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"Computer network architectures Security measures."
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Flexible network architectures security issues and principles
The future of Internet security doesn't lie in doing more of the same. It requires not only a new architecture, but the means of securing that architecture. Two trends have come together to make the topic of this book of vital interest. First, the explosive growth of the Internet connections for the exchange of information via networks increased the dependence of both organizations and individuals on the systems stored and communicated. This, in turn, has increased the awareness for the need to protect the data and add security as chief ingredient in the newly emerged architectures. Second, the disciplines of cryptography and network security have matured and are leading to the development of new techniques and protocols to enforce the network security in Future Internet. This book examines the new security architectures from organizations such as FIArch, GENI, and IETF and how they'll contribute to a more secure Internet-- Provided by publisher.
Book
Practical Industrial Internet of Things Security
This book provides you with a comprehensive understanding of Industrial IoT security; and practical methodologies to implement safe, resilient cyber-physical systems. It will help you develop a strong foundation and deeper insights on the entire gamut of securing connected industries, from the edge to the cloud.
eBook
Security without obscurity : a guide to cryptographic architectures
Information security has a major gap when cryptography is implemented. Cryptographic algorithms are well defined, key management schemes are well known, but the actual deployment is typically overlooked, ignored, or unknown. Cryptography is everywhere. Application and network architectures are typically well-documented but the cryptographic architecture is missing. This book provides a guide to discovering, documenting, and validating cryptographic architectures. Each chapter builds on the next to present information in a sequential process. This approach not only presents the material in a structured manner, it also serves as an ongoing reference guide for future use-- Provided by the publisher.
Book
Cloud security automation
Security issues are still a major concern for all IT organizations. For many enterprises, the move to cloud computing has raised concerns for security, but when applications are architected with focus on security, cloud platforms can be made just as secure as on-premises platforms. Cloud instances can be kept secure by employing security automation that helps make your data meet your organization's security policy. This book starts with the basics of why cloud security is important and how automation can be the most effective way of controlling cloud security. You will then delve deeper into the AWS cloud environment and its security services by dealing with security functions such as Identity and Access Management and will also learn how these services can be automated. Moving forward, you will come across aspects such as cloud storage and data security, automating cloud deployments, and so on. Then, you'll work with OpenStack security modules and learn how private cloud security functions can be automated for better time- and cost-effectiveness. Toward the end of the book, you will gain an understanding of the security compliance requirements for your Cloud. By the end of this book, you will have hands-on experience of automating your cloud security and governance.
eBook
Ansible Quick Start Guide
Configuration Management (CM) tools help administrators reduce their workload. Ansible is one of the best Configuration Management tools, and can act as an orchestrator for managing other CMs. This book is the easiest way to learn how to use Ansible as an orchestrator and a Configuration Management tool. With this book, you'll learn how to.
eBook
Intrusion detection systems for IoT-based smart environments: a survey
One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.
Journal Article
A blockchain-based smart home gateway architecture for preventing data forgery
With the advancement of Information and Communication Technology (ICT) and the proliferation of sensor technologies, the Internet of Things (IoT) is now being widely used in smart home for the purposes of efficient resource management and pervasive sensing. In smart homes, various IoT devices are connected to each other, and these connections are centered on gateways. The role of gateways in the smart homes is significant, however, its centralized structure presents multiple security vulnerabilities such as integrity, certification, and availability. To address these security vulnerabilities, in this paper, we propose a blockchain-based smart home gateway network that counters possible attacks on the gateway of smart homes. The network consists of three layers including device, gateway, and cloud layers. The blockchain technology is employed at the gateway layer wherein data is stored and exchanged in the form blocks of blockchain to support decentralization and overcome the problem from traditional centralized architecture. The blockchain ensures the integrity of the data inside and outside of the smart home and provides availability through authentication and efficient communication between network members. We implemented the proposed network on the Ethereum blockchain technology and evaluated in terms of standard security measures including security response time and accuracy. The evaluation results demonstrate that the proposed security solutions outperforms over the existing solutions.
Journal Article
Network intrusion detection using oversampling technique and machine learning algorithms
The expeditious growth of the World Wide Web and the rampant flow of network traffic have resulted in a continuous increase of network security threats. Cyber attackers seek to exploit vulnerabilities in network architecture to steal valuable information or disrupt computer resources. Network Intrusion Detection System (NIDS) is used to effectively detect various attacks, thus providing timely protection to network resources from these attacks. To implement NIDS, a stream of supervised and unsupervised machine learning approaches is applied to detect irregularities in network traffic and to address network security issues. Such NIDSs are trained using various datasets that include attack traces. However, due to the advancement in modern-day attacks, these systems are unable to detect the emerging threats. Therefore, NIDS needs to be trained and developed with a modern comprehensive dataset which contains contemporary common and attack activities. This paper presents a framework in which different machine learning classification schemes are employed to detect various types of network attack categories. Five machine learning algorithms: Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors and Artificial Neural Networks, are used for attack detection. This study uses a dataset published by the University of New South Wales (UNSW-NB15), a relatively new dataset that contains a large amount of network traffic data with nine categories of network attacks. The results show that the classification models achieved the highest accuracy of 89.29% by applying the Random Forest algorithm. Further improvement in the accuracy of classification models is observed when Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. After applying the SMOTE, the Random Forest classifier showed an accuracy of 95.1% with 24 selected features from the Principal Component Analysis method.
Journal Article