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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
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
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
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
Security for Service Oriented Architectures
This book examines application and security architectures, and illustrates the relationship between the two. Supplying guidance through the design of distributed and resilient applications, it gives an overview of the standards that service oriented and distributed applications leverage to provide the understanding to make decisions on design.
eBook
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
ICN intrusion detection method based on GA-CNN
The current industrial control system network is susceptible to data theft attacks such as SQL injection in practical applications, resulting in data loss or leakage of enterprise secrets. To solve the network intrusion problem faced by industrial control systems in the current global communication security environment, a network intrusion detection method based on genetic algorithm and improved convolutional neural network is proposed. Genetic algorithm is utilized to solve and optimize the data, one-dimensional multi-scale convolutional neural network is combined with gated recurrent unit to improve the network intrusion detection model, and finally the detection and defense of industrial control network intrusion is completed. GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. One-dimensional multi-scale convolutional neural network captures multi-scale features in network traffic data through multi-scale convolutional kernels, compensating for key features that traditional convolutional neural networks may overlook. The introduction of gated recurrent unit addresses the dependency of time series data and effectively solves the problem of gradient vanishing or exploding in traditional recurrent neural networks when processing long sequence data. The results showed that the proposed model only took about 8 seconds to complete training and testing, while all other models required about 10 seconds. The running time of the proposed method was less than that of other methods. In addition, the detection rate, packet loss rate, and false alarm rate of the proposed method for industrial control systems were 96.97%, 1.256%, and 0.0947% respectively, and the defense success rate of intrusion was higher than 90%. The results above show that the proposed method has very superior intrusion detection performance and good generalization ability and can meet the needs of industrial control systems for network intrusion detection.
Journal Article
SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization
The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion.
Journal Article