Abstract

Leishmania donovani causes the neglected tropical disease visceral leishmaniasis. There is currently no vaccine available to combat the disease. Therefore, chemotherapy is the only treatment option available. However, the available drugs have various adverse effects on the host. As a result, new drugs are needed to treat the disease; natural compounds can be used as an alternative to traditional drugs. This study examined apigenin, diosmin, and naringin as prospective inhibitors against the Leishmania donovani Sterol 24-C- methyltransferase enzyme (LdSMT). All three compounds have a high affinity for the LdSMT and remain stable for 100 ns during the molecular dynamics simulation. In the cell viability assays, these compounds showed excellent IC50 values, with apigenin having the best IC50 value of 16.11 µM against Leishmania donovani promastigote. The findings of this study showed that all three flavonoids could be used as a safe alternative to conventional anti-leishmanial drugs.

Abstract

The Internet of Things (IoT)-enabled ride sharing is one of the most transforming and innovative technologies in the transportation industry. It has myriads of advantages, but with increasing demands there are security concerns as well. Traditionally, cryptographic methods are used to address the security and privacy concerns in a ride sharing system. Unfortunately, due to the emergence of quantum algorithms, these cryptographic protocols may not remain secure. Hence, there is a necessity for privacy-preserving ride sharing protocols which can resist various attacks against quantum computers. In the domain of privacy preserving ride sharing, a threshold private set intersection (TPSI) can be adopted as a viable solution because it enables the users to determine the intersection of private data sets if the set intersection cardinality is greater than or equal to a threshold value. Although TPSI can help to alleviate privacy concerns, none of the existing TPSI is quantum secure. Furthermore, the existing TPSI faces the issue of long-term security. In contrast to classical and post quantum cryptography, quantum cryptography (QC) provides a more robust solution, where QC is based on the postulates of quantum physics (e.g., Heisenberg uncertainty principle, no cloning theorem, etc.) and it can handle the prevailing issues of quantum threat and long-term security. Herein, we propose the first QC based TPSI protocol which has a direct application in privacy preserving ride sharing. Due to the use of QC, our IoT-enabled ride sharing scheme remains quantum secure and achieves long-term security as well.

Abstract

Reaction networks can display a wide array of dynamics. However, it is possible for different reaction networks to display the same dynamics. This phenomenon is called dynamical equivalence and makes network identification a hard problem. We show that to find a strongly endotactic/endotactic/consistent/conservative realization (when it exists) that is dynamically equivalent to the mass-action system generated by a given network it suffices to consider only the source vertices of the given network. In addition, we show that weakly reversible deficiency one realizations are not unique. We also present a characterization of the dynamical relationships that exist between several types of weakly reversible deficiency one networks.

Abstract

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi- dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm.

Abstract

Excessive nutrient loading into the inland water bodies and resulting algal blooms have been a major threat to the water quality of the inland water bodies such as lakes and reservoirs where the water is relatively stagnant for longer duration. To improve the conditions, it is essential to study and understand the role of the contributing factors in increasing the nutrient load coming into the water body from the watershed. In general, the excessive usage of inorganic (NPK) fertilizers are considered to be the spearhead behind the increasing nutrient output from the watershed, but their impact has not been well evaluated due to several constraints. So, this study tries to evaluate the role of the fertilizer application on the nutrient yield from the watershed into an inland water body. Nagarjuna Sagar reservoir and its contributing

Abstract

A persistent, targeted cyber attack is called an advanced persistent threat (APT) attack. The attack is mainly launched to gain sensitive information, take over the system, and for financial gain, which creates nowadays more hurdles and challenges for the organization in preventing, detecting, and recovering from such attacks. Due to the nature of APT attacks, it is difficult to detect them quickly. Therefore machine learning techniques come into these research areas. This study uses deep and machine learning models such as random forest, decision tree, convolutional neural network, multilayer perceptron and so forth to categorize and effectively detect APT attacks by utilizing publicly accessible datasets. The datasets used in this study are CSE-CIC-IDS2018, CIC-IDS2017, NSL-KDD, and UNSW-NB15. This study proposes the hybrid ensemble machine learning model, a mixed approach of random forest and XGBoost classifiers. It has obtained the maximum prediction accuracy of 98.92%, 99.91%, 99.24%, and 97.11% for datasets CSE-CIC-IDS2018, CIC-IDS2017, NSL-KDD, and UNSW-NB15, with a false positive rate of 0.52%, 0.12%, 0.62%, and 5.29% respectively. These results are compared to other closely related recent studies in the literature. Our experiment's findings show that our model has performed significantly better for all datasets.

Abstract

The most significant and critical infrastructures, such as the electricity utilities, clean water facilities, nuclear plants and manufacturing industries are controlled and supervised by the industrial control systems. These systems undergo through a metamorphosis as a result of the Industry 4.0 revolution, which emphasises enhanced connectivity and flexibility with the Internet of Things (IoT) and cloud computing technologies. As the data is transferred across the Internet, Industry 4.0 communication can be easily attacked by launching different potential attacks. As a consequence, we attempt to propose a novel certificate-based access control and key establishment scheme for securing Industry 4.0 communication, called ACKS-IA. It offers access control and key establishment between smart industrial devices, as well as between a smart device and its associated cloud server. A formal security analysis of ACKS-IA through the broadly-accepted Burrows–Abadi– Needham (BAN) logic is provided. It confirms that ACKS-IA is secured and provides secure mutual authentication among the communication entities. The detailed informal security analysis and comparative study with the existing related schemes reveal that the proposed ACKS-IA is secured and efficient in terms of communication cost, computation cost, and security and functionality features including anonymity and untraceability as compared to other competing schemes. Finally, a real testbed implementation of ACKS-IA is provided to measure its effect on important performance attributes.

Abstract

Biometric signal consisting of irrelevant or non-distinctive features can contain useful correlational properties that privacy-preserving verification schemes can exploit. While an efficient protocol for iris verification using noise has been presented, it is not applicable to other widely used modalities, i.e., face and fingerprint, since the methods of noise extraction and comparison are different. In this work, we design a verification protocol for secure dot product computation and also propose noise extraction mechanisms for face and fingerprint modalities. We evaluate the performance of the protocol on CFP, LFW, CelebA, FVC 2004 DB1A, DB2A, DB3A, and SOCOFing datasets. While the protocol exhibits a slight degradation in accuracy, it provides information-theoretic security with a practical computational complexity.

Abstract

This paper discusses the development of autopilot hardware for small fixed-wing air vehicles. Weight constraint is the critical factor in developing such hardware. The sensors and communication devices are selected based on the requirements and constraints of these air vehicles. The sensors used in the hardware are calibrated using a three-axis rotating platform. The software written in the autopilot hardware is flexible enough to incorporate complex estimation and control algorithms along with the hardware-in-loop simulations. Linear output feedback controllers are designed for fixed wing micro and nano air vehicles. Successful flight trials are conducted to demonstrate the utility of the autopilot hardware for small fixed-wing air vehicles

Abstract

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol’s security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols.