Advanced Sciences and Technologies for Security Applications

Reza Montasari

Fiona Carroll

Stuart Macdonald

Hamid Jahankhani

Amin Hosseinian-Far

Alireza Daneshkhah

Digital Forensic Investigation of Internet of Things (IoT) Devices

Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence

人工智能与机器学习在生成可操作网络威胁情报中的应用

Mohamed Baza

Mostafa M. Fouda

Mahmoud Nabil

Adly Tag Eldien

Hala Mansour

Mohamed Mahmoud

Blockchain-Based Distributed Key Management Approach Tailored for Smart Grid

基于区块链的智能电网分布式密钥管理方法

Social media (SM) has revolutionized how people interact and communicate, effectively turning our planet into a true “global village”. At the same time, new and unexpected challenges emerged in conjunction with SM, including phenomena of “fake news” and cyber-bullying. As with most technological advancements, international health security (IHS) stands to benefit hugely from the advent of SM. At the same time, some real and potential dangers associated with both intentional and unintentional misuse of SM exist. The chapter will outline how SM can be used to fight disease more effectively (e.g., by information sharing/case identification during outbreaks) but also cause confusion and unauthorized transfer of sensitive information (e.g., premature or erroneous notification of a public health emergency). Finally, public health panics and other forms of harm can potentially be orchestrated with malevolent use of SM involving vulnerable populations (e.g., misinformation campaigns, encouraging deleterious behaviors). In each of the above situations, and especially the latter, SM can be viewed as an asymmetric influencer, with low investment cost(s) leading to disproportionate population effect(s).

Stanislaw P. Stawicki

Michael S. Firstenberg

Thomas J. Papadimos

The Growing Role of Social Media in International Health Security: The Good, the Bad, and the Ugly

社交媒体在国际卫生安全中的日益增长的作用：利与弊及丑陋面

This book is on urban resilience how to design and operate cities that can withstand major threats such as natural disasters and economic downturns and how to recover from them. It is a collection of latest research results from two separate but collaborating research groups, namely, researchers in urban design and those on general resilience theory. The book systematically deals with the core aspects of urban resilience: systems, management issues and populations. The taxonomy can be broken down into threats, systems, resilience cycles and recovery types in the context of urban resilience. It starts with a discussion of systems resilience models, focusing on the central idea that resilience is a moving average of costs (a set of trajectories in a two-player game paradigm). The second section explores management issues, including planning, operating and emergency response in cities with specific examples such as land-use planning and carbon-neutral scenarios for urban planning. The next section focuses on urban dwellers and specific people-related issues in the context of resilience. Agent-based simulation of behaviour and perception-based resilience, as well as brand crisis management are representative examples of the topics discussed. A further section examines systems like public utilities including managing power supplies, cyber-security issues and models for pandemics. It concludes with a discussion of the future challenges and risks facing complex systems, for example in resilient power grids, making it essential reading for a wide range of researchers and policymakers.

Y. Yamagata

H. Maruyama

Urban Resilience

城市韧性

Software Defined Networking (SDN) is emerging as a key technology for future Internet. SDN provides a global network along with the capability to dynamically control network flow. One key advantage of SDN, as compared to the traditional network, is that by virtue of centralized control it allows better provisioning of network security. Nevertheless, the flexibility provided by SDN architecture manifests several new network security issues that must be addressed to strengthen SDN network security. So, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection system for SDN. The proposed approach was tested using the NSL-KDD and CICIDS2017 dataset, and we achieved an accuracy of 89% and 99% respectively with low dimensional feature sets that can be extracted at the SDN controller. We also evaluated network performance of our proposed approach in terms of throughput and latency. Our test results show that the proposed GRU-RNN model does not deteriorate the network performance. Through extensive experimental evaluation, we conclude that our proposed approach exhibits a strong potential for intrusion detection in the SDN environments.

Tuan Anh Tang

Des McLernon

Lotfi Mhamdi

Syed Ali Raza Zaidi

Mounir Ghogho

Deep Learning Applications for Cyber Security

Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach

基于SDN网络的入侵检测：深度循环神经网络方法

ISSN	1613-5113, 2363-9466
h-index	19

Publication Information	Publisher: Springer Nature，Publishing cycle: ，Journal Type: book-series，Open Access Journals: No
Basic data	Year of publication: 2016，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:

Advanced Sciences and Technologies for Security Applications

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Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence

Blockchain-Based Distributed Key Management Approach Tailored for Smart Grid

The Growing Role of Social Media in International Health Security: The Good, the Bad, and the Ugly

Urban Resilience

Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach

Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence

Blockchain-Based Distributed Key Management Approach Tailored for Smart Grid

The Growing Role of Social Media in International Health Security: The Good, the Bad, and the Ugly

Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach

Urban Resilience