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There is a massive growth in the rate of heterogeneous devices configured in the Internet of Things (IoT) environment for efficient communication. The IoT devices are limited in resources, and there are no defined protocols in terms of security during communication in the IoT-based platforms. Several solutions are framed to make communication secure in the IoT ecosystem. However, the existing schemes need to be more reliable to handle the cyber threats and unwarranted incidents (such as intrusions, anomalies and attacks) coming from IoT endpoints owing to the unstructured patterns of IoT data and dynamic network conditions. Moreover, heavy cryptographic primitives have their deployment challenges due to the resource constraints of the IoT ecosystem. The dynamic nature of IoT traffic requires flexible and varied rules to handle the threats in different deployment scenarios. Therefore, a programmable interface enabled through Software-defined Networking (SDN) can handle heterogeneous threats and incidents in the IoT cyber world. Thus, in this paper, we have designed a novel framework, SecureFlow, an intrusion detection and dynamic rule configuration system based on the knowledge-based and data-driven ensemble. The proposed framework is robust and fault tolerant owing to dual-layer Intrusion Detection System (IDS) and rule configuration modules that can work without one of them. SecureFlow validated through several experiments performed through emulations in Mininet. The results depict that the proposed framework is effective and promising. [ABSTRACT FROM AUTHOR]

With the ongoing trend of smart and Internet-connected objects being deployed across a broad range of applications, there is also a corresponding increase in the amount of data movement across different geographical regions. This, in turn, poses a number of challenges with respect to big data storage across multiple locations, including cloud computing platform. For example, the underlying distributed file system has a large number of directories and files in the form of gigantic trees, which are difficult to parse in polynomial time. Moreover, with the exponential increase of big data streams (i.e., unbounded sets of continuous data flows), challenges associated with indexing and membership queries are compounded. The capability to process such significant amount of data with high accuracy can have significant impact on decision-making and formulation of business and risk-related strategies, particularly in our current Industrial Internet of Things environment (IIoT). However, existing storage solutions are deterministic in nature. In other words, they tend to consume considerable memory and CPU time to yield accurate results. This necessitates the design of efficient quality of service-aware IIoT applications that are able to deal with the challenges of data storage and retrieval in the cloud computing environment. In this paper, we present an effective space-effective strategy for massive data storage using bloom filter (BF). Specifically, in the proposed scheme, the standard BF is extended to incorporate fuzzy-enabled folding approach, hereafter referred to as fuzzy folded BF (FFBF). In FFBF, fuzzy operations are used to accommodate the hashed data of one BF into another to reduce storage requirements. Evaluations on UCI ML AReM and Facebook datasets demonstrate the efficacy of FFBF, in terms of dealing with approximately 1.9 times more data as compared to using the standard BF. This is also achieved without affecting the false positive rate and query time. [ABSTRACT FROM AUTHOR]

With the exponential growth of technologies such as IoT, edge computing, and 5G, a tremendous amount of structured and unstructured data is being generated from different applications in the smart citiy environment in recent years. Thus, there is a need to develop sophisticated techniques that can efficiently process such huge volumes of data. One of the important components of smart cities, ITS, has led to many applications, including surveillance, infotainment, real-time traffic monitoring, and so on. However, its security, performance, and availability are major concerns facing the research community. The existing solutions, such as cellular networks, RSUs, and mobile cloud computing, are far from perfect because these are highly dependent on centralized architecture and bear the cost of additional infrastructure deployment. Also, the conventional methods of data processing are not capable of handling dynamic and scalable data efficiently. To mitigate these issues, this article proposes an advanced vehicular communication technique where RSUs are proposed to be replaced by edge computing platforms. Then secure V2V and V2E communication is designed using the Quotient filter, a probabilistic data structure. In summary, a smart security framework for VANETs equipped with edge computing nodes and 5G technology has been designed to enhance the capabilities of communication and computation in the modern smart city environment. It has been experimentally demonstrated that use of edge nodes as an intermediate interface between vehicle and cloud reduces access latency and avoids congestion in the backbone network, which allows quick decisions to be made based on the traffic scenario in the geographical location of the vehicles. The proposed scheme outperforms the conventional vehicular models by providing an energy-efficient secure system with minimum delay. [ABSTRACT FROM AUTHOR]