Active cyber defense mechanisms are necessary to perform automated, and even autonomous operations using intelligent agents that defend against modern/sophisticated AI-inspired cyber threats (e.g., ransomware, cryptojacking, deep-fakes). These intelligent agents need to rely on deep learning using mature knowledge and should have the ability to apply this knowledge in a situational and timely manner for a given AI-inspired cyber threat. In this paper, we describe a ‘domain-agnostic knowledge graph-as-a-service’ infrastructure that can support the ability to create/store domain-specific knowledge graphs for intelligent agent Apps to deploy active cyber defense solutions defending real-world applications impacted by AI-inspired cyber threats. Specifically, we present a reference architecture, describe graph infrastructure tools, and intuitive user interfaces required to construct and maintain large-scale knowledge graphs for the use in knowledge curation, inference, and interaction, across multiple domains (e.g., healthcare, power grids, manufacturing). Moreover, we present a case study to demonstrate how to configure custom sets of knowledge curation pipelines using custom data importers and semantic extract, transform, and load scripts for active cyber defense in a power grid system. Additionally, we show fast querying methods to reach decisions regarding cyberattack detection to deploy pertinent defense to outsmart adversaries.

The exponential growth of web documents has resulted in traditional search engines producing results with high recall but low precision when queried by users. In the contemporary internet landscape, resources are made available via hyperlinks which may or may not meet the expectations of the user. To mitigate this issue and enhance the level of pertinence, it is imperative to examine the challenges associated with querying the semantic web and progress towards the advancement of semantic search engines. These search engines generate outcomes by prioritizing the semantic significance of the context over the structural composition of the content. This paper outlines a proposed architecture for a semantic search engine that utilizes the concept of semantics to refine web search results. The resulting output would consist of ontologically based and contextually relevant outcomes pertaining to the user s query.

In this paper, we design and develop a new multimedia distribution platform that mainly utilizes containerization and microservice architecture technologies. Using our approach, the multimedia service source code located in a repository such as Git can be built into a container image for distribution and management, and the process of delivering it to the target edge device can be performed through a pipeline. In addition, distributed edge devices can be built into clusters with various connection profiles and utilized for services. Real-time monitoring functions are provided to ensure stable service operation even after the service is deployed. To implement this complex service platform, we follow the microservice architecture method. Stable operation was confirmed even during an operational test period of over a year. This technology is expected to help deploy multimedia services conveniently and quickly and manage them stably and efficiently.

Right to education is a basic need of every child and every society across the globe. Ever since the internet revolution and technological upgradation takes place, education system starts evolving from traditional way to smarter way. Covid-19 and industrial revolution has made smart education a global business that is now even penetrating to rural footprints of remote locations. Use of smart devices, IoT based communications and AI techniques have increased the cyberattack surface over the smart education system. Moreover, lack of cyber awareness and absence of essential cyber sanity checks has exposed the vulnerability in smart education system. A study of technology evolution of education to smart education and its penetration across the globe, details of smart education ecosystem, role of various stakeholders are discussed in this paper. It also covers most trending cyber-attacks, history of reported cyber-attacks in smart education sector. Further, in order to make smart educational cyber space more secure, proactive preventive measures and cyber sanity actions to mitigate such attacks are also discussed.

The Internet of Things (IoT) has changed the way we gather medical data in real time. But, it also brings worries about keeping this data safe and private. Ensuring a secure system for IoT is crucial. At the same time, a new technology is emerging that can help the IoT industry a lot. It s called Blockchain technology. It keeps data secure, transparent, and unchangeable. It s like a ledger for tracking lots of connected devices and making them work together. To make IoT even safer, we can use facial recognition with Convolutional Neural Networks (CNN). This paper introduces a healthcare system that combines Blockchain and artificial intelligence in IoT. An implementation of Raspberry Pi E-Health system is presented and evaluated in terms of function s cost. Our system present low cost functions.

The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (IoT) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service s architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure IoT service, mainly IoT Hub and IoT Central, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability.

Systems with artificial intelligence components, so-called AI-based systems, have gained considerable attention recently. However, many organizations have issues with achieving production readiness with such systems. As a means to improve certain software quality attributes and to address frequently occurring problems, design patterns represent proven solution blueprints. While new patterns for AI-based systems are emerging, existing patterns have also been adapted to this new context. The goal of this study is to provide an overview of design patterns for AI-based systems, both new and adapted ones. We want to collect and categorize patterns, and make them accessible for researchers and practitioners. To this end, we first performed a multivocal literature review (MLR) to collect design patterns used with AI-based systems. We then integrated the created pattern collection into a web-based pattern repository to make the patterns browsable and easy to find. As a result, we selected 51 resources (35 white and 16 gray ones), from which we extracted 70 unique patterns used for AI-based systems. Among these are 34 new patterns and 36 traditional ones that have been adapted to this context. Popular pattern categories include architecture (25 patterns), deployment (16), implementation (9), or security \& safety (9). While some patterns with four or more mentions already seem established, the majority of patterns have only been mentioned once or twice (51 patterns). Our results in this emerging field can be used by researchers as a foundation for follow-up studies and by practitioners to discover relevant patterns for informing the design of AI-based systems.

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.CCS CONCEPTS• Software and its engineering Software architectures; • Information systems World Wide Web; • Security and privacy Privacy protections; • Social and professional topics Copyrights; • Computing methodologies Machine learning.

This work introduces an innovative security system prototype tailored explicitly for paying guest accommodations or hostels, blending Internet of Things (IoT), artificial intelligence (AI), machine learning algorithms, and web crawling technologies. The core emphasis revolves around facial recognition, precisely distinguishing between known and unknown individuals to manage entry effectively. The system, integrating camera technology, captures visitor images and employs advanced face recognition algorithms for precise face classification. In instances where faces remain unrecognized, the system leverages web crawling to retrieve potential intruder details. Immediate notifications, featuring captured images, are swiftly dispatched to users through email and smartphone alerts, enabling prompt responses. Operated within a wireless infrastructure governed by a Raspberry Pi, this system prioritizes cost-effectiveness and user-friendliness. Rigorously tested across diverse environments encompassing homes, paying guest accommodations, and office spaces, this research establishes a remarkable balance between cutting-edge technology and pragmatic security applications. This solution offers an affordable and efficient security option tailored explicitly for the unique needs of contemporary hostels and paying guest accommodations, ensuring heightened security without exorbitant expenses.

A decentralized and secure architecture made possible by blockchain technology is what Web 3.0 is known for. By offering a secure and trustworthy platform for transactions and data storage, this new paradigm shift in the digital world promises to transform the way we interact with the internet. Data is the new oil, thus protecting it is equally crucial. The foundation of the web 3.0 ecosystem, which provides a secure and open method of managing user data, is blockchain technology. With the launch of Web 3.0, demand for seamless communication across numerous platforms and technologies has increased. Blockchain offers a common framework that makes it possible for various systems to communicate with one another. The decentralized nature of blockchain technology almost precludes hacker access to the system, ushering in a highly secure Web 3.0. By preserving the integrity and validity of data and transactions, blockchain helps to build trust in online transactions. AI can be integrated with blockchain to enhance its capabilities and improve the overall user experience. We can build a safe and intelligent web that empowers users, gives them more privacy, and gives them more control over their online data by merging blockchain and AI. In this article, we emphasize the value of blockchain and AI technologies in achieving Web 3.0 s full potential for a secure internet and propose a Blockchain and AI empowered framework. The future of technology is now driven by the power of blockchain, AI, and web 3.0, providing a secure and efficient way to manage digital assets and data.

The HTTP protocol is the backbone for how traffic is communicated over the Internet and between web applications and users. Introduced in 1997 with HTTP 1.0 and 1.1, HTTP has gone through several developmental changes throughout the years. HTTP/1.1 suffers from several issues. Namely only allowing a one-to-one connection. HTTP/2 allowed for multiplexed connections. Additionally, HTTP/2 attempted to address the security issues that were faced by the prior version of HTTP by allowing administrators to enable HTTPS, as well as enable certificates to help ensure the encryption and protection of data between users and the web application. One of the major issues HTTP/2 faces is that it allows users to have multiplexed connections, but when there is an error and data needs to be retransmitted, this leads to head of line blocking. HTTP/3 is a new protocol that was proposed for formalization to the IETF in June of 2022. One of the first major changes is that unlike prior versions of HTTP that used the TCP/IP method of networking for data transmission, HTTP/3 uses UDP for data transmission. Prior research has focused on the protocol itself or investigating how certain types of attacks affect your web architecture that uses QUIC and HTTP/3. One area lacking research in this topic is how to secure web architecture in the cloud that uses this new protocol. To this end, we will be investigating how logging can be used to secure your web architecture and this protocol in the cloud.

The exponential growth of web documents has resulted in traditional search engines producing results with high recall but low precision when queried by users. In the contemporary internet landscape, resources are made available via hyperlinks which may or may not meet the expectations of the user. To mitigate this issue and enhance the level of pertinence, it is imperative to examine the challenges associated with querying the semantic web and progress towards the advancement of semantic search engines. These search engines generate outcomes by prioritizing the semantic significance of the context over the structural composition of the content. This paper outlines a proposed architecture for a semantic search engine that utilizes the concept of semantics to refine web search results. The resulting output would consist of ontologically based and contextually relevant outcomes pertaining to the user s query.