Abstract

IoT-enabled Smart Grid (IoT-SG) is an emerging paradigm that enables the bi-direction communication of IoT devices and hardware to efficiently collect and transmit the consumer’s information over the Internet. However, the underlying open communication network and resource-constrained capabilities of devices invite manifold challenges and threats in terms of security and privacy. To alleviate such issues, we designed a private blockchain-based access control protocol for IoT-SG using Physically Unclonable Function (PUF). Our protocol allows the Service Provider (SP) and smart meters to transfer the data in an efficient and secure manner. The participating SPs form the Peer-to-Peer (P2P) network, and each peer node is responsible for securely creating the blocks from the gathered data. Thereafter, all the peer nodes employ a voting-based consensus mechanism to verify and add the recently created block …

Abstract

With the continuous improvement of the public medical system, the current medical service industry is more intelligent. Among them, the Internet of Medical Things (IoMT) plays a significant role in intelligent medicine. The emergence of remote wireless monitoring platforms in the IoMT systems has significantly reduced the risk of virus infection among medical staff. However, data is easy to be stolen by criminals in the transmission process. To mitigate these issues, we propose a three-factor enhanced protocol for monitoring patient body information. The proposed protocol can provide the confidentiality of transmitted data and the privacy of doctors and patients. The security of the protocol is demonstrated in the Random Oracle Model (ROM) for formal security. In addition, by comparing the proposed protocol with other related works, our design has efficient performance both in execution time and communication costs.

Abstract

Multiple kinds of ransomware are currently posing a growing threat to the Internet users. Important user data is encrypted by the trendy ransomware, and its recovery requires payment of a ransom in terms of some amount of money. The trend of crypto-currencies may be a contributing factor to the rise of ransomware attacks. From time to time, different mechanisms for detection and mitigation of ransomware attacks have been proposed. However, the performance of the ransomware detection process can be improved through the deployment of Machine Learning/Deep Learning based mechanisms. In this article, we propose a novel Spline Interpolation envisioned Neural Network based Ransomware Detection Scheme (in short, we call it as SINN-RD). Additionally, we introduce the mechanisms for normalizing the data and generating the new features from the log files. The conducted security analysis of the

Abstract

Designing a secure and efficient authentication protocol is crucial and challenging in the mobility network. Due to the seamless roaming of mobile users over multiple foreign agents and the broadcast nature of the communication channel, the mobile networks are often exposed to several network attacks. To achieve perfect authentication and secure communication among mobility entities like MU (Mobile User), FA (Foreign Agent) and HA (Home Agent), the researchers have proposed numerous authentication protocols in the past. However, the existing protocols for the mobility environments are insufficient to address the fundamental security concerns and an adversary can impersonate the mobile user at anytime. Thus, we propose Mobile-Chain, a secure blockchain-based authentication system for mobility environments. The proposed Mobile-Chain is designed to protect user privacy and guarantees provable

Abstract

We investigate whether it is possible to teleport the coherence of an unknown quantum state from Alice to Bob by communicating lesser number of classical bits in comparison to what is required in teleporting an unknown quantum state. We find that we cannot do perfect teleportation of coherence with one bit of classical communication for an arbitrary qubit. However, we find that if the qubit is chosen from equatorial and polar circles, then teleportation of coherence will be possible with transfer of one cbit of information if we have maximally entangled states as a shared resource. In the case of resource being a non maximally entangled state, we can teleport quantum coherence with a certain probability of success. In a general teleportation protocol for coherence, we derive a compact formula for the final state at Bob’s lab in terms of composition of the completely positive maps corresponding to the shared resource state and joint POVM performed by Alice on her qubit and the unknown state. With the help of this formula, we investigate the amount of coherence teleported when the resource shared state is the maximally entangled mixed state and Werner state for the unknown pure as well as mixed states state at Alice’s lab. In particular, we show that when the Werner state becomes separable then also the amount of teleported coherence is non-zero, implying the possibility of teleportation of coherence without entanglement. We have also shown that teleportation of coherence of an arbitrary state with real matrix elements is exactly possible with the help of maximally entangled state as a resource.

Abstract

Blockchains have proven to provide a high level of performance in terms of security and reliability for various applications like cryptocurrencies and Internet-of-Things (IoT). Prism is a recent blockchain algorithm that achieves the physical limit on throughput and latency without compromising security. In recent days, reinforcement learning approaches are investi- gated in traditional blockchains, to improve performance. In this work, we apply Deep Reinforcement Learning (DRL) to one of the promising blockchain protocols, Prism, to optimize its performance. We propose a Deep Reinforcement Learning- based Prism Blockchain (DRLPB) scheme which dynamically optimizes the parameters of the Prism blockchain and helps in achieving a better performance. In DRLPB, we apply two widely used DRL algorithms, Dueling Deep Q Networks (DDQN) and Proximal Policy Optimization (PPO). This work presents a novel approach to applying DDQN and PPO to a blockchain protocol and comparing the performance. The DRLPB scheme adapts the Prism blockchain parameters to enhance the number of votes upto 84% more than Prism, while still preserving the security and latency performance guarantees of Prism.

Abstract

Integrating scalability, interoperability, and security has become crucial with the widespread adoption of Internet of Things (IoT)-enabled smart city solutions. In this context, the oneM2M provides promising technical specifications for an interoperable and secure IoT/M2M system. This paper focuses on the potential threats and their impact on oneM2M standardbased smart city deployments. Further, configurations for baseline security of the oneM2M open-source implementation, called eclipse OM2M, are presented. Recommendations are proposed based on actual on-ground tests conducted on OM2M-based smart city deployment of IIIT Hyderabad (IIIT-H) in India. The tests cover passive eavesdropping, performing replay attacks, brute force on credentials, denial of service, and analysis of access control policies. Index Terms—Eclipse OM2M, Internet of Things (IoT) security, IoT security standardisation, oneM2M, smart city

Abstract

Cognitive brain mapping is enjoying its growth with the availability of large open data sharing efforts as well as the application of modern machine learning and deep learning methods. In this article, we review some of the current practices in cognitive neuroscience predominantly focusing on functional imaging and highlight the tremendous opportunities fostered by the unprecedented scale of datasets in cognitive neuroscience. We also point out challenges and limitations to keep in mind while working with these datasets.

Abstract

The construction of a decision tree on a given data set involves certain features. In case these features have NULL values during the production environment, the decision tree performance suffers. In this work, we develop a methodology to generate subspace deci- sion tree classifiers, where each decision tree uses non-overlapping subsets of feature sets. We build an ensemble classifier over these subspace classifiers to get better performance. We consider different levels of missing data in our ensemble classifier, to show that it is resilient to missing data and can also give better performance even in cases of incomplete data (that is, data with missing values). We show the viability of our methodology using real-life and synthetic data sets. Our approach delivers a simple yet effective missing data resilient decision tree classifier

Abstract

Retailer revenue is significantly impacted by item placement. Given the prevalence and popularity of medium-to-large-sized retail stores, several research efforts have been made towards facilitating item/itemset placement for improving retailer revenue. However, they fail to consider the issue of inventory of the items w.r.t. itemset placement. Notably, the inventory of a given item refers to the number of instances of that item that are available to the retailer for sales purposes. Moreover, efficient retrieval and placement of top-revenue itemsets in the retail store slots cannot be performed by existing approaches. Our key contributions are summarized as follows. First, we introduce the notion of inventory in retail itemset placement. Second, we propose an inventory-aware indexing scheme, designated as IRIS, for efficiently retrieving high-revenue itemsets. Moreover, we propose the IRPS inventory-aware itemset placement scheme, which exploits the IRIS indexing scheme, for facilitating improved retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate the effectiveness of our proposed itemset indexing and placement schemes in improving retailer revenue.