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Blockchained Federated Learning for Threat Defense

arXiv.org Artificial Intelligence

Given the increasing complexity of threats in smart cities, the changing environment, and the weakness of traditional security systems, which in most cases fail to detect serious threats such as zero-day attacks, the need for alternative more active and more effective security methods keeps increasing. Such approaches are the adoption of intelligent solutions to prevent, detect and deal with threats or anomalies under the conditions and the operating parameters of the infrastructure in question. This research paper introduces the development of an intelligent Threat Defense system, employing Blockchain Federated Learning, which seeks to fully upgrade the way passive intelligent systems operate, aiming at implementing an Advanced Adaptive Cooperative Learning (AACL) mechanism for smart cities networks. The AACL is based on the most advanced methods of computational intelligence while ensuring privacy and anonymity for participants and stakeholders. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. Learning is achieved through encrypted smart contracts within the blockchain technology, for unambiguous validation and control of the process. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods, in order to identify anomalies that are usually due to Advanced Persistent Threat (APT) attacks.


Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

arXiv.org Artificial Intelligence

As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.


Research and Education Towards Smart and Sustainable World

arXiv.org Artificial Intelligence

We propose a vision for directing research and education in the ICT field. Our Smart and Sustainable World vision targets at prosperity for the people and the planet through better awareness and control of both human-made and natural environment. The needs of the society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.


Cyber Threat Intelligence for Secure Smart City

arXiv.org Artificial Intelligence

Sujata238dash@gmail.com Abstract--Smart city improved the quality of life for the The rest of this paper is structured as follows. York start becoming more intelligent. These cities are providing services through technology such as IoT and Cyber-A. Smart City Physical Systems (CPS), where they are connected through a The smart city concept refers to urban systems that network to monitor, control and automate the city services to integrated with ICT to improve city services in terms of provide the best quality of life for the citizens [1]. The smart city contains a huge number of sensors Smart city technologies exchange and process different that continuously generate a tremendous amount of sensitive types of data to provide services. These data can be sensitive data such as location coordinates, credit card numbers, and and critical which imposes security and privacy requirements. These data are transmitted through the However, the characteristics of smart city technology such as network to data centers for processing and analysis to take the IoT and CPS in terms of resources limitation such as power, appropriate decisions such as managing traffic and energy in memory, and processing imposes challenges to run a smart city [6][3]. Therefore, different attacks Sensors that generate data and devices that handle the data target smart city infrastructure including Distributed Denial of in a smart city have vulnerabilities that can be exploited by Service (DDoS) using IoT devices by infecting IoT devices by cybercriminals.


A Survey on Edge Intelligence

arXiv.org Artificial Intelligence

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.


Artificial Intelligence for Digital Agriculture at Scale: Techniques, Policies, and Challenges

arXiv.org Artificial Intelligence

Digital agriculture has the promise to transform agricultural throughput. It can do this by applying data science and engineering for mapping input factors to crop throughput, while bounding the available resources. In addition, as the data volumes and varieties increase with the increase in sensor deployment in agricultural fields, data engineering techniques will also be instrumental in collection of distributed data as well as distributed processing of the data. These have to be done such that the latency requirements of the end users and applications are satisfied. Understanding how farm technology and big data can improve farm productivity can significantly increase the world's food production by 2050 in the face of constrained arable land and with the water levels receding. While much has been written about digital agriculture's potential, little is known about the economic costs and benefits of these emergent systems. In particular, the on-farm decision making processes, both in terms of adoption and optimal implementation, have not been adequately addressed. For example, if some algorithm needs data from multiple data owners to be pooled together, that raises the question of data ownership. This paper is the first one to bring together the important questions that will guide the end-to-end pipeline for the evolution of a new generation of digital agricultural solutions, driving the next revolution in agriculture and sustainability under one umbrella.



Video meets the Internet of Things

@machinelearnbot

Video-analytics technology is transforming the Internet of Things and creating new opportunities. Are companies prepared to capture growth? Some of the most innovative Internet of Things (IoT) applications involve video analytics--a technology that applies machine-learning algorithms to video feeds, allowing cameras to recognize people, objects, and situations automatically. These applications are relatively new, but several factors are encouraging their growth, including the increased sophistication of analytical algorithms and lower costs for hardware, software, and storage. With video analytics becoming more important to IoT applications, we decided to examine this technology more closely.


V5 Systems Showcases Market-Ready IoT Technology at Intel Partner Connect 2018

#artificialintelligence

The Intel event, which is expected to be attended by approximately 2500 channel partners, has Dell EMC as one of its premier sponsors, a key technology partner with which V5 Systems has already implemented a number of turnkey IoT security solutions in the outdoors. The upshot of the event is for attendees to gain insights from Intel innovators and industry visionaries on how the IoT, along with artificial intelligence, virtual reality, the Cloud and other leading-edge technology applications, is contributing to the digital transformation of businesses. The event enables participants to accelerate their partnerships and identify new opportunities for profitability. V5 Systems has been invited to present its technology at the event because it has already established its leadership position in the Industrial IoT by implementing numerous real-world solutions. At Intel Partner Connect, V5 Systems, Intel and Dell EMC are changing the security space through technology and innovation.


10 Indian AI Startups To Watch Out For In 2018 [Startup Watchlist]

#artificialintelligence

This article is part of Inc42's Startup Watchlist annual series where we list the top startups to watch for 2018 from industries like AI, Logistics, Fintech etc. Explore all the stories from'Startup Watchlist' series here.