The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

The platforms supporting the smart city applications are rarely implemented from scratch by a municipality and/or totally owned by a single company, but are more typically realized by integrating some existing ICT infrastructures thanks to a supporting platform, such as the well known FIWARE platform. Such a multi-tenant deployment model is required to lower the initial investment costs to implement large scale solutions for smart cities, but also imposes some key security obstacles. In fact, smart cities support critical applications demanding to protect the data and functionalities from malicious and unauthorized uses. Equipping the supporting platforms with proper means for access control is demanding, but these means are typically implemented according to a centralized approach, where a single server stores and makes available a set of identity attributes and authorization policies. Having a single root of trust is not suitable in a distributed and cooperating scenario of large scale smart cities due to their multi-tenant deployment. In fact, each of the integrated system has its own set of security policies, and the other systems need to be aware of these policy, in order to allow a seamless use of the same credentials across the overall infrastructure (realizing what is known as the single-sign-on). This imposes the problem of consistent and secure data replicas within a distributed system, which can be properly approached by using the blockchain technology. Therefore, this work proposes a novel solution for distributed management of identity and authorization policies by leveraging on the blockchain technology to hold a global view of the security policies within the system, and integrating it in the FIWARE platform. A detailed assessment is provided to evaluate the goodness of the proposed approach and to compare it with the existing solutions.

Blockchain-based authentication and authorization for smart city applications

Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN). First, to improve the accuracy of malware detection, we reconstruct the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware. In the serial convolutional neural network architecture (CNN-S), we use Relu, a non-linear function, as the activation function to increase sparseness and “dropout” to prevent over-fitting. The convolutional layer and pooling layer are combined with the full-connection layer to enhance feature extraction capability. Under these conditions, CNN-S shows powerful ability in feature extraction and malware detection. Second, to reduce the training time, we use deep autoencoder as a pre-training method of CNN. With the combination, deep autoencoder and CNN model (DAE-CNN) can learn more flexible patterns in a short time. We conduct experiments on 10,000 benign apps and 13,000 malicious apps. CNN-S demonstrates a significant improvement compared with traditional machine learning methods in Android malware detection. In details, compared with SVM, the accuracy with the CNN-S model is improved by 5%, while the training time using DAE-CNN model is reduced by 83% compared with CNN-S model.

Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network

In recent time, software defined networking (SDN) has evolved into a new and promising networking paradigm. In the SDN-based cloud, the essential features of SDN, including global view of the whole network, software-based traffic analysis, centralized control over the network, etc. can greatly improve the DDoS attack detection and mitigation capabilities of the cloud. However, integration of SDN in the cloud itself introduces new DDoS attack vulnerabilities. Limited flow-table size is a vulnerability that can be exploited by the adversaries to perform DDoS attacks on the SDN-based cloud. In this paper, we first discuss various essential features of SDN that makes it a suitable networking technology for cloud computing. In addition, we represent the flow table-space of a switch by using a queuing theory based mathematical model. Further, we propose a novel flow-table sharing approach to protect the SDN-based cloud from flow table overloading DDoS attacks. This approach utilizes idle flow-table of other OpenFlow switches in the network to protect the switch’s flow-table from overloading. Our approach increases the resistance of the cloud system against DDoS attacks with minimal involvement of the SDN controller. Thus, it has very low communication overhead. Our claims are well supported by the extensive simulation-based experiments.

Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment

Multimedia computing has evolved as a remarkable technology which provides services to view, create, edit, process, and search multimedia contents. All these multimedia services have high computational, bandwidth, and storage requirements. Therefore, multimedia cloud computing has gained appreciable popularity and acceptance in the past one decade. The convenience of cloud computing comes with financial burden. One of the fundamental features of cloud computing, which helps in reducing the financial worries of the multimedia service providers is the cloud’s pay-as-you-go pricing model. However, the cloud’s pricing model has also attracted adversaries that have hindered the migration of services and/or data by various organisations to the cloud. Through the cloud’s pay-as-you-go pricing model, attackers usually target the financial viability of the cloud customers. Therefore, such attacks are capable to affect the long term availability of multimedia-services hosted on the public cloud. These attacks are known as Fraudulent Resource Consumption (FRC) attack. Therefore, research in the area of FRC attack detection and mitigation is important in motivating the organisations to adopt the public cloud platform. In this paper, we propose a novel approach based on network flow analysis at the victim side to detect and mitigate the FRC attacks against cloud-based services. Experiments were conducted using real world benchmark datasets to evaluate the performance of the proposed approach. Experimental outcomes suggest that our proposed approach is able to detect and mitigate the FRC attacks with satisfactory accuracy and low overhead.

Network flow analysis for detection and mitigation of Fraudulent Resource Consumption (FRC) attacks in multimedia cloud computing

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and "pay-as-you-go" computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

Cloud computing platform provides on-demand access of services, and these services can also be customised, which enables the customers to use these services as per their requirement and they also need to pay according to their uses. Recent advancements in the field of cloud computing and internet technologies have attracted the organisations to shift their businesses on cloud. However, moving data and applications to a third party services provider also raises critical concern for security and privacy. Availability of cloud services is one of the most important security issues which directly affect the business of the cloud service providers and also its customers. Distributed denial of service (DDoS) attack on cloud is the root cause that challenges the availability of cloud services. This paper presents taxonomy of security issues in cloud, taxonomy of DDoS attacks in cloud, and taxonomy of DDoS defence mechanisms in cloud environment. We have also discussed, analysed, and compared some well-known methods for DDoS attack defence in cloud on different parameters that will help the victim to understand the risk and also assists them to select the proper defence technique to protect their cloud system from DDoS attacks.

Security challenges in cloud computing: state-of-art

Cloud is the requirement of today's competitive world that demand flexible, agile and adaptable technology to be at par with rapidly changing IT industry. Cloud offers scalable, on-demand, pay-as-you-go services to enterprise and has hence become a part of growing trend of organizations IT service model. With emerging trend of cloud the security concerns have further increased and one of the biggest concerns related to cloud is DDoS attack. DDoS attack tends to exhaust all the available resources and leads to unavailability of services in cloud to legitimate users. In this paper the concept of fog computing is used, it is nothing but an extension to cloud computing that performs analysis at the edge of the network, i.e. bring intelligence at the edge of the network for quick real time decision making and reducing the amount of data that is forwarded to cloud. We have proposed a framework in which DDoS attack traffic is generated using different tools which is made to pass through fog defender to cloud. Furthermore, rules are applied on fog defender to detect and filter DDoS attack traffic targeted to cloud.

Kriti Bhushan

Papers

Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment

Network flow analysis for detection and mitigation of Fraudulent Resource Consumption (FRC) attacks in multimedia cloud computing

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

Security challenges in cloud computing: state-of-art

DDoS attack defense framework for cloud using fog computing