Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement
احراز هویت سریع و مجوز پیشرو در اینترنت اشیا با مقیاس بزرگ: نحوه استفاده از هوش مصنوعی برای تقویت امنیت-2020
Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles in supporting diverse vertical applications by connecting heterogenous devices, machines, and industry processes. Conventional authentication and authorization schemes are insufficient to overcome the emerging IoT security challenges due to their reliance on both static digital mechanisms and computational complexity for improving security levels. Furthermore, the isolated security designs for different layers and link segments while ignoring the overall protection leads to cascaded security risks as well as growing communication latency and overhead. In this article, we envision new artificial intelligence (AI)-enabled security provisioning approaches to overcome these issues while achieving fast authentication and progressive authorization. To be more specific, a lightweight intelligent authentication approach is developed by exploring machine learning at the base station to identify the prearranged access time sequences or frequency bands or codes used in IoT devices. Then we propose a holistic authentication and authorization approach, where online machine learning and trust management are adopted for achieving adaptive access control. These new AI-enabled approaches establish the connections between transceivers quickly and enhance security progressively so that communication latency can be reduced and security risks are well controlled in large-scale IoT systems. Finally, we outline several areas for AI-enabled security provisioning for future research.
Toward Integrated Virtual Emotion System with AI Applicability for Secure CPS-Enabled Smart Cities: AI-Based Research Challenges and Security Issues
به سمت سیستم احساس مجازی مجتمع با قابلیت هوش مصنوعی برای شهرهای هوشمند دارای CPS امن: چالش های تحقیقاتی مبتنی بر هوش مصنوعی و مسائل امنیتی-2020
Cyber-physical systems (CPS) basically pursue a new form of integrated interaction with humans through computation and physical capabilities covering complex, intelligent, autonomous systems. Also, artificial intelligence (AI) is considered as a promising technology that will be applicable to numerous combined next generation applications including CPS, security, and communication in smart cities. However, cyber security based on AI technologies is still in its infancy and, in particular, the differential challenges or issues should be addressed for various AI-enabled applications and systems. In this article, we introduce a new integrated virtual emotion system with AI applicability, called as I-VEmoSYS, toward secure CPS-enabled smart cities. The integrated virtual emotion system covers several subsystems such as virtual emotion barrier, virtual emotion map, and virtual emotion block. We describe their system settings, concepts, components, and operations, and also deal with AI applicability to those subsystems. Furthermore, we discuss future challenges and security issues that must be met to achieve secure advanced smart cities using the AI-enabled virtual emotion system.
ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial
غربالگری هدایت شده با هوش مصنوعی ECG برای کسر کم دفع (EAGLE): منطق و طراحی یک آزمایش تصادفی خوشه عملی-2020
Background A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment. Objectives To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices. Design The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize N100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. Summary This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms. (Am Heart J 2020;219:31-6.)
AI-enabled recruiting: What is it and how should a manager use it?
استخدام با هوش مصنوعی: چه چیزیست و یک مدیر چگونه باید از آن استفاده کند؟-2020
AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets, human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.
KEYWORDS : AI-enabled recruiting | Artificial intelligence | Digital recruiting | technology | Human resources
Problems of Poison: New Paradigms and "Agreed" Competition in the Era of AI-Enabled Cyber Operations
مسئله سم: پارادایم های جدید و رقابت "توافق شده" در عصر عملیات سایبری با هوش مصنوعی-2020
Few developments seem as poised to alter the characteristics of security in the digital age as the advent of artificial intelligence (AI) technologies. For national defense establishments, the emergence of AI techniques is particularly worrisome, not least because prototype applications already exist. Cyber attacks augmented by AI portend the tailored manipulation of human vectors within the attack surface of important societal systems at great scale, as well as opportunities for calamity resulting from the secondment of technical skill from the hacker to the algorithm. Arguably most important, however, is the fact that AI-enabled cyber campaigns contain great potential for operational obfuscation and strategic misdirection. At the operational level, techniques for piggybacking onto routine activities and for adaptive evasion of security protocols add uncertainty, complicating the defensive mission particularly where adversarial learning tools are employed in offense. Strategically, AI-enabled cyber operations offer distinct attempts to persistently shape the spectrum of cyber contention may be able to pursue conflict outcomes beyond the expected scope of adversary operation. On the other, AI-augmented cyber defenses incorporated into national defense postures are likely to be vulnerable to “poisoning” attacks that predict, manipulate and subvert the functionality of defensive algorithms. This article takes on two primary tasks. First, it considers and categorizes the primary ways in which AI technologies are likely to augment offensive cyber operations, including the shape of cyber activities designed to target AI systems. Then, it frames a discussion of implications for deterrence in cyberspace by referring to the policy of persistent engagement, agreed competition and forward defense promulgated in 2018 by the United States. Here, it is argued that the centrality of cyberspace to the deployment and operation of soon-to-be-ubiquitous AI systems implies new motivations for operation within the domain, complicating numerous assumptions that underlie current approaches. In particular, AI cyber operations pose unique measurement issues for the policy regime.
Keywords: deterrence | persistent engagement | cyber | AI | machine learning
Multiple contents offloading mechanism in AI-enabled opportunistic networks
مکانیسم تخلیه محتوای چندگانه در شبکه های فرصت طلب مجهز به هوش مصنوعی-2020
With the rapid growth of mobile devices and the emergence of 5G applications, the burden of cellular and the use of the licensed band have enormous challenges. In order to solve this problem, opportunity communication is regarded as a potential solution. It can use unlicensed bands to forward content to users under delay-tolerance constraints, as well as reduce cellular data traffic. Since opportunity communication is easily interrupted when User Equipment (UE) is moving, we adopt Artificial Intelligence (AI) to predict the location of the mobile UE. Then, the meta-heuristic algorithm is used to allocate multiple contents. In addition, deep learning-based methods almost need a lot of training time. Based on real-time requirements of the network, we propose AI-enabled opportunistic networks architecture, combined with Mobile Edge Computing (MEC) to implement edge AI applications. The simulation results show that the proposed multiple contents offloading mechanism can reduce cellular data traffic through UE location prediction and cache allocation.
Keywords: Opportunistic networks | MEC | Offloading | Content caching
A taxonomy of AI techniques for 6G communication networks
طبقه بندی تکنیک های هوش مصنوعی برای شبکه های ارتباطی 6G-2020
With 6G flagship program launched by the University of Oulu, Finland, for full future adaptation of 6G by 2030, many institutes worldwide have started to explore various issues and challenges in 6G communication networks. 6G offers ultra high-reliable and massive ultra-low latency while opening the doors for many applications currently not viable by today’s 4G and 5G communication standards. The current 5G technology has security and privacy issues which makes its usage in limited applications. In such an environment, we believe that AI can offer efficient solutions for the aforementioned issues having low communication overhead cost. Keeping focus on all these issues, in this paper, we presented a comprehensive survey on AI-enabled 6G communication technology, which can be used in wide range of future applications. In this article, we explore how AI can be integrated into different applications such as object localization, UAV communication, surveillance, security and privacy preservation etc. Finally, we discussed a use case that shows the adoption of AI techniques in intelligent transport system.
Keywords: Artificial Intelligence | 6G | Communication networks | Mobile edge computing | Intelligent transportation system
AI-enabled emotion-aware robot: The fusion of smart clothing, edge clouds and robotics
ربات آگاه از احساسات مجهز به هوش مصنوعی: تلفیق لباس های هوشمند ، ابرهای لبه ای و رباتیک-2020
Mental health has become a severe problem that significantly influences people’s living quality. With the rapid development of science and technology, a completely new direction for mental health improving by using the interaction between robots and people has emerged. As an intelligent personal agent, a robot can be easily accepted in people’s daily life, meeting users’ behavior and mental demands to a certain extent. Nevertheless, the existing robot design is very limited, and a household personal robot is too large to be carried anywhere . The usage of wearable devices is simple, but these devices cannot offer diversified services. Therefore, this paper puts forward an emotion-aware system that integrates a personal robot, smart clothing, and cloud terminal. A new ’people-centered’ emotioninteraction mode is realized. Namely, personal robot and smart clothing supplement each other seamlessly and interact jointly with users . Artificial intelligence technology and knowledge graph are used to design emotion perception and interaction algorithms including intelligent recommendation, relation recognition, emotional expression recognition. Also, different scenarios are analyzed . Finally, a testbed is built to carry out relevant tests to verify the effectiveness of the proposed algorithms and emotion-aware system. According to the obtained test results, the system can be widely used to serve people and improve people’s mental health.
Keywords: Artificial intelligence | Emotion-aware | Personal robot | Smart clothing
A stochastic model of particulate matters with AI-enabled technique-based IoT gas detectors for air quality assessment
مدل تصادفی ذرات معلق با ردیاب های گاز IoT مبتنی بر تکنیک هوش مصنوعی برای ارزیابی کیفیت هوا-2020
Monitoring air quality in urban and industrial environments and estimating exposure to particulate matter (PM) pollution concentrations are critical issues that affect human health. Because of aerosols (suspended particles), PM is mostly observed near the surface and thus can be inhaled. To predict the modeling of micro-to-nano-sized particle suspensions, this study presents a stochastic model in environmental dynamics with internet of things (IoT) gas detectors based on an artificial intelligence (AI)-enabled technique; the model can determine floating fine PM dispersion in a city to assess and monitor air quality. The factors that influence the prediction are weather- and air pollution-related data, such as humidity, temperature, wind, PM2.5, and PM10. In this study, these factors have been considered at 7 measuring stations across the urban region in Taipei City, Taiwan, from 2013 to 2018. A nonlinear autoregressive network with exogenous inputs model is constructed using estimated states to investigate approaches for identifying PM; the model can be a state–space self-tuning stochastic model for predicting unknown nonlinear sampled data. The results indicate that a satisfactory agreement was obtained using a normalized root mean square deviation, with small values of 0.0504 and 0.0802 for PM2.5 and PM10, respectively. Accordingly, this study presents that the time-domain causality between PM and the atmospheric environment can be constructed using discrete-time models that can be satisfactorily implemented in developing different air quality monitoring systems for the long-term prediction of air pollution.
Keywords: Particulate matter | Micro-to-nano-sized particle suspensions | Modeling | Micropollutants | Artificial intelligence | Atmospheric environment
Towards privacy preserving AI based composition framework in edge networks using fully homomorphic encryption
به سمت حفظ حریم خصوصی و حفظ چارچوب ترکیب مبتنی بر هوش مصنوعی در شبکه های لبه ای با استفاده از رمزنگاری کاملاً همگن-2020
We present a privacy-preserving framework for Artificial Intelligence (AI) enabled composition for the edge networks. Edge computing is a very promising technology for provisioning realtime AI services due to low response time and network bandwidth requirements. Due to the lack of computational capabilities, an edge device alone cannot provide the complex AI services. Complex AI tasks should be divided into multiple subtasks and distributed among multiple edge devices for efficient service provisioning in the edge network. AI-enabled or automatic service composition is one of the essential AI tasks in the service provisioning. In edge computing-based service provisioning, service composition related tasks need to be offloaded to several edge nodes for efficient service. Edge nodes can be used for monitoring services, storing Qualityof- Service (QoS) data, and composing services to find the best composite service. Existing service composition methods use plaintext QoS data. Hence, attackers may compromise edge devices to reveal QoS data of services and modify them for giving an advantage to particular edge service providers, and the AI-based service composition becomes biased. From that point of view, a privacy-preserving framework for AI-based service composition is required for the edge networks. In our proposed framework, we introduce an AI-based composition model for edge services in the edge networks. Additionally, we present a privacy-preserving AI service composition framework to perform composition on encrypted QoS data using fully homomorphic encryption (FHE) algorithm. We conduct several experiments to evaluate the performance of our proposed privacy-preserving service composition framework using a synthetic QoS dataset.
Keywords: Edge-AI | Artificial Intelligence | Privacy in edge networks | Privacy-preserving AI | Privacy-preserving AI-based service | composition | Privacy-preserving service composition