LAAP: Lightweight anonymous authentication protocol for D2D-Aided fog computing paradigm

Fog computing is a new paradigm that extends cloud computing and services to the edge of the network. Although it has several distinct characteristics, however, the conventional fog computing model does not support some of the imperative features such as D2D communications, which can be useful for several critical IoT applications and services. Besides, fog computing faces numerous new security and privacy challenges apart from those inherited from cloud computing, however, security issues in fog computing have not been addressed properly. In this article, first we introduce a new privacy-preserving security architecture for fog computing model with the cooperative D2D communication support, which can be useful for various IoT applications. Subsequently, based on the underlying foundation of our proposed security architecture we design three lightweight anonymous authentication protocols (LAAPs) to support three distinct circumstances in D2D-Aided fog computing. In this regard, we utilize the lightweight cryptographic primitives like one-way function and EXCLUSIVE-OR operations, which will cause limited computational overhead for the resource limited edge devices

Lightweight and privacy-preserving two-factor authentication scheme for IoT devices

Device authentication is an essential security feature for Internet of Things (IoT). Many IoT devices are deployed in the open and public places, which makes them vulnerable to physical and cloning attacks. Therefore, any authentication protocol designed for IoT devices should be robust even in cases when an IoT device is captured by an adversary. Moreover, many of the IoT devices have limited storage and computational capabilities. Hence, it is desirable that the security solutions for IoT devices should be computationally efficient. To address all these requirements, in this paper, we present a lightweight and privacy-preserving two-factor authentication scheme for IoT devices, where physically uncloneable functions have been considered as one of the authentication factors. Security and performance analysis show that our proposed scheme is not only robust against several attacks, but also very efficient in terms of computational efficiently

An efficient privacy-preserving authentication scheme for energy internet-based vehicle-to-grid communication

The energy Internet (EI) represents a new electric grid infrastructure that uses computing and communication to transform legacy power grids into systems that support open innovation. EI provides bidirectional communication for analysis and improvement of energy usage between service providers and customers. To ensure a secure, reliable, and efficient operation, the EI should be protected from cyber attacks. Thus, secure and efficient key establishment is an important issue for this Internet-based smart grid environment. In this paper, we propose an efficient privacy-preserving authentication scheme for EI-based vehicle-to-grid communication using lightweight cryptographic primitives such as one-way non-collision hash functions. In our proposed scheme, a customer can securely access services provided by the service provider using a symmetric key established between them. Detailed security and performance analysis of our proposed scheme are presented to show that it is resilient against many security attacks, cost effective in computation and communication, and provides an efficient solution for the EI

PrivHome: Privacy-preserving authenticated communication in smart home environment

A smart home enables users to access devices such as lighting, HVAC, temperature sensors, and surveillance camera. It provides a more convenient and safe living environment for users. Security and privacy, however, is a key concern since information collected from these devices are normally communicated to the user through an open network (i. e. Internet) or system provided by the service provider. The service provider may store and have access to these information. Emerging smart home hubs such as Samsung SmartThings and Google Home are also capable of collecting and storing these information. Leakage and unauthorized access to the information can have serious consequences. For example, the mere timing of switching on/off of an HVAC unit may reveal the presence or absence of the home owner. Similarly, leakage or tampering of critical medical information collected from wearable body sensors can have serious consequences. Encrypting these information will address the issues, but it also reduces utility since queries is no longer straightforward. Therefore, we propose a privacy-preserving scheme, PrivHome. It supports authentication, secure data storage and query for smart home systems. PrivHome provides data confidentiality as well as entity and data authentication to prevent an outsider from learning or modifying the data communicated between the devices, service provider, gateway, and the user. It further provides privacy-preserving queries in such a way that the service provider, and the gateway does not learn content of the data. To the best of our knowledge, privacy-preserving queries for smart home systems has not been considered before. Under our scheme is a new, lightweight entity and key-exchange protocol, and an efficient searchable encryption protocol. Our scheme is practical as both protocols are based solely on symmetric cryptographic techniques. We demonstrate efficiency and effectiveness of our scheme based on experimental and simulation results, as well as comparisons to existing smart home security protocols

Isolation, characterization and mapping of temperature-sensitive mutants of mycobacteriophage I3

Eighteen temperature-sensitive mutants of mycobacteriophage I3 have been isolated and partially characterized. All the mutants were defective in vegetative replication. Based on temperature shift experiments with the temperature sensitive mutants, the thermosensitive phase of the phage development period has been characterized for each mutant. The genes have been mapped by recombination analysis. The early, continuous and middle genes seem to cluster on the genetic ma

Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI

Reasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach

An efficient privacy-preserving authenticated key agreement scheme for edge-assisted internet of drones

There has been a a significant increase in the popularity of using Unmanned Aerial Vehicles (UAVs), popularly known as drones, in several applications. In many application scenarios, UAVs are deployed in missions where sensitive data is collected, such as monitoring critical infrastructure, industrial facilities, crops, and public safety. Due to the sensitive and/or safety critical nature of the data collected in these applications, it is imperative to consider the security and privacy aspects of the UAVs used in these scenarios. In this article, we propose an efficient privacy aware authentication scheme for edge-assisted UAVs (Internet of Drones). Unlike the existing security solutions for UAV, our proposed scheme does not require to store any secret key in the devices but still can ensure the desired security level. To the best of our knowledge, this is the first article, where physical security of the UAV has taken into account. The proposed system allows third-party communication and mobile edge computing service provides to authenticate the UAVs without any loss of provacy and outperforms existing methods in terms of computational complexity

Evaluation of stress intensity factor of multiple inclined cracks under biaxial loading

A finite rectangular plate of unit thickness with two inclined cracks (parallel and non parallel) under biaxial mixed mode condition are modelled using finite element method. The finite element method is used for determination of stress intensity factors by ANYSIS software. Effects of crack inclination angle on stress intensity factors for two parallel and non parallel cracks are investigated. The significant effects of different crack inclination parameters on stress intensity factors are seen for lower and upper crack in two inclined crack.The present method is validated by comparing the results from available experimental data obtained by photo elastic method in same condition

Incremental hybrid intrusion detection for 6LoWPAN

IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) has grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various ‘concept-drift detection’ mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme

A Security-enabled Safety Assurance Framework for IoT-based Smart Homes

YesThe exponential growth of the Internet of Things (IoT) has paved the way for safety-critical cyber-physical systems to enter our everyday activities. While such systems have changed the way of our life, they brought new challenges that can adversely affect our life and the environment. Safety and security are two such challenges that can hamper the widespread adoption of new IoT applications. Due to a large number of connected devices and their ability to control critical physical assets, intended attacks on them and/or unintended failure events such as mechanical failure of devices, communication failure and unforeseen bad interactions between connected devices may cause an IoT-based system to enter into unsafe and dangerous physical states. By considering the importance of safety and security of IoT systems, in this article, we present a security-enabled safety monitoring framework for IoT-based systems. In the proposed framework, we utilise design-time system analysis to create an executable monitoring model that enables run-time safety assurance provision for a system via collecting and analysing operational data and evidence to determine the safety status of the system and then taking appropriate actions and securely communicating the safety status and recommended actions to the system users to minimise the risk of the system entering into an unsafe state