage on the gate level netlist of a design. It detects information flow security violations and generates the exhaustive set of control sequences that reproduces them. We further expand its capabilities to quantify the confusion and diffusion present in cryptographic modules and to analyze an FSM's susceptibility to fault injection attacks. The proposed methodology efficiently explores the complete input space of designs utilizing symbolic execution. In short, EISec is a holistic security analysis tool to help hardware designers capture security violations early on and mitigate them by reporting their triggers.

Artificial intelligence creation comes into fashion and has brought unprecedented challenges to intellectual property law. In order to study the viewpoints of AI creation copyright ownership from professionals in different institutions, taking the papers of AI creation on CNKI from 2016 to 2021, we applied orthogonal design and analysis of variance method to construct the dataset. A kernel-SVM classifier with different kernel methods in addition to some shallow machine learning classifiers are selected in analyzing and predicting the copyright ownership of AI creation. Support vector machine (svm) is widely used in statistics and the performance of SVM method is closely related to the choice of the kernel function. SVM with RBF kernel surpasses the other seven kernel-SVM classifiers and five shallow classifier, although the accuracy provided by all of them was not satisfactory. Various performance metrics such as accuracy, F1-score are used to evaluate the performance of KSVM and other classifiers. The purpose of this study is to explore the overall viewpoints of AI creation copyright ownership, investigate the influence of different features on the final copyright ownership and predict the most likely viewpoint in the future. And it will encourage investors, researchers and promote intellectual property protection in China.

The rapid improvement of computer and network technology not only promotes the improvement of productivity and facilitates people's life, but also brings new threats to production and life. Cyberspace security has attracted more and more attention. Different from traditional cyberspace security, APT attacks on key networks or infrastructure, with the main goal of stealing intellectual property, confidential information or sabotage, seriously threatening the interests and security of governments, enterprises and scientific research institutions. Timely detection and blocking is particularly important. The purpose of this paper is to study the security of software supply chain in power industry based on BAS technology. The experimental data shows that Type 1 projects account for the least amount and Type 2 projects account for the highest proportion. Type 1 projects have high unit price contracts and high profits, but the number is small and the time for signing orders is long.

Due to its decentralized trust mechanism, blockchain is increasingly used as a trust intermediary for multi-party cooperation to reduce the cost and risk of maintaining centralized trust nowadays. And as the requirements for privacy and high throughput, consortium blockchain is widely used in data sharing and business cooperation in practical application scenarios. Nowadays, the protection of traditional medicine has been regarded as human intangible cultural heritage in recent years, but this kind of protection still faces the problem that traditional medicine prescriptions are unsuitable for disclosure and difficult to protect. Hyperledger is a consortium blockchain featuring authorized access, high throughput, and tamper-resistance, making it ideal for privacy protection and information depository in traditional medicine protection. This study proposes a solution for intellectual property protection of traditional medicine by using a blockchain platform to record prescription iterations and clinical trial data. The privacy and confidentiality of Hyperledger can keep intellectual property information safe and private. In addition, the author proposes to invite the Patent Offices and legal institutions to join the blockchain network, maintain users' properties and issue certificates, which can provide a legal basis for rights protection when infringement occurs. Finally, the researchers have built a system corresponding to the scheme and tested the system. The test outcomes of the system can explain the usability of the system. And through the test of system throughput, under low system configuration, it can reach about 200 query operations per second, which can meet the application requirements of relevant organizations and governments.

[Purpose/meaning] In this paper, a unified scheme based on blockchain technology to realize the three modules of intellectual property confirmation, utilization, and protection of rights at the application layer is constructed, to solve the problem of unbalanced and inadequate resource distribution and development level in the field of industrial intellectual property. [Method/process] Based on the application of the core technology of blockchain in the field of intellectual property, this paper analyzes the pain points in the current field of intellectual property, and selects matching blockchain types according to the protection of intellectual property and the different decisions involved in the transaction process, to build a heterogeneous multi-chain model based on blockchain technology. [Conclusion] The heterogeneous multi-chain model based on Polkadot[1] network is proposed to realize the intellectual property protection scheme of a heterogeneous multi-chain model, to promote collaborative design and product development between regions, and to make up for the shortcomings of technical exchange, and weaken the phenomenon of "information island" in a certain extent. [Limitation/deficiency] The design of smart contracts in the field of intellectual property, the development of cross-chain protocols, and the formulation of national standards for blockchain technology still need to be developed and improved. At the same time, the intellectual property protection model designed in this paper needs to be verified in the application of practical cases.

Smart contracts are an attractive aspect of blockchain technology. A smart contract is a piece of executable code that runs on top of the blockchain and is used to facilitate, execute, and enforce agreements between untrustworthy parties without the need for a third party. This paper offers a review of the literature on smart contract applications in intellectual property management. The goal is to look at technology advancements and smart contract deployment in this area. The theoretical foundation of many papers published in recent years is used as a source of theoretical and implementation research for this purpose. According to the literature review we conducted, smart contracts function automatically, control, or document legally significant events and activities in line with the contract agreement's terms. This is a relatively new technology that is projected to deliver solutions for trust, security, and transparency across a variety of areas. An exploratory strategy was used to perform this literature review.

The goals, objectives and criteria of the effectiveness of the creation, maintenance and use of the Digital Information Fund of Intellectual Property (DIFIP) are considered. A formalized methodology is proposed for designing DIFIPs, increasing its efficiency and quality, based on a set of interconnected models, methods and algorithms for analysis, synthesis and normalization distributed information management of DIFIP's structure; classification of databases users of patent and scientific and technical information; synthesis of optimal logical structures of the DIFIP database and thematic databases; assessing the quality of the database and ensuring the required level of data security.

In software engineering, the aspect of addressing security requirements is considered to be of paramount importance. In most cases, however, security requirements for a system are considered as non-functional requirements (NFRs) and are addressed at the very end of the software development life cycle. The increasing number of security incidents in software systems around the world has made researchers and developers rethink and consider this issue at an earlier stage. An important and essential step towards this process is the elicitation of relevant security requirements. In a recent work, Imtiaz et al. proposed a framework for creating a mapping between existing requirements and the vulnerabilities associated with them. The idea is that, this mapping can be used by developers to predict potential vulnerabilities associated with new functional requirements and capture security requirements to avoid these vulnerabilities. However, to what extent, such existing vulnerability information can be useful in security requirements elicitation is still an open question. In this paper, we design a human subject study to answer this question. We also present the results of a pilot study and discuss their implications. Preliminary results show that existing vulnerability information can be a useful resource in eliciting security requirements and lays ground work for a full scale study.

In Production System Engineering (PSE), domain experts from different disciplines reuse assets such as products, production processes, and resources. Therefore, PSE organizations aim at establishing reuse across engineering disciplines. However, the coordination of multi-disciplinary reuse tasks, e.g., the re-validation of related assets after changes, is hampered by the coarse-grained representation of tasks and by scattered, heterogeneous domain knowledge. This paper introduces the Multi-disciplinary Reuse Coordination (MRC) artifact to improve task management for multi-disciplinary reuse. For assets and their properties, the MRC artifact describes sub-tasks with progress and result states to provide references for detailed reuse task management across engineering disciplines. In a feasibility study on a typical robot cell in automotive manufacturing, we investigate the effectiveness of task management with the MRC artifact compared to traditional approaches. Results indicate that the MRC artifact is feasible and provides effective capabilities for coordinating multi-disciplinary re-validation after changes.

Evaluating the security gains brought by software diversity is one key issue of software diversity research, but the existing software diversity evaluation methods are generally based on conventional code features and are relatively single, which are difficult to accurately reflect the security gains brought by software diversity. To solve these problems, from the perspective of return-oriented programming (ROP) attack, we present a software diversity evaluation method which integrates metrics for the quality and distribution of gadgets. Based on the proposed evaluation method and SpiderMonkey JavaScript engine, we implement a software diversity evaluation system for compiled languages and script languages. Diversity techniques with different granularities are used to test. The evaluation results show that the proposed evaluation method can accurately and comprehensively reflect the security gains brought by software diversity.

Common Vulnerabilities and Exposures (CVE) databases contain information about vulnerabilities of software products and source code. If individual elements of CVE descriptions can be extracted and structured, then the data can be used to search and analyze CVE descriptions. Herein we propose a method to label each element in CVE descriptions by applying Named Entity Recognition (NER). For NER, we used BERT, a transformer-based natural language processing model. Using NER with machine learning can label information from CVE descriptions even if there are some distortions in the data. An experiment involving manually prepared label information for 1000 CVE descriptions shows that the labeling accuracy of the proposed method is about 0.81 for precision and about 0.89 for recall. In addition, we devise a way to train the data by dividing it into labels. Our proposed method can be used to label each element automatically from CVE descriptions.

Successive approximation register analog-to-digital converter (SAR ADC) is widely adopted in the Internet of Things (IoT) systems due to its simple structure and high energy efficiency. Unfortunately, SAR ADC dissipates various and unique power features when it converts different input signals, leading to severe vulnerability to power side-channel attack (PSA). The adversary can accurately derive the input signal by only measuring the power information from the analog supply pin (AVDD), digital supply pin (DVDD), and/or reference pin (Ref) which feed to the trained machine learning models. This paper first presents the detailed mathematical analysis of power side-channel attack (PSA) to SAR ADC, concluding that the power information from AVDD is the most vulnerable to PSA compared with the other supply pin. Then, an LSB-reused protection technique is proposed, which utilizes the characteristic of LSB from the SAR ADC itself to protect against PSA. Lastly, this technique is verified in a 12-bit 5 MS/s secure SAR ADC implemented in 65nm technology. By using the current waveform from AVDD, the adopted convolutional neural network (CNN) algorithms can achieve \textgreater99% prediction accuracy from LSB to MSB in the SAR ADC without protection. With the proposed protection, the bit-wise accuracy drops to around 50%.

The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology

Code-reuse attacks (including ROP/JOP) severely threaten computer security. Control-flow integrity (CFI), which can restrict control flow in legal scope, is recognised as an effective defence mechanism against code-reuse attacks. Hardware-based CFI uses Instruction Set Architecture (ISA) extensions with additional hardware modules to implement CFI and achieve better performance. However, hardware-based fine-grained CFI adds new instructions to the ISA, which can not be executed on old processors and breaks the compatibility of programs. Some coarse-grained CFI designs, such as Intel IBT, maintain the compatibility of programs but can not provide enough security guarantees.To balance the security and compatibility of hardware CFI, we propose Transparent Forward CFI (TFCFI). TFCFI implements hardware-based fine-grained CFI designs without changing the ISA. The software modification of TFCFI utilizes address information and hint instructions in RISC-V as transparent labels to mark the program. The hardware module of TFCFI monitors the control flow during execution. The program modified by TFCFI can be executed on old processors without TFCFI. Benefiting from transparent labels, TFCFI also solves the destination equivalence problem. The experiment on FPGA shows that TFCFI incurs negligible performance overhead (1.82% on average).

With the development of software defined network and network function virtualization, network operators can flexibly deploy service function chains (SFC) to provide network security services more than before according to the network security requirements of business systems. At present, most research on verifying the correctness of SFC is based on whether the logical sequence between service functions (SF) in SFC is correct before deployment, and there is less research on verifying the correctness after SFC deployment. Therefore, this paper proposes a method of using Colored Petri Net (CPN) to establish a verification model offline and verify whether each SF deployment in SFC is correct after online deployment. After the SFC deployment is completed, the information is obtained online and input into the established model for verification. The experimental results show that the SFC correctness verification method proposed in this paper can effectively verify whether each SF in the deployed SFC is deployed correctly. In this process, the correctness of SF model is verified by using SF model in the model library, and the model reuse technology is preliminarily discussed.

At present, code reuse attacks, such as Return Oriented Programming (ROP), execute attacks through the code of the application itself, bypassing the traditional defense mechanism and seriously threatening the security of computer software. The existing two mainstream defense mechanisms, Address Space Layout Randomization (ASLR), are vulnerable to information disclosure attacks, and Control-Flow Integrity (CFI) will bring high overhead to programs. At the same time, due to the widespread use of software of unknown origin, there is no source code provided or available, so it is not always possible to secure the source code. In this paper, we propose FRCFI, an effective method based on binary rewriting to prevent code reuse attacks. FRCFI first disrupts the program's memory space layout through function shuffling and NOP insertion, then verifies the execution of the control-flow branch instruction ret and indirect call/jmp instructions to ensure that the target address is not modified by attackers. Experiment show shows that FRCFI can effectively defend against code reuse attacks. After randomization, the survival rate of gadgets is only 1.7%, and FRCFI adds on average 6.1% runtime overhead on SPEC CPU2006 benchmark programs.

From an information-theoretic standpoint, the intrusion detection process can be examined. Given the IDS output(alarm data), we should have less uncertainty regarding the input (event data). We propose the Capability of Intrusion Detection (CID) measure, which is simply the ratio of mutual information between IDS input and output, and the input of entropy. CID has the desirable properties of (1) naturally accounting for all important aspects of detection capability, such as true positive rate, false positive rate, positive predictive value, negative predictive value, and base rate, (2) objectively providing an intrinsic measure of intrusion detection capability, and (3) being sensitive to IDS operation parameters. When finetuning an IDS, we believe that CID is the best performance metric to use. In terms of the IDS’ inherent ability to classify input data, the so obtained operation point is the best that it can achieve.

Artificial Intelligence (AI) technology is developing rapidly, permeating every aspect of human life. Although the integration between AI and communication contributes to the flourishing development of wireless communication, it induces severer security problems. As a supplement to the upper-layer cryptography protocol, physical layer security has become an intriguing technology to ensure the security of wireless communication systems. However, most of the current physical layer security research does not consider the intelligence and mobility of collusive eavesdroppers. In this paper, we consider a MIMO system model with a friendly intelligent jammer against multiple collusive intelligent eavesdroppers, and zero-sum game is exploited to formulate the confrontation of them. The Nash equilibrium is derived by convex optimization and alternative optimization in the free-space scenario of a single user system. We propose a zero-sum game deep learning algorithm (ZGDL) for general situations to solve non-convex game problems. In terms of the effectiveness, simulations are conducted to confirm that the proposed algorithm can obtain the Nash equilibrium.

This paper assesses the impact on the performance that information-theoretic physical layer security (IT-PLS) introduces when integrated into a 5G New Radio (NR) system. For this, we implement a wiretap code for IT-PLS based on a modular coding scheme that uses a universal-hash function in its security layer. The main advantage of this approach lies in its flexible integration into the lower layers of the 5G NR protocol stack without affecting the communication's reliability. Specifically, we use IT-PLS to secure the transmission of downlink control information by integrating an extra pre-coding security layer as part of the physical downlink control channel (PDCCH) procedures, thus not requiring any change of the 3GPP 38 series standard. We conduct experiments using a real-time open-source 5G NR standalone implementation and use software-defined radios for over-the-air transmissions in a controlled laboratory environment. The overhead added by IT-PLS is determined in terms of the latency introduced into the system, which is measured at the physical layer for an end-to-end (E2E) connection between the gNB and the user equipment.

Physical layer (PHY) security in decode-and-forward (DF) relay systems is discussed. Based on the types of wiretap links, the secrecy performance of three typical secure DF relay models is analyzed. Different from conventional works in this field, rigorous derivations of the secrecy channel capacity are provided from an information-theoretic perspective. Meanwhile, closed-form expressions are derived to characterize the secrecy outage probability (SOP). For the sake of unveiling more system insights, asymptotic analyses are performed on the SOP for a sufficiently large signal-to-noise ratio (SNR). The analytical results are validated by computer simulations and are in excellent agreement.

Measuring the information leakage is critical for evaluating the practical security of cryptographic devices against side-channel analysis. Information-theoretic measures can be used (along with Fano’s inequality) to derive upper bounds on the success rate of any possible attack in terms of the number of side-channel measurements. Equivalently, this gives lower bounds on the number of queries for a given success probability of attack. In this paper, we consider cryptographic implementations protected by (first-order) masking schemes, and derive several information-theoretic bounds on the efficiency of any (second-order) attack. The obtained bounds are generic in that they do not depend on a specific attack but only on the leakage and masking models, through the mutual information between side-channel measurements and the secret key. Numerical evaluations confirm that our bounds reflect the practical performance of optimal maximum likelihood attacks.

A multi-designated receiver authentication code (MDRA-code) with information-theoretic security is proposed as an extension of the traditional multi-receiver authentication code. The purpose of the MDRA-code is to securely transmit a message via a broadcast channel from a single sender to an arbitrary subset of multiple receivers that have been designated by the sender, and only the receivers in the subset (i.e., not all receivers) should accept the message if an adversary is absent. This paper proposes a model and security formalization of MDRA-codes, and provides constructions of MDRA-codes.

All-or-nothing transforms (AONT) were proposed by Rivest as a message preprocessing technique for encrypting data to protect against brute-force attacks, and have many applications in cryptography and information security. Later the unconditionally secure AONT and their combinatorial characterization were introduced by Stinson. Informally, a combinatorial AONT is an array with the unbiased requirements and its security properties in general depend on the prior probability distribution on the inputs s-tuples. Recently, it was shown by Esfahani and Stinson that a combinatorial AONT has perfect security provided that all the inputs s-tuples are equiprobable, and has weak security provided that all the inputs s-tuples are with non-zero probability. This paper aims to explore on the gap between perfect security and weak security for combinatorial (t, s, v)-AONTs. Concretely, we consider the typical scenario that all the s inputs take values independently (but not necessarily identically) and quantify the amount of information H(\textbackslashmathcalX\textbackslashmid \textbackslashmathcalY) about any t inputs \textbackslashmathcalX that is not revealed by any s−t outputs \textbackslashmathcalY. In particular, we establish the general lower and upper bounds on H(\textbackslashmathcalX\textbackslashmid \textbackslashmathcalY) for combinatorial AONTs using information-theoretic techniques, and also show that the derived bounds can be attained in certain cases.

Building occupancy data helps increase energy management systems’ performance, enabling lower energy use while preserving occupant comfort. The focus of this study is employing environmental data (e.g., including but not limited to temperature, humidity, carbon dioxide (CO2), etc.) to infer occupancy information. This will be achieved by exploring the application of information theory metrics with machine learning (ML) approaches to classify occupancy levels for a given dataset. Three datasets and six distinct ML algorithms were used in a comparative study to determine the best strategy for identifying occupancy patterns. It was determined that both k-nearest neighbors (kNN) and random forest (RF) identify occupancy labels with the highest overall level of accuracy, reaching 97.99% and 98.56%, respectively.