Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

/pdf/cyber-risk-at-the-edge-current-and-future-trends-on-cyber-2admm410oq.pdf

Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

/pdf/cyber-risk-at-the-edge-current-and-future-trends-on-cyber-31at96uvey.pdf

Cyber Risk at the Edge: Current and future trends on Cyber Risk Analytics and Artificial Intelligence in the Industrial Internet of Things and Industry 4.0 Supply Chains

This paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

/pdf/artificial-intelligence-and-the-internet-of-things-in-5c572d5vt7.pdf

Artificial Intelligence and the Internet of Things in Industry 4.0

Digital technologies have changed the way supply chain operations are structured. In this article, we develop design principles to show determining factors for an Internet-of-Things approach within Supply Chain Management. From the design principles, the article derives a new model for the Industrial Internet of Things supply chains. The focus is on Small and Medium Enterprises (SMEs). This research design results in a new process of compounding knowledge from existing supply chain models and adapting the cumulative findings to the concept of supply chains in the Industrial Internet of Things. The paper outlines the design principles for developing cognition in the process of integrating SME’s digital supply chains in the Industrial Internet of Things (IIoT) and the Industry 4.0 (I4.0).

/pdf/supply-chain-design-for-the-industrial-internet-of-things-345cv28ygi.pdf

Supply Chain Design for the Industrial Internet of Things and the Industry 4.0

The only few existing methods for network topology boundary routing IP identification are often based on a single network characteristic, with poor applicability, low accuracy, and difficult to meet the IP geolocation requirements. This paper proposes a network topology boundary routing IP identification algorithm for IP geolocation. The proposed algorithm is no longer limited to a specific delay distribution used in D-Based, but identifies the boundary routing IP according to the difference between each single-hop delay in the path and the city delay threshold; more different from traditional methods, when the delay difference characteristic is not obvious, the proposed algorithm combines the differences of routing IP hostnames in different cities to further identify the boundary routing IP and the proportion of identifiable paths is increased. Experiments are carried out using the probing results of millions of landmarks in 10 target cities in China and the US. Results show that compared with D-Based and P-Based, the ratio of identifiable paths is increased by about 103.0% and 69.5%; the number of nodes in the target city is about 1.7 and 1.9 times, and the accuracy of the node location is improved by about 48.3% and 16.9%; using the topology obtained by the proposed algorithm, the success rate of three classic street-level IP geolocation methods such as SLG, LENCR, and Geo-RMP is increased by 75.8%, 61.2%, and 71.7%.

Network Topology Boundary Routing IP Identification for IP Geolocation

As one of the most notorious programming errors, memory access errors still hurt modern software security. Particularly, they are hidden deeply in important software systems written in memory unsafe languages like C/C++. Plenty of work have been proposed to detect bugs leading to memory access errors. However, all existing works lack the ability to handle two challenges. First, they are not able to tackle fine-grained memory access errors, e.g., data overflow inside one data structure. These errors are usually overlooked for a long time since they happen inside one memory block and do not lead to program crash. Second, most existing works rely on source code or debugging information to recover memory boundary information, so they cannot be directly applied to detection of memory access errors in binary code. However, searching memory access errors in binary code is a very common scenario in software vulnerability detection and exploitation. In order to overcome these challenges, we propose Memory Access Integrity (MAI), a dynamic method to detect fine-grained memory access errors in off-the-shelf binary executables. The core idea is to recover fine-grained accessing policy between memory access behaviors and memory ranges, and then detect memory access errors based on the policy. The key insight in our work is that memory accessing patterns reveal information for recovering the boundary of memory objects and the accessing policy. Based on these recovered information, our method maintains a new memory model to simulate the life cycle of memory objects and report errors when any accessing policy is violated. We evaluate our tool on popular CTF datasets and real world softwares. Compared with the state of the art detection tool, the evaluation result demonstrates that our tool can detect fine-grained memory access errors effectively and efficiently. As the practical impact, our tool has detected three 0-day memory access errors in an audio decoder.

/pdf/memory-access-integrity-detecting-fine-grained-memory-access-40r76gn8an.pdf

Memory access integrity: detecting fine-grained memory access errors in binary code

COMRace: Detecting Data Race Vulnerabilities in COM Objects

Counter-fact is the highest form of causal reasoning. Focusing on counter-fact will provide new ideas for higher order causal reasoning. For counterfactual text detection, two detection methods are proposed. One is the automatic detection method based on classification model, that is, counterfactual detection is carried out on the given annotated text data. In this way, the data imbalance problem is solved first, then word vector is generated by doc2vec, and finally the binary SVM training model is adopted.The second is automatic detection method based on sequence annotation model, namely the counterfactual statements of reason and result orientation, to this sequence labeling method is adopted, the individual words in the text annotation, after processing the data to be included in the BiLSTM after training, and through a layer of conditions with the airport, the resulting text labeling information. Experiments show that the proposed method can accurately detect and locate the cause and result in the counterfactual text.

Research on automatic detection of text-oriented counterfactual statements

Sheep's Clothing, Wolf's Data: Detecting Server-Induced Client Vulnerabilities in IPC

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Fangming gu

Author Tools

Chat about Author

Papers

Memory access integrity: detecting fine-grained memory access errors in binary code

COMRace: Detecting Data Race Vulnerabilities in COM Objects

Research on automatic detection of text-oriented counterfactual statements

Sheep's Clothing, Wolf's Data: Detecting Server-Induced Client Vulnerabilities in IPC