Search Results (14)

Continuous authentication allows devices to keep checking that the active user is still the rightful owner instead of relying on a single login. However, current methods can be tricked by forging faces, revealing personal data, or draining the battery. Additionally, the environment where the user plays a vital role in determining the user’s online security. Thanks to several security attacks, such as impersonation and replay, the user or the device can easily be compromised. We present a lightweight system that pairs face recognition with complex environmental sensing, i.e., the phone validates the user when the surrounding light or noise changes. A convolutional network turns each captured face into a 128-bit code, which is combined with a random “nonce” and protected by hashing. A camera–microphone module monitors light and sound to decide when to sample again, reducing unnecessary checks. We verified the protocol with formal security tools (Scyther v1.1.3.) and confirmed resistance to replay, interception, deepfake, and impersonation attacks. Across 2700 authentication cycles on a Snapdragon 778G testbed, the median decision time decreased from 61.2 ± 3.4 ms to 42.3 ± 2.1 ms (p < 0.01, paired t-test). Data usage per authentication cycle fell by an average of 24.7% ± 1.8%, and mean energy consumption per cycle decreased from 21.3 mJ to 19.8 mJ (∆ = 6.6 mJ, 95% CI: 5.9–7.2). These differences were consistent across varying lighting (≤50, 50–300, >300 lux) and noise conditions (30–55 dB SPL). These results show that smart-sensor-triggered face recognition can offer secure and energy-efficient continuous verification, supporting smart imaging and deep-learning-based face recognition. Full article

Prestressed concrete railroad ties are the global standard for railway infrastructure due to their structural stability, durability, and cost-effective maintenance. However, their long-term performance is often compromised by premature deterioration. This study investigates the degradation of prestressed concrete railways ties from a Brazilian rail line after ten years of natural exposure, emphasizing critical implications for infrastructure maintenance. Two groups of ties, separated by 30 km, were analyzed through physical property assessments, petrography, X-ray diffraction (XRD), and scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS). The results reveal that deterioration was driven by the combined effects of alkali–silica reaction (ASR) and sulfate attack, confirmed by the presence of (N, C)ASH gels, ettringite crystallization, and cryptocrystalline materials within cracks and voids. Prestressing-induced stresses and environmental moisture further accelerated degradation, leading to a 66% reduction in mechanical strength in the T1 group. These findings demonstrate that internal swelling reactions and moisture exposure synergistically accelerate deterioration in prestressed concrete ties, particularly in low-prestress, poorly drained zones. Full article

Owing to limited resources, implementing conventional security components in wireless sensor networks (WSNs) rather than wireless networks is difficult. Because most sensor nodes are typically powered by batteries, the battery power should be sufficiently long to prevent the shortening of the network lifetime. Therefore, many studies have focused on detecting and avoiding energy drainage attacks in WSNs. However, a survey paper has yet to be published for energy drain attacks in WSNs since 2019. Therefore, we present a novel comprehensive survey paper for energy drainage attacks in WSNs. First, we address an overview of WSNs and their security issues. Next, we explain the methodology for this study and explain the existing approaches for energy drainage attacks in layered architectures. Based on the results of this analysis, open issues and further research directions are presented. Full article

Wireless local area networks (WLANs) have recently evolved into technologies featuring extremely high throughput and ultra-high reliability. As WLANs are predominantly utilized in Internet of Things (IoT) and Wi-Fi-enabled sensor applications powered by coin cell batteries, these high-efficiency, high-performance technologies often cause significant battery depletion. The introduction of the trigger frame-based uplink transmission method, designed to enhance network throughput, lacks adequate security measures, enabling attackers to manipulate trigger frames. Devices receiving such frames must respond immediately; however, if a device receives a fake trigger frame, it fails to enter sleep mode, continuously sending response signals and thereby increasing power consumption. This problem is specifically acute in next-generation devices that support multi-link operation (MLO), capable of simultaneous transmission and reception across multiple links, rendering them more susceptible to battery draining attacks than conventional single-link devices. To address this, this paper introduces a Secure Triggering Frame-Based Dynamic Power Saving Mechanism (STF-DPSM) specifically designed for multi-link environments. Experimental results indicate that even in a multi-link environment with only two links, the STF-DPSM improves energy efficiency by an average of approximately 55.69% over conventional methods and reduces delay times by an average of approximately 44.7% compared with methods that consistently utilize encryption/decryption and integrity checks. Full article

The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device’s battery should be replaced in order to ensure that the battery is not completely drained before it is replaced. Full article

Solar power (SP) passive optical network (PON)-based fiber-wireless (FiWi) access systems are becoming increasingly popular as they provide coverage to rural and urban areas where no power grid exists. Secure operation of such networks which includes solar- and/or battery-powered devices, is crucial for anticipating potential network issues and prolong the life of the network operation. Since optical network units (ONUs) may be powered by SP-charged batteries, energy awareness becomes an important issue, particularly when it comes to reducing ONUs’ energy consumption and allowing them to operate in off-grid remote areas. With the PON as the fixed part of these networks, the optical line terminal (OLT) informs the ONUs through a message exchange mechanism when no traffic is present, allowing them to transition to a low-power-consumption sleep mode. However, man-in-the-middle (MITM) attacks pose a serious threat to the message exchange mechanisms, which can eventually drain the energy of battery-powered ONUs resulting in their shutdown. Consequently, this paper introduces two novel mechanisms for reducing ONU energy consumption, namely the wake-up and time-out mechanisms, which can be used to mitigate the effectiveness of MITM attacks that may seek to affect the unit’s operation due to battery drain. The formal verification results show that these goals were effectively achieved. Full article

Electric vehicles (EVs) are becoming popular due to their efficiency, eco-friendliness, and the increasing cost of fossil fuel. EVs support a variety of apps because they house powerful processors and allow for increased connectivity. This makes them an attractive target of stealthy cryptomining malware. Recent incidents demonstrate that both the EV and its communication model are vulnerable to cryptojacking attacks. The goal of this research is to explore the extent to which cryptojacking impacts EVs in terms of recharging and cost. We assert that while cryptojacking provides a financial advantage to attackers, it can severely degrade efficiency and cause battery loss. In this paper we present a simulation model for connected EVs, the cryptomining software, and the road infrastructure. A novel framework is proposed that incorporates these models and allows an objective quantification of the extent of this economic damage and the advantage to the attacker. Our results indicate that batteries of infected cars drain more quickly than those of normal cars, forcing them to return more frequently to the charging station for a recharge. When just 10% of EVs are infected we observed 70.6% more refueling requests. Moreover, if the hacker infects a charging station then he can make a USD 436.4 profit per day from just 32 infected EVs. Overall, our results demonstrate that cryptojackers injected into EVs indirectly provide a financial advantage to the charging stations at the cost of an increased energy strain on society. Full article

Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network. Full article

Wireless Sensor Networks (WSNs) are networks of small devices with limited resources which are able to collect different information for a variety of purposes. Energy and security play a key role in these networks and MAC aspects are fundamental in their management. The classical security approaches are not suitable in WSNs given the limited resources of the nodes, which subsequently require lightweight cryptography mechanisms in order to achieve high security levels. In this paper, a security analysis is provided comparing BMAC and LMAC protocols, in order to determine, using AES, RSA, and elliptic curve techniques, the protocol with the best trade-off in terms of received packets and energy consumption. Full article

This paper proposes a routing protocol for wireless sensor networks to deal with energy-depleting vampire attacks. This resource-conserving protection against energy-draining (RCPED) protocol is compatible with existing routing protocols to detect abnormal signs of vampire attacks and identify potential attackers. It responds to attacks by selecting routes with the maximum priority, where priority is an indicator of energy efficiency and estimation of security level calculated utilizing an analytic hierarchy process (AHP). RCPED has no dependence on cryptography, which consumes less energy and hardware resources than previous approaches. Simulation results show the benefits of RCPED in terms of energy efficiency and security awareness. Full article

In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency. Full article

Wi-Fi technology connects sensor-based things that operate with small batteries, and allows them to access the Internet from anywhere at any time and perform networking. It has become a critical element in many areas of daily life and industry, including smart homes, smart factories, smart grids, and smart cities. The Wi-Fi-based Internet of things is gradually expanding its range of uses from new industries to areas that are intimately connected to people’s lives, safety, and property. Wi-Fi technology has undergone a 20-year standardization process and continues to evolve to improve transmission speeds and service quality. Simultaneously, it has also been strengthening power-saving technology and security technology to improve energy efficiency and security while maintaining backward compatibility with past standards. This study analyzed the security vulnerabilities of the Wi-Fi power-saving mechanism used in smart devices and experimentally proved the feasibility of a battery draining attack (BDA) on commercial smartphones. The results of the experiment showed that when a battery draining attack was performed on power-saving Wi-Fi, 14 times the amount of energy was consumed compared with when a battery draining attack was not performed. This study analyzed the security vulnerabilities of the power-saving mechanism and discusses countermeasures. Full article

This work shows the results of an experimental investigation on a domestic hydropower model assembled at the Laboratory of Environmental and Maritime Hydraulics of the Department of Civil Engineering, University of Salerno, Italy. Hydropower offers the opportunity to create a clean renewable source of energy, reducing carbon footprint and having a minimal impact on the environment. Small-scale hydropower plants in the domestic context are suitable for buildings with heights and roof surfaces that, in conjunction with a proper storage system, may yield capacities of up 100 kW. The device here adopted, mimicking the vertical part of a drain serving a flat complex, is composed of a storage tank of 60 l connected to a pressurized system fitted in the final downstream section with a nozzle. The available kinetic energy is converted in electricity thanks to a microturbine which drives a generator. The system is analyzed by: using different nozzles obtained by a 3D printer, varying the flowrate and attack angle at the microturbine and changing the number of blades. Full article

Wireless sensor networks are supplied with limited energy resources and are usually installed in unattended and unfriendly environments. These networks are also highly exposed to security attacks aimed at draining the energy of the network to render it unresponsive. Adversaries launch counterfeit report injection attacks and false vote injection attacks through compromised sensor nodes. Several filtering solutions have been suggested for detecting and filtering false reports during the multi-hop forwarding process. However, almost all such schemes presuppose a conventional underlying protocol for data routing that do not consider the attack status or energy dissipation on the route. Each design provides approximately the equivalent resilience in terms of protection against compromised node. However, the energy consumption characteristics of each design differ. We propose a fuzzy adaptive path selection to save energy and avoid the emergence of favored paths. Fresh authentication keys are generated periodically, and these are shared with the filtering nodes to restrict compromised intermediate filtering nodes from the verification process. The scheme helps delay the emergence of hotspot problems near the base station and exhibits improved energy conserving behavior in wireless sensor networks. The proposed scheme provides an extended network lifetime and better false data filtering capacity. Full article