ABSTRACT Public auditing is a method used to verify the integrity of data stored in the cloud without requiring access to the actual data. However, the advancement of quantum computers poses significant security threats to existing public auditing schemes, as these schemes are based on conventional cryptography hard problems, which are vulnerable to quantum attacks. To address this, NIST has launched the development of post‐quantum cryptographic primitives and protocols. Among the various approaches, lattice‐based cryptography (LBC) is considered one of the most promising candidates due to its strong security guarantees and inherent resistance to quantum attacks. Leveraging LBC, several researchers have proposed lattice‐based public auditing (LBPA) schemes for cloud storage security based on lattice hardness assumptions. This paper provides a comprehensive survey of existing LBPA schemes for cloud storage, presenting a detailed taxonomy and analyzing their similarities, differences, and performance. Additionally, it highlights key challenges and outlines future research directions for designing efficient and secure public auditing schemes in the post‐quantum era.

The rapid expansion of cloud computing has made it increasingly difficult for users to determine the most appropriate cloud service provider (CSP). The provider offers diverse services, typically assessed based on quality of service (QoS) attributes, including throughput, reliability, availability, latency, and response time. Researchers often present these QoS attributes in a decision matrix and apply multi-attribute decision-making (MADM) algorithms to evaluate and rank the CSPs. However, in practical scenarios, not all CSPs satisfy every QoS attribute, leading to unavailable performance measure values in a decision matrix. To address this challenge, we develop a hybrid MADM framework for CSP selection that handles an incomplete decision matrix. The framework integrates QoS-aware MADM (QMADM) algorithms, QTOPSIS-W and QVIKOR-W with attribute weights (QMADM-W). It employs three imputation techniques to determine unavailable performance values: minimum (min), maximum (max), and mean. The weights are derived using the analytic hierarchy process (AHP) and the analytic network process (ANP). Simulation results using the QoS for web services (QWS) dataset demonstrate the framework's effectiveness in QTPOSIS-W, with consistent and robust performance observed under the mean imputation technique through sensitivity analysis. The proposed algorithms offer a reliable solution for selecting an optimal CSP, even for an incomplete decision matrix.

In a healthcare IoT based Fog System, malicious attacks may alter patient’s critical health data, which may lead to severe repercussions. This makes it incumbent to adopt authentication mechanisms for preventing unauthorised access and implement data encryption to enhance the security in the system. This work presents an efficient learning integrated dynamic attribute based encryption mechanism by reducing encryption, decryption and communication costs associated in a Fog system with IoT devices and dynamic attribute updates. The Ciphertext Policy Attribute Based Encryption approach (CP-ABE) has been integrated with a Deep Neural Network model to use learning patterns related to attributes. This in turn reduces the communication cost incurred by the resource limited end devices for dynamic attribute updates. Further, an access control mechanism has been implemented by optimizing the system due to dynamic attributes and by analysing the updates in the access policy defined in CP-ABE. The Deep Neural Network has been trained using existing data sets and experimental results are presented by performing analysis on neural network parameters.

Detecting social bots and identifying social botnet communities are extremely important in online social networks (OSNs). In this paper, we first construct a weighted signed Twitter network graph based on the behavioral similarity and trust values between the participants (i.e., OSN accounts) as weighted edges. The behavioral similarity is analyzed from the viewpoints of tweet-content similarity, shared URL similarity, interest similarity, and social interaction similarity for identifying similar types of behavior (malicious or not) among the participants in the Twitter network; whereas the participant's trust value is determined by a random walk model. Next, we design two algorithms - Social Botnet Community Detection (SBCD) and Deep Autoencoder based SBCD (called DA-SBCD) - where the former detects social botnet communities of social bots with malicious behavioral similarity, while the latter reconstructs and detects social botnet communities more accurately in presence of different types of malicious activities. Finally, we evaluate the performance of proposed algorithms with the help of two Twitter datasets. Experimental results demonstrate the efficacy of our algorithms with better performance than existing schemes in terms of normalized mutual information (NMI), precision, recall and F-measure. More precisely, the DA-SBCD algorithm achieves about 90% precision and exhibits up to 8% improvement on NMI.

Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, a learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes' theorem, and the indirect trust is derived from the Dempster-Shafer theory (DST) to determine the trustworthiness of each participant accurately. Experimentation has been performed on two Twitter data sets, and the results illustrate that the proposed algorithm achieves improvement in precision, recall, F-measure, and accuracy compared with existing approaches for MSBD.

A fundamental challenge in the design of Wireless Sensor Network (WSN) is to enhance the network lifetime. The area around the Sink forms a bottleneck zone due to heavy traffic-flow, which limits the network lifetime in WSN. This work attempts to improve the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by considering a duty cycled WSN. An efficient communication paradigm has been adopted in the bottleneck zone by combining duty cycle and network coding. Studies carried out to estimate the upper bounds of the network lifetime by considering (i) duty cycle, (ii) network coding and (iii) combinations of duty cycle and network coding. The sensor nodes in the bottleneck zone are divided into two groups: simple relay sensors and network coder sensors. The relay nodes simply forward the received data, whereas, the network coder nodes transmit using the proposed network coding based algorithm. Energy efficiency of the bottleneck zone increases because more volume of data will be transmitted to the Sink with the same number of transmissions. This in-turn improves the overall lifetime of the network. Performance metrics, namely, packet delivery ratio and packet latency have also been investigated. A detailed theoretical analysis and simulation results have been provided to show the efficacy of the proposed approach.