Supply chain management (SCM) governance is the streamline of the IoT product life cycle from its production to delivery. Integrating blockchain with supply chain management is essential to ensure end-to-end tracking, trustiness between manufacturers and customers, fraud and counterfeit elimination, and customizing administrative costs and paperwork. This paper proposes an RFID ownership transfer protocol with the help of zk-SNARKs (Zero Knowledge-Succinct Noninteractive Arguments of Knowledge) using Ethereum blockchain. When the owner performs RFID transfer, the transferred information will be recorded on the blockchain using smart contracts. When using a smart contract to transfer ownership on the Ethereum blockchain, because the content on the blockchain will not be tampered with, all accounts in the Ethereum can view the transfer results and verify them. The privacy of the supply chain is attained by generating the proof of product code via zk-SNARKs algorithm. This algorithm also enhances the scalability of the supply chain system by creating a trusted setup in off-chain mode.

Software-Defined Network (SDN) segregates the control plane and the data plane to bring about a programmable network. The controller at the control plane runs network modules and sets rules for forwarding the packets in the switches that resides at the data plane. Though advantageous in several ways, SDN can fail when the controller is saturated by a flood of TCP SYN packets. SYN flood can be created using malicious spoofing of IP or MAC addresses or flash crowd. The existing solutions to mitigate SYN flood against the controller does not adequately handle MAC spoofing based SYN flood, and these are unable to distinguish between flash crowd and malicious traffic. To overcome some limitations in existing solutions, we propose a novel mechanism called AEGIS, which detect and mitigate SYN flood against the controller in SDN. AEGIS runs in the controller, and it regularly checks if there is a performance lag in the controller due to an ongoing SYN flood. If a performance degradation is detected, then AEGIS takes it an indication of SYN flood and it identifies whether it is due to spoofed addresses or flash crowd. Once the reason is found, the appropriate mitigation procedure is triggered. We evaluate AEGIS in testbed and emulator settings, and we compare the results of the evaluation with state-of-the-art solutions. The performance evaluation of AEGIS shows that it identifies the malicious SYN at an accuracy of 97.78%. Moreover, when there is no SYN flood, AEGIS takes 0.0637s to set up a successful TCP connection, which is 53.81% less than the time taken by the state-of-the-art solution, thus, it proves that AEGIS is lightweight.

The Internet-of-Things (IoT) network is growing big owing to its utility in smart applications. An IoT network is susceptible to security breaches, in majority due to the resource-constrained nature of IoT. Of the various breaches, the Distributed Denial-of-Service (DDoS) attack can snip off the network service to the users in various ways, such as consumption of server's resources, saturating link bandwidth, etc. These types of DDoS breaches can turn out to be a catastrophe in critical IoT use cases. This article delves into tackling the DDoS attack triggered by malicious wireless IoT on IoT servers. Our security scheme leverages the cloud and software-defined network (SDN) paradigm to mitigate the DDoS attack on IoT servers. We have proposed a novel mechanism named learning-driven detection mitigation (LEDEM) that detects DDoS using a semisupervised machine-learning algorithm and mitigates DDoS. We tested LEDEM in the testbed and emulated topology, and compared the results with state-of-the-art solutions. We achieved an improved accuracy rate of 96.28% in detecting DDoS attack.

Our world is moving toward an Internet of Things (IoT) era by connecting billions of IoT. There are several security loopholes in the IoT network. Intrusion can lead to performance degradation and pose a threat to data security. Hence, there is a need for a method to detect intrusion in the IoT networks. Existing solutions use supervised-learning-based intrusion detection methods that need a huge labeled data set for better accuracy. It is not easy to source out a huge labeled data set because the size of the IoT network is huge. To overcome some of the impediments in the existing solutions, we propose a novel SDRK machine learning (ML) algorithm to detect intrusion. SDRK leverages supervised deep neural networks (DNNs) and unsupervised clustering techniques. The intrusion detection and mitigation algorithms are placed in the fog nodes that are between IoT and cloud layers. We test our proposed methodology against the data deluge (DD) attack in the testbed. The SDRK model is tested on the benchmark NSL-KDD data set. We compare the results with state-of-the-art solutions. When testing with the NSL-KDD data set, we find that SDRK detects the attacks with improved accuracy of 99.78%.

Link Flooding Attack (LFA) is a genre of Distributed Denial of Service (DDoS) attack. LFA can cut off a target area from the network, without directly attacking the target. The attacker chooses links which when cut off will disconnect the target area and instruct the bots to flood those links with small packets. Some of the existing solutions are suitable for specific routing methods like shortest path routing or need cooperation between Autonomous Systems (AS). To overcome certain hitches of existing solutions, we have proposed a novel mechanism named BALANCE. It detects and mitigates LFA via hybrid-Software-Defined Network (SDN). SDN splits the control and data plane using OpenFlow protocol. Hybrid SDN has both legacy and SDN nodes, with a controller in the control plane. We have used Service Based Hybrid SDN (SBHS), which is a type of hybrid-SDN. BALANCE begins with an algorithm that chooses nodes in an AS to be SBHS enabled in such a way that the controller can get statistics of all the links in the AS. Next, congestion detection and location algorithms are implemented in the controller to find the congested links. Finally, LFA bot detection and mitigation algorithms are implemented in the controller to mitigate LFA. BALANCE was evaluated in testbed and emulator. We compared the results with state-of-the-art solutions. BALANCE was able to detect LFA bots at a precision of 97.64% and had HTTP response time of 2 seconds during the LFA attack.

Summary We propose a parallel generalization scheme for Singular Value Decomposition–based clustering algorithms. The scheme enables the clustering algorithm to generate a hierarchy of clusters instead of a flat set of clusters. The generalization scheme infers the number of levels to be formed and the number of clusters per level of the hierarchy automatically without depending on any user‐supplied parameter. The performance of the suggested hierarchical clustering algorithm was evaluated using the web directory taxonomy hosted by the Open Directory DMOZ. Empirical evaluations and statistical tests reveal that the proposed generalization scheme produces a superior cluster hierarchy when compared with two existing generalization techniques in terms of the precision, recall, f‐measure, and the rand index. The generalization scheme is well‐equipped to deal with large datasets and the speed‐up achieved by the parallelized generalization scheme over its sequential variant was measured using a multicore computer.

Summary We propose a distributed parallel algorithm for inferring the hierarchical groups present in a large‐scale text corpus. The algorithm is designed to deal with corpuses that typically do not fit into the main memory of a workstation computer. The key contribution of this paper lies in its proposal and verification of a parallel distributed algorithm that exploits the advantages of two complementary techniques based on (i) localized modularity optimization and (ii) spectral clustering. Based on our experimental observations, these are complementary in the sense that the former excels at finding coarse groups in a large‐scale network, while the latter demands a heavy memory footprint but is effective in inferring tightly knit fine‐grained groups. Empirical evaluation of the distributed implementation scheme shows that the algorithm exhibits a significant speed‐up when compared to existing algorithms like Louvain and, at the same time, produces better quality clusters than either Louvain or spectral clustering algorithms in terms of the F‐score and Rand index.

Summary We propose a parallelization scheme for an existing algorithm for constructing a web‐directory, that contains categories of web documents organized hierarchically. The clustering algorithm automatically infers the number of clusters using a quality function based on graph cuts. A parallel implementation of the algorithm has been developed to run on a cluster of multi‐core processors interconnected by an intranet. The effect of the well‐known Latent Semantic Indexing on the performance of the clustering algorithm is also considered. The parallelized graph‐cut based clustering algorithm achieves an F‐measure in the range [0.69,0.91] for the generated leaf‐level clusters while yielding a precision‐recall performance in the range [0.66,0.84] for the entire hierarchy of the generated clusters. As measured via empirical observations, the parallel algorithm achieves an average speedup of 7.38 over its sequential variant, at the same time yielding a better clustering performance than the sequential algorithm in terms of F‐measure. Copyright © 2015 John Wiley & Sons, Ltd.