In recent years a number of large-scale triple-oriented knowledge graphs have been generated and various models have been proposed to perform learning in those graphs. Most knowledge graphs are static and reflect the world in its current state. In reality, of course, the state of the world is changing: a healthy person becomes diagnosed with a disease and a new president is inaugurated. In this paper, we extend models for static knowledge graphs to temporal knowledge graphs. This enables us to store episodic data and to generalize to new facts (inductive learning). We generalize leading learning models for static knowledge graphs (i.e., Tucker, RESCAL, HolE, ComplEx, DistMult) to temporal knowledge graphs. In particular, we introduce a new tensor model, ConT, with superior generalization performance. The performances of all proposed models are analyzed on two different datasets: the Global Database of Events, Language, and Tone (GDELT) and the database for Integrated Conflict Early Warning System (ICEWS). We argue that temporal knowledge graph embeddings might be models also for cognitive episodic memory (facts we remember and can recollect) and that a semantic memory (current facts we know) can be generated from episodic memory by a marginalization operation. We validate this episodic-to-semantic projection hypothesis with the ICEWS dataset.

Embedding Models for Episodic Knowledge Graphs

Cybersecurity knowledge graph enabled attack chain detection for cyber-physical systems

Critical infrastructure must withstand cyber and physical assaults in today's interconnected world. This study explores innovative strategies to shield these systems from cyber-physical threats. As networks become more connected, cyberattacks grow more sophisticated, making it imperative to safeguard sectors like transportation, energy, water, and information. The paper analyzes the present and future threat landscape, identifying vulnerabilities within critical infrastructures and proposing targeted mitigation strategies to enhance security. The research leverages AI and machine learning to develop detection tools that identify and predict cyber-physical attacks, enhancing response times and preventive measures. Additionally, resilience engineering and architecture are crucial in fortifying infrastructure, enabling it to withstand and recover from attacks while maintaining essential functions. The study also reviews legislation and policies surrounding infrastructure protection, pinpointing shortcomings and recommending improvements to bolster national security. Effective countermeasures against cyber-physical threats require collaboration and information sharing among government bodies, industry stakeholders, and educational institutions. These partnerships facilitate the exchange of knowledge, best practices, and tools to address threats efficiently. Furthermore, the paper highlights the importance of training programs that equip professionals with skills to integrate cyber and physical security defenses. Exploring the integration of blockchain, IoT, and autonomous technologies could further enhance the resilience of critical systems. These technologies foster secure communications and automated responses to incidents. Lastly, community awareness initiatives play a vital role in preparing for and mitigating cyber-physical attacks, ensuring that public readiness and resilience are maintained. This comprehensive approach aims to fortify critical infrastructure against evolving cyber threats, ensuring continuous operation and security. 

Innovative solutions for critical infrastructure resilience against cyber-physical attacks

In this research, we delve into an exhaustive ex- amination of the usage of machine learning (ML) models for the identification of cyber threats within Cloud-Based Intrusion Detection Systems (IDS). With the escalating dependence on cloud services across various industries, the urgency to develop effective and resilient IDS to mitigate burgeoning cyber risks is paramount. Our study makes a significant contribution to this imperative field by analyzing the efficacy of diverse ML models in detecting cyber-attacks within a cloud context. We scrutinized an array of ML models, namely Decision Trees (DT), Random Forest (RF), XGBoost, and Support Vector Machines (SVM), utilizing key performance parameters such as accuracy, recall, precision, F1-s, and confusion matrix to understand their practical application in real-world IDS scenarios. XGBoost stood out as the most proficient model, showcasing not only an impressive accuracy but also a balanced performance in terms of precision and recall. This highlights the considerable potential of ensemble and gradient boosting techniques in optimizing cloud- based IDS detection capabilities. Our findings underscore the significant role of machine learning in fostering more dependable, robust, and efficient IDS in the cloud, thus significantly aiding in securing our digital ecosystems. 

pdf/cyber-attack-detection-for-cloud-based-intrusion-detection-3kj571bcrb.pdf

Cyber-Attack Detection for Cloud-Based Intrusion Detection Systems

With the continuous emergence of new applications such as holographic multimedia, integration of space, air and ground, metaverse and social Internet of things, the development of business applications moved towards digitalization, integration, bandwidth and intelligence, which correspondingly puts forward new demands for the transformation of the network.Through the research and analysis of future network characteristics, combined with the characteristics of the cloud native network, the cloud native advantage can be used to meet the needs of current hot business applications.The general architecture of the cloud native network was proposed.The capabilities of data plane, control plane and management plane were defined.Finally, the typical and hot application cases of the cloud native network were given.

Cloud Native Network Creating a New Era of Intelligent Application

Cyberspace has been threatened by attacks ever since its birth. With the development of the information technologies, especially big data and artificial intelligence, many kinds of cyber attacks are emerging every day, causing severe consequences to society. Meanwhile, intelligent defense methods are proposed to detect these attacks. Such attack and defense methods are constantly being renovated. In particular, advanced persistent threats are intensifying. How to effectively prevent this type of attack has become the a vital problem in recent years. The detection and defense technologies have made great progress. This chapter mainly discusses the research progress of APT attack detection and defense strategies at home and abroad, and focuses on the practice of using machine learning methods to perform attack detection while elaborating on traditional attack detection methods. We also introduce game theory based defense strategy to find the best defense strategy in limited resources, dynamic information flow tracking and cloud platform. With the development of knowledge representation, how to use the MDATA model to characterize the APT attacks is also be discussed in this chapter.

Detection and Defense Methods of Cyber Attacks

This chapter introduces the application of information cascading analysis in social networks. We present a deep learning based framework of social network information cascade analysis, and we show the challenges of applying the MDATA model. The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data have become overwhelmed in SNICA. At the same time, the MDATA model fuses data from multiple sources in a graph, which can be applied to the SNICA problems. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. This chapter provides a comprehensive survey of recent progress in applying deep learning techniques for SNICA.

Information Cascading in Social Networks

Knowledge is human’s high-level understanding and summary of massive data. Intelligence is based on knowledge, and many works aim at representing knowledge and understanding intelligence. Although many models could represent static knowledge efficiently, it is still difficult to represent dynamic knowledge, especially which changes with time and space factors. In this chapter, we introduce a new knowledge representation model, Multi-dimensional Data Association and inTelligent Analysis (MDATA for short). We introduce three main parts in the MDATA model, knowledge representation, knowledge acquisition, and knowledge usage. We also discuss some potential applications that MDATA could be adopted and works greatly to improve the efficiency by the stronger ability of representing knowledge.

Introduction to the MDATA Model

The cyberspace has expanded from traditional internet to ubiquitous cyberspace which interconnects human, machines, things, services, and applications. The computing paradigm is also shifting from centralized computing in the cloud to combined computing in the front end, middle layer, and cloud. Therefore, traditional computing paradigms such as cloud computing and edge computing can no longer satisfy the evolving computing needs of big data in ubiquitous cyberspace. This paper presents a computing architecture named Fogcloud Computing for big data in ubiquitous cyberspace. Collaborative computing by multiple knowledge actors in the fog, middle layer, and cloud is realized based on the collaborative computing language and models, thereby providing a solution for big data computing in ubiquitous cyberspace.

/pdf/software-architecture-of-fogcloud-computing-for-big-data-in-wfozkax4vw.pdf

Software Architecture of Fogcloud Computing for Big Data in Ubiquitous Cyberspace

Information security is an important part of Internet security. As more and more industries rely on the Internet, it has become urgent to protect information security of these industries, spawned local area networks (LANs), intranets and so on. With the development of information sensor technology, the Internet of Things (IoT) that interconnects physical devices has emerged. As a unity of computing process and physical process, the Cyber-physical systems (CPS) is the next generation intelligent system which integrates computing, communication and controlling capabilities. CPS covers a wide range of applications and critical infrastructures, including intelligent transportation systems, telemedicine, smart grids, aerospace, and many other fields. The APT attacks are typically conducted directly against these critical infrastructures around the world, which would incur severe consequences. It is meaningful to protect these information by detecting the APT attacks timely and accurately, and effective defensive measures could be adopted. Although the APT attacks seem destructive, the attack process are complex and changeable. In essence, the attack process usually follows certain rules. In this chapter, we introduce a distributed framework for detecting the APT attacks. Cyber security knowledge graph stores existing knowledge and the attack rules, which plays an important role in analyzing the attacks. We first analyze potential attack events by the proposed distributed framework on Spark, then we mine the attack chains from massive data with the spatial and temporal characteristics. These steps could help identify complicated attacks. We also conduct extensive experiments, the results show that the analysis accuracy depends on the completeness of the cyber security knowledge graph and the precision of the detection results from security equipments. With the rational expectation about more exposure of attacks and faster upgrade of security equipments, it is sufficient and necessary to improve the cyber security knowledge graph constantly for better performance.

A Distributed Framework for APT Attack Analysis

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