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The Internet of Things is visualized as a fundamental networking model that bridges the gap between the cyber and real-world entity. Uniting the real-world object with virtualization technology is opening further opportunities for innovation in nearly every individual's life. Moreover, the usage of smart heterogeneous multimedia devices is growing extensively. These multimedia devices that communicate among each other through the Internet form a unique paradigm called the Internet of Multimedia Things (IoMT). As the volume of the collected data in multimedia application increases, the security, reliability of communications, and overall quality of service need to be maintained. Primarily, distributed denial of service attacks unveil the pervasiveness of vulnerabilities in IoMT systems. However, the Software Defined Network (SDN) is a new network architecture that has the central visibility of the entire network, which helps to detect any attack effectively. In this regard, the combination of SDN and IoMT, termed SD-IoMT, has the immense ability to improve the network management and security capabilities of the IoT system. This article proposes an SDNassisted two-phase detection framework, namely SD-IoMT-Protector, in which the first phase utilizes the entropy technique as the detection metric to verify and alert about the malicious traffic. The second phase has trained with an optimized machine learning technique for classifying different attacks. The outcomes of the experimental results signify the usefulness and effectiveness of the proposed framework for addressing distributed denial of service issues of the SD-IoMT system. [ABSTRACT FROM AUTHOR]

Abstract Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source. These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC's are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time. The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection. Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC's with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication. Highlights • An adaptive EDC authentication technique with the help of a centralized cloud datacenter. • Authentication is initiated by the cloud and then all EDCs authenticate with each other. • Sustainable load balancing technique by considering the load of the destination EDCs. • The proposed approach combines both the authentication and load balancing technique. [ABSTRACT FROM AUTHOR]

Big data streaming has become an important paradigm for real-time processing of massive continuous data flows in large scale sensing networks. While dealing with big sensing data streams, a Data Stream Manager (DSM) must always verify the security (i.e. authenticity, integrity, and confidentiality) to ensure end-to-end security and maintain data quality. Existing technologies are not suitable, because real time introduces delay in data stream. In this paper, we propose a Dynamic Prime Number Based Security Verification (DPBSV) scheme for big data streams. Our scheme is based on a common shared key that updated dynamically by generating synchronized prime numbers. The common shared key updates at both ends, i.e., source sensing devices and DSM, without further communication after handshaking. Theoretical analyses and experimental results of our DPBSV scheme show that it can significantly improve the efficiency of verification process by reducing the time and utilizing a smaller buffer size in DSM. [ABSTRACT FROM AUTHOR]

Applications in risk-critical domains such as emergency management and industrial control systems need near-real-time stream data processing in large-scale sensing networks. The key problem is how to ensure online end-to-end security (e.g., confidentiality, integrity, and authenticity) of data streams for such applications. We refer to this as an online security verification problem. Existing data security solutions cannot be applied in such applications as they cannot deal with data streams with high-volume and high-velocity data in real time. They introduce a significant buffering delay during security verification, resulting in a requirement for a large buffer size for the stream processing server. To address this problem, we propose a Dynamic Key-Length-Based Security Framework (DLSeF) based on a shared key derived from synchronized prime numbers; the key is dynamically updated at short intervals to thwart potential attacks to ensure end-to-end security. Theoretical analyses and experimental results of the DLSeF framework show that it can significantly improve the efficiency of processing stream data by reducing the security verification time and buffer usage without compromising security. [ABSTRACT FROM AUTHOR]

Data processing in real-time brings better business modeling and an intuitive plan of action. Internet of things (IoT), being a source of sensitive data collected and communicated through either public or private networks, requires better security from end to end to uphold integrity, quality, and acceptability of data. Designing an adaptive solution plays a vital role where IoT is deployed for the sensing-as-a-services in the critical infrastructure and near real-time decision making by deploying data analysis in the edge datacenters. Again, securing the system with user's demand and device specifications is a challenging and open research problem. This paper proposed a decision tree based user-centric security approach named D e c i s i o n T S e c that provides a secure channel for communication in IoT networks, combining edge datacenters in the network edges. Further, the proposed D e c i s i o n T S e c is validated by experimenting with the real-time testbed for the system performance along with the theoretical security validation. [ABSTRACT FROM AUTHOR]

• An operation on graphs is defined that obeys the properties of an algebraic group. • The proposed mechanism represents plaintext, ciphertext, and key as a graph. • It provides an unambiguous mapping process from plaintext to graph and vice versa. • Unlike other symmetric cryptosystems, the encryption and decryption keys are not the same. • The decryption key graph is the inverse graph of the encryption key graph. Data confidentiality and integrity are essential security goals in the data communication in the smart city or similar applications. Despite significant changes in modern data communication systems and devices, the cryptosystems are still the primary option to achieve data security. Several cryptosystems have been developed, implemented and standardized to date. However, there is always a continuous demand for a new cryptosystem for providing security during data communication. In this paper, a novel symmetric cryptosystem is presented for achieving data confidentiality. The proposed cryptosystem is a graph-based system, where an algebraic structure of directed graphs along with a novel operation is defined to provide the base of the cryptosystem. Here, a directed graph represents a plaintext, a ciphertext, and a secret key as well. The defined operation is used for encrypting and decrypting plaintext and ciphertext graphs. The proposed cryptosystem provides a simplified transformation process and representation of plaintext to plaintext graph. The correctness of the proposed cryptosystem is validated, and also shown that the conventional brute-force approach to obtain the key from either plaintext or ciphertext is exponential and this computational time is significantly higher than AES. This implies that the proposed cryptosystem is highly secured against this attack. The proposed cryptosystem is implemented and compared with AES and the results infer that it incurs a marginally higher execution time than the AES for larger plaintext due to the involvement of matrix operations. [ABSTRACT FROM AUTHOR]