The last decade has shown that networked cyberphysical systems (NCPS) are the future of critical infrastructure such as transportation systems and energy production. However, they have introduced an uncharted territory of security vulnerabilities and a wider attack surface, mainly due to network openness and the deeply integrated physical and cyber spaces. On the other hand, relying on manual analysis of intrusion detection alarms might be effective in stopping run-of-the-mill automated probes but remain useless against the growing number of targeted, persistent, and often AI-enabled attacks on large-scale NCPS. Hence, there is a pressing need for new research directions to provide advanced protection. This paper introduces a novel security paradigm for emerging NCPS, namely Autonomous CyberPhysical Defense (ACPD). We lay out the theoretical foundations and describe the methods for building autonomous and stealthy cyber-physical defense agents that are able to dynamically hunt, detect, and respond to intelligent and sophisticated adversaries in real time without human intervention. By leveraging the power of game theory and multi-agent reinforcement learning, these selflearning agents will be able to deploy complex cyber-physical deception scenarios on the fly, generate optimal and adaptive security policies without prior knowledge of potential threats, and defend themselves against adversarial learning. Nonetheless, serious challenges including trustworthiness, scalability, and transfer learning are yet to be addressed for these autonomous agents to become the next-generation tools of cyber-physical defense.

With the advancement in computing power and speed, the Internet is being transformed from screen-based information to immersive and extremely low latency communication environments in web 3.0 and the Metaverse. With the emergence of the Metaverse technology, more stringent demands are required in terms of connectivity such as secure access and data privacy. Future technologies such as 6G, Blockchain, and Artificial Intelligence (AI) can mitigate some of these challenges. The Metaverse is now on the verge where security and privacy concerns are crucial for the successful adaptation of such disruptive technology. The Metaverse and web 3.0 are to be decentralized, anonymous, and interoperable. Metaverse is the virtual world of Digital Twins and nonfungible tokens (NFTs).The control and possession of users’ data on centralized servers are the cause of numerous security and privacy concerns.This paper proposes a solution for the security and interoperability challenges using Self-Sovereign Identity (SSI) integrated with blockchain. The philosophy of Self-Sovereign Identity, where the users are the only holders and owners of their identity, comes in handy to solve the questions of decentralization, trust, and interoperability in the Metaverse. This work also discusses the vision of a single, open standard, trustworthy, and interoperable Metaverse with initial design and implementation of SSI concepts.

Vulnerability Detection 2022 - The increasing number of security vulnerabilities has become an important problem that needs to be solved urgently in the field of software security, which means that the current vulnerability mining technology still has great potential for development. However, most of the existing AI-based vulnerability detection methods focus on designing different AI models to improve the accuracy of vulnerability detection, ignoring the fundamental problems of data-driven AI-based algorithms: first, there is a lack of sufficient high-quality vulnerability data; second, there is no unified standardized construction method to meet the standardized evaluation of different vulnerability detection models. This all greatly limits security personnel’s in-depth research on vulnerabilities. In this survey, we review the current literature on building high-quality vulnerability datasets, aiming to investigate how state-of-the-art research has leveraged data mining and data processing techniques to generate vulnerability datasets to facilitate vulnerability discovery. We also identify the challenges of this new field and share our views on potential research directions.

Object Oriented Security - Aerial surveillance plays an important role for security applications. It can be further used to monitor borders, restricted zones and critical infrastructure. With the help of drones one can perform surveillance and get the exact location of various objects. Aerial object detection comes with many challenges like the object size which can be as low as 20×20 pixels. Images taken from satellites are hundreds of megapixels. Traditional methods like Histogram of oriented gradients (HOG) and Scale invariant feature transformation (SIFT) were used to extract features from the objects. Then these features were given to machine learning classifier like logistic regression, Support vector machine (SVM) and Random forest (RF) for detection and classification. However, the issue with these methods is that they are highly inaccurate and generated many false detections and misclassifications too. With the evolution of Graphics processing units (GPU) and the introduction of convolutional neural networks (CNN) as well as Deep Learning algorithms situation got changed. Now, it is possible to extract more information and provide better accuracy. In this paper for object detection You only look once version 4 (YOLOv4) is used which is one of the state-of-the-art algorithms. It uses Darknet 53 which is a type of CNN as a backbone for feature extraction. In this work the YOLOv4 based proposed system detect and localize vehicles present in the restricted zone and then geotag them.

Network Security Resiliency - Distributed cyber-infrastructures and Artificial Intelligence (AI) are transformative technologies that will play a pivotal role in the future of society and the scientific community. Internet of Things (IoT) applications harbor vast quantities of connected devices that collect a massive amount of sensitive information (e.g., medical, financial), which is usually analyzed either at the edge or federated cloud systems via AI/Machine Learning (ML) algorithms to make critical decisions (e.g., diagnosis). It is of paramount importance to ensure the security, privacy, and trustworthiness of data collection, analysis, and decision-making processes. However, system complexity and increased attack surfaces make these applications vulnerable to system breaches, single-point of failures, and various cyber-attacks. Moreover, the advances in quantum computing exacerbate the security and privacy challenges. That is, emerging quantum computers can break conventional cryptographic systems that offer cyber-security services, public key infrastructures, and privacy-enhancing technologies. Therefore, there is a vital need for new cyber-security paradigms that can address the resiliency, long-term security, and efficiency requirements of distributed cyber infrastructures.

Intellectual Property Security - 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.

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.

With the widespread deployment of data-driven services, the demand for data volumes continues to grow. At present, many applications lack reliable human supervision in the process of data collection, which makes the collected data contain low-quality data or even malicious data. This low-quality or malicious data make AI systems potentially face much security challenges. One of the main security threats in the training phase of machine learning is data poisoning attacks, which compromise model integrity by contaminating training data to make the resulting model skewed or unusable. This paper reviews the relevant researches on data poisoning attacks in various task environments: first, the classification of attacks is summarized, then the defense methods of data poisoning attacks are sorted out, and finally, the possible research directions in the prospect.

In this modern era, web security is often required to beware from fraudulent activities. There are several hackers try to build a program that can interact with web pages automatically and try to breach the data or make several junk entries due to that web servers get hanged. To stop the junk entries; CAPTCHA is a solution through which bots can be identified and denied the machine based program to intervene with. CAPTCHA stands for Completely Automated Public Turing test to tell Computers and Humans Apart. In the progression of CAPTCHA; there are several methods available such as distorted text, picture recognition, math solving and gaming based CAPTCHA. Game based turing test is very much popular now a day but there are several methods through which game can be cracked because game is not intellectual. So, there is a required of intrinsic CAPTCHA. The proposed system is based on Intrinsic Decision based Situation Reaction Challenge. The proposed system is able to better classify the humans and bots by its intrinsic problem. It has been considered as human is more capable to deal with the real life problems and machine is bit poor to understand the situation or how the problem can be solved. So, proposed system challenges with simple situations which is easier for human but almost impossible for bots. Human is required to use his common sense only and problem can be solved with few seconds.