Additional Authentication Research Articles

Opportunistic Sensor-Based Authentication Factors in and for the Internet of Things.

Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques.

Enhancing Credit Card Fraud Detection: A Novel Approach with Random Forest and Behavioral Biometrics

Abstract: In an era marked by heightened fraudulent activities targeting credit card transactions, this research delves into the efficacy of advanced machine learning algorithms in combating such threats. The performance of logistic regression, decision trees, and the novel random forest approach in identifying fraudulent transactions is scrutinized. Additionally, behavioral biometrics are incorporated as an innovative authentication factor to enhance fraud detection accuracy. Incorporating behavioral biometrics as an additional authentication factor represents a cutting-edge approach to fraud detection, leveraging the unique behavioral patterns exhibited by individuals during transactional interactions.

HWMP-based secure communication of multi-agent systems

Multi-agent systems (MAS), known for their autonomy, fault tolerance, and flexible collaboration capabilities, are widely used in various fields. Communication security is one of the most critical problems of MAS tasks. Once a malicious attacker invades agents in the system, it brings massive threats to the entire system. It tampers with routing messages, discards packets, and paralyzes the communication network. To implement collaboration tasks of MASs safely and efficiently, this work makes security improvements on the hybrid wireless mesh protocol (HWMP) with an additional authentication system. We enhance the proactive and reactive mode of the HWMP and apply it to an integrated system, i.e., the MAS formation control as a proper instance. We present a HWMP-based secure communication model (HSCM) for MASs. The theoretical analysis and simulation results demonstrate a significant enhancement in network performance under various threats, including packet loss rate, throughput, etc. The results indicate that HSCM plays a significant role in resisting common attacks (such as black hole and flooding attacks) when compared to multi-agent systems without any security protection. There is an approximate 30% increase in packet delivery rate, a substantial enhancement in throughput, and a reduction in routing overhead. The latency also fluctuates within reasonable bounds. Our solution provides secure communication for formation control under malicious attacks.

Optical essential secret image sharing using unequal modulus decomposition and gyrator transform

Essential Secret Image Sharing (ESIS) decomposes a secret image into a set of shares that are distributed among categorized participants, and ensures that only authorized subsets of these participants can restore the image. All ESIS schemes to date have been based merely on computational techniques. In this paper, an optical ESIS system is introduced which uses unequal modulus decomposition (UMD) and optical gyrator transform (GT), offering high-speed parallel processing and dispensing with any pre-processing stages. The presented (1, 2, n) ESIS system generates n shares, including one essential share, such that any two shares that include the essential one, can reconstruct the initial secret image with no distortion. Any other unauthorized subset will not gain any information about the image. The scheme generates essential and nonessential shares that are of equal size, eliminating the need to concatenate sub-shares during the reconstruction of the secret image. The results verify that the secret image was completely retrieved in cases of authorized access, while full distortion occurred in cases of unauthorized access. The GT rotation angle serves as an additional authentication factor to validate the essential share and bolster the security. The optical ESIS system exhibits a high level of sensitivity to the changes in the GT rotation angle - that a variation of just 0.001 radians can cause the correlation coefficient to drop below 0.05.

WPA2 Security Enhancement Method through NFT in Web3 Era

WPA2 is the most used wireless communication protocol in the world (2023). It first appeared in 2006, and now several vulnerabilities have been identified. To use WPA2-EAP or WPA3 (2018), which were released to compensate for the vulnerabilities of WPA2, additional equipment upgrades are required for STA (station) and AP (access point, router), which are connecting devices. We are currently living in the Web3 era. In the future society, people will have more than one NFT each. It is possible to improve the security of WPA2 by using this as an authentication means. In this paper, see the principles of WPA2 crack tools that are currently used today and suggest a way to defend against them using NFT. An experiment was carried out on the security of WPA2, which is widely used in SOHO environments, using only SBC (Single Board Computer) and NFT without expensive routers or additional authentication means. Hacking time, Internet connection delay time, download speed, etc. were compared on various PCs. In conclusion, this proposal demonstrated that representative WPA2 cracking tools can be defended without performance degradation compared to existing WPA2.

Design and Build a Network Security System Using Port Knocking, DMZ and IDS Techniques at SMA Negeri 1 Warungkiara

The network security system of SMA Negeri 1 Warungkiara needs improvement to be more effective in safeguarding data integrity and confidentiality. Currently, it only relies on a filter rule security, which is not robust enough to fend off hacker attacks. Therefore, advanced security techniques such as Port Knocking, DMZ, and IDS are required. Port Knocking serves as additional authentication by accessing ports in a specific sequence to prevent unauthorized access. DMZ is used to isolate the internal network from direct attacks. IDS is used to detect suspicious activities and alert the administrator. The system was tested against various attacks such as port scanning, DDoS, and Ping of Death, and it was proven to reduce server resource usage from 34% to 6%. The test results show that this security system provides strong protection and preserves the integrity and confidentiality of data within the school network.

Design of a smart doorbell for a leader’s office with availability status notification and visitor recognition features

Smart doorbells have become a critical component of smart homes and modern offices. However, a smart doorbell, particularly designed for a leader’s office, has not been introduced. In this study, a smart doorbell is developed for a leader’s office. The system includes an application that allows availability status notification on the doorbell module and voice communication with the visitor from inside the office based on a private Wi-Fi network without an Internet connection to prevent the leader from potential privacy and security issues. It also features a live video capture of the visitor with face recognition by implementing a MobileNet model. In training and testing this model, 1,549 free face images of 125 people were augmented to generate training, validation, and testing datasets of 9,185, 2,500, and 5,000 face images, respectively. An additional authentication testing dataset of 1,068 AI-generated face images was also used to evaluate the system’s False Acceptance Rate (FAR). A high confidence level of 0.945 was selected for the developed MobileNet model to obtain zero FAR and high accuracy, recall, and F-score values of 0.960, 0.960, and 0.978, respectively. Therefore, the proposed doorbell could be used for an office leader, showing potential use for biometric authentication.

A study on cloud data access through browser credential migration in Windows environment

Various user data stored in cloud services for data continuity and efficiency is one of the main collection targets in digital forensic investigation. Some forensic tools collect cloud data based on user account and password, or provide data collection functions based on user credentials stored in the web browser. However, because many web services require additional authentication using user devices to protect user data, access using only the account and password is becoming difficult. In the case of credentials generated by auto-login, it does not work or requires re-authentication when moved to the investigator's device. This is so that other devices cannot utilize the credentials that are kept on the device due to security measures. In this paper, we propose a new method to migrate the credentials stored by the web browser to other devices and effectively utilize them, unlike the forensic method that involves using local credentials. Our analysis revealed that the majority of browsers encrypt and store credentials, so we researched credential decryption methods. We proceeded with the migration; move and encrypt the decrypted credentials to the investigator's device, or move the not encrypted credentials simply. As a result, we conducted credentials migration experiments on a total of 28 browsers, among which we have clarified that migration is possible in all browsers except three that do not store data, such as Tor. We verified that it is possible to log in and collect data on 20 types of web services that are frequently used using migrated credentials. Although the approach we propose is straightforward, it allows for effective and efficient cloud data collection in digital forensic investigation.

BIOMETRIC AUTHENTICATION UTILIZING CONVOLUTIONAL NEURAL NETWORKS

Relevance. Cryptographic algorithms and protocols are important tools in modern cybersecurity. They are used in various applications, from simple software for encrypting computer information to complex information and telecommunications systems that implement various electronic trust services. Developing complete biometric cryptographic systems will allow using personal biometric data as a unique secret parameter instead of needing to remember cryptographic keys or using additional authentication devices. The object of research the process of generating cryptographic keys from biometric images of a person's face with the implementation of fuzzy extractors. The subject of the research is the means and methods of building a neural network using modern technologies. The purpose of this paper to study new methods for generating cryptographic keys from biometric images using convolutional neural networks and histogram of oriented gradients. Research results. The proposed technology allows for the implementation of a new cryptographic mechanism - a technology for generating reliable cryptographic passwords from biometric images for further use as attributes for access to secure systems, as well as a source of keys for existing cryptographic algorithms.

Response of Faba Bean (<em>Vicia faba</em> L.) Grain Yield to Biofertilizer Rates and Inter Row Spacings at Kaffa Zone, South Western Ethiopia

Purpose: Soil nutrient depletion and crop management practices are the significant factors affecting faba beans production over the world. Biofertilizers can improve soil fertility and health. Moreover, it adds essential nutrients and improves water retention in the soil. Thus, its use is unquestionable to maintain soil fertility and enhance crop production. Hence, biofertilizer rates and inter-spacings were used to enhance faba bean production. Research Method: The Field trials were performed to determine the response of faba bean (Vicia faba L.) grain yield to bio-fertilizer rates and row spacings in the main growing season in 2019 at Kaffa zone, Southwestern Ethiopia. Factorial combinations of four rates of bio-fertilizer (0, 0.25, 0.5, and 0.75kg/ha) and three inter-row spacings (30, 40, and 50cm) were carried out in RCBD with three replications. The LSD test was done at α = 0.05 level of probability. Findings: The results of ANOVA presented that the main effect, as well as the interaction effects of biofertilizers and row spacing, have a significant effect on the yield of faba bean. The pods/plants number, hundred seed weight, and HI are mainly affected by biofertilizer rates and row spacings. The combination of 0.75kg/ha of biofertilizer rates and 50cm of space between rows recorded the maximum pods per plant in number, the weight of hundred seeds, grain yield, and harvest index. The highest grain yield (2540±276kg/ ha) was attained with the application of 0.75kg/ha of bio-fertilizer and 50cm of distance between rows, whereas, the lowest (1083.3±117.7kg/ha) was observed from the null treatments and the smaller spacing between rows (30cm). Generally, the significantly maximum yield was obtained because of the maximum rate of bio-fertilizer with the widest inter-row spacing in this experiment. Therefore, inoculation of bio-fertilizer at a dosage of 0.75kg/ha with a row distance of 50cm was found to be optimum for the maximum yield of faba bean in Southwestern Ethiopia and other similar agroecologies. It is further recommended that this result is from only one season at one site and hence this study may be repeated in space and time to reach a concrete recommendation. Research Limitation: Additional authentication and trials across similar agroecology would be essential to make conclusive recommendations since the field trials were directed for sole cropping season and a site. Originality/ Value: This is an organic fertilizer that could be used by farmers and investors in the future, to increase the production of faba bean.

A Lightweight, Secure Big Data-Based Authentication and Key-Agreement Scheme for IoT with Revocability

With the rapid development of Internet of Things (IoT), designing a secure two-factor authentication scheme for IoT is becoming increasingly demanding. Two-factor protocols are deployed to achieve a higher security level than single-factor protocols. Given the resource constraints of IoT devices, other factors such as biometrics are ruled out as additional authentication factors due to their large overhead. Smart cards are also prone to side-channel attacks. Therefore, historical big data have gained interest recently as a novel authentication factor in IoT. In this paper, we show that existing big data-based schemes fail to achieve their claimed security properties such as perfect forward secrecy (PFS), key compromise impersonation (KCI) resilience, and server compromise impersonation (SCI) resilience. Assuming a real strong attacker rather than a weak one, we show that previous schemes not only fail to provide KCI and SCI but also do not provide real two-factor security and revocability and suffer inside attack. Then, we propose our novel scheme which can indeed provide real two-factor security, PFS, KCI, and inside attack resilience and revocability of the client. Furthermore, our performance analysis shows that our scheme has reduced modular exponentiation operation and multiplication for both the client and the server compared to Liu et al.’s scheme which reduces the execution time by one third for security levels of λ = 128 . Moreover, in order to cope with the potential threat of quantum computers, we suggest using lightweight XMSS signature schemes which provide the desired security properties with λ = 128 bit postquantum security. Finally, we prove the security of our proposed scheme formally using both the real-or-random model and the ProVerif analysis tool.

Achieving Decentralized and Dynamic SSO-Identity Access Management System for Multi-Application Outsourced in Cloud

Existing single sign-on (SSO) access control systems typically rely on the traditional protocols requiring additional authentication mechanism and/or identity providers. As the growing demand in outsourcing system resources such as data and applications to the cloud platform, implementing traditional SSO models to support efficient and fine-grained access control for multi-user and multi-application environment is not practical. In this paper, we propose a blockchain-based identification and access management (IAM) scheme called D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM to provide strong security measures for controlling SSO access to resources in the cloud. At a core of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM, all access control processes are done by the smart contracts and blockchain where the access transactions are well retained for the accountability. In our system, the SSO authentication is based on highest authentication level and hashed-based token that significantly reduces the communication overhead for multi-system authentication. For the authorization system, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM enables fine-grained access through the access policy modeled in document database written and enforced to each customer. Finally, we conducted the experiments on Google cloud to show that our D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM system is efficient for the implementation. The performance test showed that our proposed system is approximately 4 times efficient than the average processing time of three existing works.

Differentiation of Lingxiaohua and Yangjinhua by chloroplast genome sequencing and DNA barcoding markers

Lingxiaohua (Campsis Flos, Campsis grandiflora (Thunb.) K. Schum) is a medicinal herb used for promoting diuresis and treating blood-related disorders by the promotion of blood circulation. It also possesses anti-inflammatory and antioxidative properties. This non-poisonous plant is frequently confused with poisonous Yangjinhua (Daturae Metelis Flos, Datura metel Linnaeus) in the market, resulting in serious anticholinergic poisoning. The confusion of these two herbs is due to the similarity in their appearances. In our study, we compared the complete chloroplast genomes of the two plants and found that they are very different in terms of their gene content and gene arrangement. There were also significant differences in the number and repeating motifs of microsatellites and complex repeats. We used universal primers for the amplification of rbcL, matK, psbA-trnH, and ITS2 regions and successfully differentiated the two plants. Furthermore, we designed two pairs of primers based on the nucleotide differences in chloroplast genomes at the rps14 and rpoC1 regions to provide additional authentication markers. The universal primers and specific primers when used together can accurately discriminate Lingxiaohua and Yangjinhua.

LENTO: Unpredictable Latency-based continuous authEntication for Network inTensive IoT envirOnments

Started as a hyped technology a few years ago, IoT is now a reality providing sensing and computing capabilities from SCADA systems to households. At their core, IoT devices connect to the outside world to share sensed or computed data. However, the sensitivity and privacy of shared data has made access management a stringent need also for the IoT. In particular, continuous authentication could solve a few security issues, like session hijacking, via checking device legitimacy for each exchanged message and preventing attackers from pretending their actions came from authenticated devices. To date, device-to-device (D2D) continuous authentication still relies on tokens/certificates or devices’ fingerprints such as battery levels or location. The cited solutions, while being not always implementable on resource constrained devices, provide low-entropy and thus sporting a non negligible probability of being guessable during impersonation attacks.In this paper, we overcome the above limitations with LENTO: unpredictable Latency-based continuous authEntication for Network inTensive IoT envirOnments. In addition to a thorough analysis, we also offer experimental validation of our proposal. We have deployed LENTO as an additional authentication module of the well-known NextCloud platform, and we have performed an extensive experimental campaign. Collected results confirm our working hypothesis. Network delays can be exploited as random seeds in continuous authentication protocols as they provide as much entropy as standard approaches.To the best of our knowledge, our approach is the first continuous authentication protocol relying purely on the network characteristics, regardless of the underneath computing base trustworthiness. Given the minimal overhead introduced by our solution, it provides continuous authentication even for those devices that cannot afford to run (defacto) standard protocols. As such, LENTO could be retrofitted, offering enhanced security to a plethora of nowadays unsecured devices.

A channel state information based virtual MAC spoofing detector

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. A deep convolutional neural network is constructed to analyze signal level information extracted from Channel State Information (CSI) between the communication peers to provide additional authentication protection at the physical layer. A significant merit of the proposed detection system is that this system can distinguish two different devices even at the same location, which was not well addressed by the existing approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located.

Low Power Intrusion Detection Using Multi-Level Sensor Authentication

Surveillance systems be connected with very less cost and less power IR and PIR sensors, which are more capable to find the false alarms and small inference. Wireless smart cameras become an appropriate solution for the upcoming challenges like the hardware capabilities in terms of low ­ power utilization and high imaging performance and high memory footprint all are very big tasks for the wireless smart cameras. For this reason, wireless surveillance systems still involve a significant aggregate of research in different areas such as mote architectures, video processing algorithms, power management, energy harvesting and distributed engine. Here we evolve a solution for this problem through multilevel sensing architecture. The proposed system to be find the energy interference in the first level. If any unwanted event, it will be occurring at the moment the additional authentication unit become initiate and activities. In this stage, PIR sensor that detects the traces of the event. If the PIR sensor detects the same, it authenticates the event and switches ON the wireless camera. This system has multiple advantages like reduced power consumption, improved event detection accuracy, longer life span and enhanced information clarity.

OCR-based vehicle number plate recognition powered by a raspberry Pi

Modern technology has revolutionized automation. Security is at high priority with increasing automation. Today, to help people feel comfortable, video surveillance cameras are installed in public places like schools, hospitals, and other buildings. The main goal of this research work is to automatically collect vehicle images with a camera using a Raspberry Pi and recognising the licence plate of the vehicles. Vehicle number plate recognition is a challenging but crucial system. This is highly helpful for automating toll booths, identifying automated signal violators, and identifying traffic regulation violators. In this work, a Raspberry Pi is used for vehicle license plate recognition, which uses image processing to automatically recognize license plates. Incoming camera footage is continuously processed by the system to look for any signs of number plates. When the camera detects a number plate, Optical Character Recognition (OCR) technique is used to process the image and extract the number from it. The distance to an object is calculated by a sensor utilizing sound waves. The extracted number is then displayed by the system. This can be used for additional authentication.

User Authentication Protocol Based on the Location Factor for a Mobile Environment

The way the internet is used by billions of users around the world has been revolutionized by mobile devices. The capabilities of smartphones are constantly growing, and the number of services available for mobile devices is also increasing. This undeniable trend makes smartphones terminals for accessing services that process confidential data, which make smartphones priceless targets of cyberattacks. Along with an increasing number of mobile services, the methods of securing the confidentiality, integrity and availability of systems used have also evolved and adapted to the capabilities of a mobile environment. One of the important security services is the user authentication process. This process often implements the postulates of strong authentication, multistage authentication based on factors from the knowledge, position and inherence categories. Unfortunately, the implementation of the factors belonging to these categories is not always possible due to the limitations of smartphones, such as the lack of interfaces for the implementation of biometrics or environmental factors - problems with network or internet access in various countries and regions. Therefore, there is a need to analyse the possibility of implementing a strong authentication process based on additional information about users, e.g., based on location data. The article analyses the requirements for the authentication process and authentication factors. Based on the performed analysis, the criteria that each authentication factor must meet were defined. This article presents a proposal for a user authentication protocol based on the location factor for a mobile environment. The method can be used in the case of problems with the implementation of strong authentication or as an additional authentication factor that increases the security of the user identity confirmation process. The presented protocol has been analysed in terms of performance, security and compliance with the requirements related to the authentication factors.

Improving the Authentication with Built-In Camera Protocol Using Built-In Motion Sensors: A Deep Learning Solution

In this paper, we propose an enhanced version of the Authentication with Built-in Camera (ABC) protocol by employing a deep learning solution based on built-in motion sensors. The standard ABC protocol identifies mobile devices based on the photo-response non-uniformity (PRNU) of the camera sensor, while also considering QR-code-based meta-information. During registration, users are required to capture photos using their smartphone camera. The photos are sent to a server that computes the camera fingerprint, storing it as an authentication trait. During authentication, the user is required to take two photos that contain two QR codes presented on a screen. The presented QR code images also contain a unique probe signal, similar to a camera fingerprint, generated by the protocol. During verification, the server computes the fingerprint of the received photos and authenticates the user if (i) the probe signal is present, (ii) the metadata embedded in the QR codes is correct and (iii) the camera fingerprint is identified correctly. However, the protocol is vulnerable to forgery attacks when the attacker can compute the camera fingerprint from external photos, as shown in our preliminary work. Hence, attackers can easily remove their PRNU from the authentication photos without completely altering the probe signal, resulting in attacks that bypass the defense systems of the ABC protocol. In this context, we propose an enhancement to the ABC protocol, using motion sensor data as an additional and passive authentication layer. Smartphones can be identified through their motion sensor data, which, unlike photos, is never posted by users on social media platforms, thus being more secure than using photographs alone. To this end, we transform motion signals into embedding vectors produced by deep neural networks, applying Support Vector Machines for the smartphone identification task. Our change to the ABC protocol results in a multi-modal protocol that lowers the false acceptance rate for the attack proposed in our previous work to a percentage as low as 0.07%. In this paper, we present the attack that makes ABC vulnerable, as well as our multi-modal ABC protocol along with relevant experiments and results.

Wearing an organic “coat” on nanocrystals of LaF3:Eu3+ to generate dynamic luminescence for optical anti-counterfeit

Nowadays, the continuous emergence of various fake and inferior products has caused a huge impact on high quality products. For protecting the interests of consumers and enterprises from the fake, the development of anti-counterfeiting technology is particularly significant. Here, temperature-dependent luminescence was used here as additional authentication information to improve the anti-counterfeiting level. In this work, a new luminescent material for optical anti-counterfeit was developed by wearing organic Tb (III) complex on nanocrystals of LaF3:Eu3+. The prepared core–shell materials exhibited a dynamic luminescence responding to temperature, mainly due to the energy transfer of hfa → Tb3+ and hfa → Tb3+→Eu3+, and the energy back transfer from the emitting level of the Tb3+ to the excited triplet state of the hfa ligand. The dynamic multicolor emissions show higher security level in anti-counterfeiting. We believe that our research results will provide some guidance for the development of temperature-dependent luminescent materials in anti-counterfeiting applications.