Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

With advances in Fog and edge computing, various problems such as data processing for large Internet of things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for smart grids incur high computation and communication costs, and in recent years there have been efforts to leverage fog computing with smart grids to overcome these limitations. In this paper, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of Paillier crypto-system is used to encrypt consumption data of the smart meter users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the smart meters malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the PPFA aggregation scheme.

With the evolution of the Internet of Things, mutual sharing of information becomes easy among various smart devices across the globe. In such an environment, smart healthcare also needs to be expedited with an aim to provide remote diagnosis to patients located across the globe. However, as the sensitive information about a patient travels using an open channel (i.e., the Internet), there are chances of misusing this information. Hence, there is a requirement for a strong crypto mechanism for securely processing smart healthcare data. Most of the solutions reported in the literature use heavy computation-based solutions for providing security to smart healthcare data, which may not be suitable in view of less resources for modern smart devices. In this article, we propose a novel lattice-based secure cryptosystem for smart healthcare (LSCSH) in future smart cities. LSCSH uses a lightweight key exchange and an authentication mechanism at several stages. The proposed scheme has been evaluated with respect to the communication and computation costs in comparison to the other competitive schemes. The results obtained prove the efficacy of the proposed scheme in comparison to the existing schemes.

Smart grid (SG) is an emerging technology which provides many services to the end users and utilities such as-load management, frequency regulation, and grid stability. Although many solutions exist to provide these services in a secure manner, but these solutions are not adequate keeping in view of the heavy cryptographic primitives execution on these devices. Hence, in this paper, GUARDIAN, a blockchain-based secure demand response management scheme is presented so as to take energy trading decisions securely for managing the overall load of residential, commercial, and industrial sectors. In GUARDIAN, the miner nodes, which are block verifiers, are selected using their power consumption and processing power. These nodes are responsible for authenticating the energy transactions in SG. The energy transaction is initialized by an end user which creates the block of transaction to trade the energy. The miner nodes then validate these blocks and adds these in the blockchain. The successful energy trade occurs only for the blocks which are in the blockchain. The proposed scheme is lightweight in terms of communication and computation costs. Moreover, the results obtained demonstrate the effectiveness of proposed scheme for secure demand response management in the SG.