Secure Method Research Articles

A Method for Assessing the Prevalence of Food Insecurity in Older Adults Based on Resource Constraints and Food-Related Physical Functioning Limitations

BackgroundOlder adults with food-related physical functioning limitations often face food insecurity because of challenges that go beyond resource constraints. Difficulties with food acquisition at retailers, and food preparation and consumption are not captured by the United States Department of Agriculture’s food security measure. ObjectivesThe objective of this study was to develop a method to assess the overall prevalence of food insecurity regardless of underlying cause using validated measures that capture both food-related physical functioning limitations and resource-constraint food hardships. It also aims to examine the validity of this method in relation to sociodemographic characteristics, health, appetite, and dietary outcomes. MethodsUsing information from National Health and Nutrition Examination Surveys (2007–2018), 10,237 adults aged 60 y and older with complete food security and food-related physical functioning limitations data were included in the study. Comparisons of the cross-classification method and the standard food security methods are made and their relationships with depression, self-reported health, and healthy eating index 2015 are examined using multiple linear regression models. ResultsUsing the cross-classification method, prevalence of low and very low food security because of resource constraints and food-related physical functioning limitations was found to be higher (13.6% and 7.3%, respectively) compared with food insecurity based solely on resource constraints (4.7% and 3.3%, respectively) or food-related physical functioning limitations (11.4% and 4.4%). Low and very low food security levels using the cross-classification method were associated with higher odds of depression and poor self-reported health, compared with the standard United States Department of Agriculture (USDA) resource-constrained food security measure. ConclusionsThe cross-classification method enables the identification of potentially food insecure older adults who might be classified otherwise if only each measure is used alone. This method serves as guidance for using both measures jointly to estimate food insecurity, regardless of its cause.

Simple and Safe Soft-Tissue Coverage Technique Using the Bipedicled Flap in the Lower Leg.

Skin and soft-tissue defects in the lower legs present significant challenges for surgeons, especially when tendons, bones, or implants are exposed. In such cases, simple dressings or skin grafts are insufficient, necessitating flap surgery. The bipedicled flap is a simpler and more reliable method with a high success rate compared to other flaps, showing minimal flap failures. This study aimed to examine the advantages and limitations of using a bipedicled flap for soft-tissue reconstruction in the lower legs. This retrospective study reviewed medical records from January 2013 to May 2020, involving 10 bipedicled flaps performed on the lower legs. The study included 5 male and 5 female patients, with an average age of 54.5 years. The defects were due to various causes, including trauma, tumor surgery, postoperative complications such as dehiscence and skin necrosis, and chronic osteomyelitis. The size of the defects ranged from 4 × 3 cm to 16 × 13 cm. The outcomes assessed included flap viability, postoperative complications, and patient satisfaction. Patient satisfaction was assessed on a 15-point scale, evaluating appearance, function, and sensation with up to 5 points each, and categorizing scores as poor (1-3), below average (4-6), average (7-9), good (10-12), and excellent (13-15). All flaps were successfully performed, and there were no cases showing any special complications. Patient satisfaction following the operation was excellent in 3 patients, good in 6 patients, and average in 1 patient, with an overall average score of 11.5 (good) out of 15 among the 10 patients evaluated. The bipedicled flap is a simple and secure method for treating soft-tissue defects in the lower legs. Therefore, this technique can be considered as one of the viable options for treating such defects.

Analysis of authentication methods for full-stack applications and implementation of a web application with an integrated authentication system

The subject of research is methods and techniques for secure user authentication in web applications. The goal of the work is to analyse authentication methods and implement a web application with an authentication system integrating JWT tokens and the OAuth v2.0 standard. The article solves the tasks of analysis of the main protocols and methods of user authentication in web applications, implementation of authentication based on the OAuth 2.0 standard and JWT access/refresh token, and analysis of the risks of vulnerabilities and attacks for the implemented web applications. Methods used: comparison, empirical analysis, calculation methods. The next results have been obtained: analysed the protocols and methods of user authentication in web applications; selected authentication methods of JWT token and OAuth v2.0 standard for building modern web applications; created a web application based on the selected authentication methods in web applications; analysed the risks of vulnerabilities and attacks in web applications. Conclusions: The most well-known authentication methods for web applications are analyzed. It is established that most modern authentication methods have many disadvantages, which leads to increased risks when using these authentication methods. It is shown that one of the most reliable methods of web application user data security is the use of a combination of JWT Access/Refresh token and browser fingerprints. The implementation, configuration, and analysis of this methodology have shown that this combination provides the most reliable prevention of token theft and use from another computer. OAuth 2.0 authentication was also implemented. The study found that delegating authentication to services such as Facebook or Google can provide a low risk of attacks and vulnerabilities for a web application. It is noted that authentication using OAuth 2.0 can be compromised only at the beginning of the connection between the client and the server, or rather when the client first sends initial information from the browser fingerprints. This information is sent over the secure HTTPS (Hypertext Transfer Protocol Secure) protocol, so the risk of compromising OAuth 2.0 authentication is low.

Multi‐Level Wettability Patterned Porous Matrix for Advanced Optical Information Encryption

AbstractIn the rapidly evolving landscape of information security, the demand for optical encryption techniques based on wettability patterning, which allows decryption without sophisticated devices and complex procedures, is progressively increasing. However, conventional approaches based on binary wettability patterned surfaces impose significant constraints in their practical use for high‐level information security. Herein, a novel approach to optically encrypt information through multi‐level wettability patterning is introduced on a porous polymer matrix synthesized from vinyl methacrylate (VMA) and poly(ethylene glycol) diacrylate (PEGDA). By employing the thiol‐ene click reaction, precise patterning of functional groups is achieved that exhibit distinct wettability responses to aqueous solutions with varying surface tensions, as well as pH‐responsive and fluorescent markers. This multi‐level wettability system surpasses traditional binary hydrophilic/hydrophobic patterns by providing enhanced control over wettability states, resulting in higher‐level encryption of information. The integration of pH and fluorescence responsivity further elevates the complexity and security of the encrypted information, making it decipherable only under specific and controlled conditions. The innovative approach offers a highly secure, robust, and customizable encryption method, redefining the potential of optical encryption technologies.

Analisis Efektivitas Keamanan Jaringan Layer 2: Port Security, VLAN Hopping, DHCP Snooping

In the industrial era 4.0, which is marked by the rapid progress of computer networks and the internet, network security at Layer 2 is part of the Data Link Layer in the OSI model, which is very important considering the increasing number of cyber threats. This research aims to compare the effectiveness of various security algorithms applied to Layer 2 switches, namely MAC Address Filtering with Port Security, VLAN Hopping Mitigation, and DHCP Snooping. Some of these methods have the function or purpose of creating MAC address filtering, port limitations, VLAN protection, ARP, etc. The device used to conduct this research is a Cisco Switch. This research uses an experimental approach by implementing each method in different network scenarios and comparing it with external data, measuring effectiveness based on attack detection and prevention, as well as its impact on network performance, then combining several of these methods into one scope. The results show that each method has its own advantages and disadvantages, which function to block various types of attacks such as Flooding, Snooping, and Rogue DHCP. It is highly recommended to combine all security methods into one integrated system.

Analysis of Image Encryption using Triple DES

Abstract: Keeping your essential data secure in today’s world is very essential. There are different types of cyber-attacks which attackers can use to exploit your personal or confidential data for their own needs. Cryptography and its algorithms in cybersecurity has been an important part in providing secure methods to hide or encrypt information or sensitive data which only the person who was intended to be send can access it. This paper presents the triple Data Encryption Standard (DES) approach for secure image encryption from malicious users. This paper provides an efficient, easy and secure approach for encryption of images which is essential in today’s scenario.

Deep learning techniques for hand vein biometrics: A comprehensive review

Biometric authentication has garnered significant attention as a secure and efficient method of identity verification. Among the various modalities, hand vein biometrics, including finger vein, palm vein, and dorsal hand vein recognition, offer unique advantages due to their high accuracy, low susceptibility to forgery, and non-intrusiveness. The vein patterns within the hand are highly complex and distinct for each individual, making them an ideal biometric identifier. Additionally, hand vein recognition is contactless, enhancing user convenience and hygiene compared to other modalities such as fingerprint or iris recognition. Furthermore, the veins are internally located, rendering them less susceptible to damage or alteration, thus enhancing the security and reliability of the biometric system. The combination of these factors makes hand vein biometrics a highly effective and secure method for identity verification.This review paper delves into the latest advancements in deep learning techniques applied to finger vein, palm vein, and dorsal hand vein recognition. It encompasses all essential fundamentals of hand vein biometrics, summarizes publicly available datasets, and discusses state-of-the-art metrics used for evaluating the three modes. Moreover, it provides a comprehensive overview of suggested approaches for finger, palm, dorsal, and multimodal vein techniques, offering insights into the best performance achieved, data augmentation techniques, and effective transfer learning methods, along with associated pretrained deep learning models. Additionally, the review addresses research challenges faced and outlines future directions and perspectives, encouraging researchers to enhance existing methods and propose innovative techniques.

Integration of NLP and machine learning methods for smart contract security: a comparison with traditional approaches

In modern blockchain systems, smart contracts are one of the most critical components for ensuring the automated execution of agreements without the need for intermediaries. However, smart contracts written in languages like Solidity may contain vulnerabilities that can be exploited by malicious actors to steal funds or manipulate assets. Given the increasing number of attacks on smart contracts, the development of effective methods for detecting such vulnerabilities is crucial. Traditional approaches to detecting vulnerabilities in smart contracts include symbolic execution, fuzzing, formal verification, and pattern matching. These methods have their advantages but face several challenges, such as high resource consumption, limitations in detecting new types of vulnerabilities, and difficulties in scaling to large contracts. As a result, there is a need to introduce new approaches, such as natural language processing (NLP) and machine learning, which can address these challenges more effectively. In this study, an NLP-based method was explored, using Word2Vec to convert smart contract code into vector representations, allowing for better analysis of the semantic relationships between elements of the code. These vector representations are then fed into a bidirectional recurrent neural network with GRU blocks and an attention mechanism. This approach allows the model to focus on the most important parts of the code and improve the accuracy of vulnerability detection. The comparative analysis showed that NLP-based methods significantly outperform traditional approaches in all key metrics. In particular, the GRU model with an attention mechanism demonstrated high results in accuracy, recall, and F-measure, making it effective for detecting complex vulnerabilities such as reentrancy. Furthermore, the NLP-based approach is capable of adapting to new types of attacks thanks to training on large datasets. Thus, the integration of NLP and machine learning represents a promising direction for enhancing the security of smart contracts. Future research can focus on improving these approaches, particularly through the implementation of advanced models such as transformers.

Retrospective analysis of 1028 ultrasound-guided central vascular access in neonates and children.

The use of real-time ultrasound has become the standard of care for percutaneous central venous access and shown to decrease overall number of attempts and complication rates. A retrospective analysis of a prospective database was done focusing on three types of central access: non-tunneled, tunneled, and implantable, placed via ultrasound-guided Brachiocephalic Vein (BCV) between January 2019 and January 2023. Data were recorded: gender, age, weight, side (left or right), number of puncture attempts, arterial puncture, change of operator or puncture side, and mechanical complications (pneumothorax and hemothorax). A total of 1028 non-tunneled, tunneled, and implantable central lines were placed. Five hundred and eighty seven were Male. The children were aged from 0 to 18 years and their weights ranged from 1 to 113 kg. Nine hundred and thirty-five were left BCV. Right BCV was cannulated in 93 patients. Failure to cannulate left BCV was recorded in seven cases. Three arterial punctures were recorded. Cannulation success rate was 97.2% (999/1028) and was higher in left BCV than right BCV (p < 0.001). Based on the above, we believe that ultrasound-guided BCV is an easy and secure method to cannulate children, in our series left BCV showed a higher cannulation success rate rather than right BCV.

Cybersecurity Frameworks for Cloud-Hosted Financial Applications

The use of cloud hosting is growing in popularity among managers. The hazards and threats are increased when the data is stored in the cloud. To optimise security and effectively manage risks, a robust security model is necessary. Cyber threats are efforts to get private information without authorisation, alter, or remove it, demand money from victims, or interfere with corporate operations. Identity theft, virus threats, online and email fraud, and bank fraud are all considered forms of cybercrime. This technique is used by both people and businesses to protect their digital systems, including data canters. Among the issues with traditional methods of network security are their incapacity to detect sophisticated and insider attacks, slow reaction times, and lack of scalability. These shortcomings show how research is needed to develop more comprehensive and effective security techniques to protect against the growing range of network threats. putting strong security measures in place to safeguard private information and guarantee business continuity. The design and optimisation of a thorough cybersecurity framework made especially for network applications are examined in this paper. The framework emphasises using best practices to safeguard applications, integrating cutting-edge security technology, and identifying and reducing security threats. This paper presents an artificial intelligence-based cyber security method for financial sector management (CS-FSM). Artificial intelligence is one of the strongest tools for mapping and preventing unforeseen hazards from consuming an organisation. The suggested method may be used to categorise and resolve cyberattack issues. Algorithms like the Enhanced Encryption Standard (EES) encrypt and decode data to guarantee the security of financial sector information. The K-Nearest Neighbour (KNN) algorithm generates predictions by learning from the training data. It is used in the banking industry to identify and thwart malware assaults. By strengthening cyber security systems' defences against cyberattacks, the suggested approach improves their performance. CS-FSM improves the ratios of attack avoidance (11.2%), risk reduction (13.2%), security of information (16.2%), scalability (17.2%), and data privacy (18.3%).

The Tricellufuse Technique for Closing Nasal Septal Perforations.

Objective: Nasal septal perforation (NSP) is a structural anomaly that affects the mucosa, cartilage, and bone of the nasal septum, often stemming from septoplasty as the primary cause. Spontaneous healing of a perforated septum is rare, and typically the healing process is observed until it is determined that the condition is stable and no longer deteriorating, at which point surgical intervention is considered. Numerous surgical techniques have been proposed for NSP repair. This study introduces a novel approach incorporating a fascia lata graft with fat tissue and diced septal cartilage to reconstruct medium-sized NSP. Methods: The researchers conducted a retrospective analysis involving 22 patients who underwent repair for NSP from January 2021 to January 2023. Grafts were obtained, and the perforation was addressed using an open rhinoplasty technique, followed by the insertion and suturing of the graft. Results: The mean size of the septal perforations was 12 mm horizontally and 13 mm vertically. All patients with NSPs were chosen as medium size (within 0.5-2 cm). The mean follow-up period was 12 months. Complete closure of NSP was achieved in 19 out of 22 patients (86.3%). Among the cases, 15 were male (68%), and the ages ranged from 25 to 45 years with a mean of 35. Eight cases (36%) were smokers. After 12 months postoperatively, closure was not achieved in 3 medium-sized NSPs due to smoking. . The analysis revealed that the failure rate in the smoker group was statistically significant (P = .036). Conclusion: The TriCelluFuse technique, combining fascia lata, fat tissue, and diced septal cartilage, is a secure and reliable method for closing NSPs. It has a significant success rate and should be considered a viable option for individuals with NSPs.

A practical scheme for enhancing consistency and independence in wireless key generation for Wi-Fi networks

Wi-Fi networks benefit from physical layer wireless key generation, a theoretically secure method for improving information transmission security. This paper introduces the Consistent Independent Key Generation (CIKG) scheme based on the Wi-Fi signal transmission model. It addresses the dual challenges of channel noise and virtual carrier low-pass effects. The scheme begins with extracting the channel frequency response (CFR) from the entire signal preamble, including both long and short symbols, followed by applying cubic spline interpolation to the short symbol-derived CFR to achieve fine-grained frequency fading corresponding to the long symbols. This refined CFR is averaged to bolster noise resistance, improving key consistency. Subsequently, the inverse Fourier transform is used to obtain the channel impulse response (CIR), and a deconvolution strategy reduces the impact of virtual carrier low-pass filtering on the multipath information of CIR, thus improving key independence. Implemented within Wi-Fi networks, the effectiveness of the CIKG scheme is rigorously tested across diverse scenarios. Quantitative evaluations indicate that the scheme substantially improves key consistency, achieving a 5 dB enhancement in signal-to-noise ratio over traditional CFR-based schemes and elevates information entropy by 20%, significantly boosting key independence. These advances affirm the potential of the CIKG scheme as a formidable solution for developing robust and secure wireless communication networks.

CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems

The increasing integration of telehealth systems underscores the importance of robust and secure methods for patient data management. Traditional authentication methods, such as passwords and PINs, are prone to breaches, underscoring the need for more secure alternatives. Therefore, there is a need for alternative approaches that provide enhanced security and user convenience. Biometric-based authentication systems uses individuals unique physical or behavioral characteristics for identification, have emerged as a promising solution. Specifically, Electrocardiogram (ECG) signals have gained attention among various biometric modalities due to their uniqueness, stability, and non-invasiveness. This paper presents CardioGaurd, a deep learning-based authentication system that leverages ECG signals—unique, stable, and non-invasive biometric markers. The proposed system uses a hybrid Convolution and Long short-term memory based model to obtain rich characteristics from the ECG signal and classify it as authentic or fake. CardioGaurd not only ensures secure access but also serves as a predictive tool by analyzing ECG patterns that could indicate early signs of cardiovascular abnormalities. This dual functionality enhances patient security and contributes to AI-driven disease prevention and early detection. Our results demonstrate that CardioGaurd offers superior performance in both security and potential predictive health insights compared to traditional models, thus supporting a shift towards more proactive and personalized telehealth solutions.

Practical two-party SM2 signing using multiplicative-to-additive functionality

Threshold signatures are important tools for addressing issues related to key management, certificate management, and cryptocurrencies. Among them, two-party SM2 signatures have received considerable interest recently. In this paper, we propose a fast and secure online/offline two-party SM2. By employing the re-sharing technique, we have successfully made the online phase of the signing process non-interactive while achieving nearly optimal computational efficiency. Additionally, in the offline phase, there is just a single call to the multiplicative-to-additive functionality based on Paillier encryption. Our protocol is existentially unforgeable under adaptive chosen message attacks in the random oracle model in the presence of a static adversary. Experimental results demonstrate that our proposed scheme outperforms previous similar schemes by approximately a factor of 2 in online computation and a factor of 3 in online communication. Our scheme can be applied in scenarios such as Certificate Authority (CA) and the signing of blockchain transactions to provide them with a more secure and flexible implementation method, enhancing the security and reliability of the systems.

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction

ABSTRACT Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are several approaches that are utilized in practice to calculate the secure key rate. In this manuscript, QKD and error rate optimization based on optimized multi-head self-attention and gated-dilated convolutional neural network (QKD-ERO-MSGCNN) is proposed. Initially, the input signals are gathered from 6G wireless networks which face obstacles to channel. For extending maximum transmission distances and improving secret key rates, the signals are fed to the variable velocity strategy particle swarm optimization algorithm, then the signals are fed to MSGCNN for analysing the quantum bit error rate reduction. The MSGCNN is optimized by intensified sand cat swarm optimization. The performance of the QKD-ERO-MSGCNN approach attains 15.57%, 23.89%, and 31.75% higher accuracy when analysed with existing techniques, like device-independent QKD utilizing random quantum states, practical continuous-variable QKD and feasible optimization parameters, entanglement and teleportation in QKD for secure wireless systems, and QKD for large scale networks methods, respectively.

A Survey: Digital Image Watermarking-Robustness and Imperceptibility

With the Internet expanding, multimedia security has become a significant problem. As digital image manipulation is getting simpler, the certification of authenticity has become more difficult. Digital watermarking outperforms conventional security methods for protecting photos from attacks. The need for reliable image authentication methods in the contemporary digital era has always been a hot topic among academics and researchers. When transmitting sensitive information today through a regular correspondence route, many hacks are possible. Everyone, therefore, requires their digital data to be non-tampered. Many solutions are available like stenography, cryptography, and watermarking. Watermarking gives promising results when it comes to the authenticity of the images. The robustness and imperceptibility of several existing watermarking techniques are demonstrated in this study and critical analysis is performed. Additionally, the author provides a thorough introduction to key ideas like the transform domain and the spatial domain. This study also offers a thorough analysis of contemporary methods for watermarking digital images while showcasing established practices in various domains. Hence, this research study could potentially serve as a viable resource for researchers seeking to develop effective watermarking techniques for practical applications.

CryptoGenSec: A Hybrid Generative AI Algorithm for Dynamic Cryptographic Cyber Defence

As the world of cybersecurity constantly changes, traditional cryptographic techniques have faced limitations in the context of today's sophisticated and dynamic threats. Existing protections usually adopt static algorithms and key structures, making it difficult for them to resist the categories of modern attacks. This research paper, therefore, presents CryptoGenSec, a brand-new generative AI algorithm based on a hybrid consisting of generative adversarial networks (GANs) on reconnaissance learning (RL), for the purpose of increasing cryptographic cyber defences. CryptoGenSec applies a GAN to simulate various types of attack scenarios in cyberspace to perceive possible vulnerabilities. Then, RL refines the response strategies of our algorithm through recursive learning from the above simulations in real time and realizes the dynamic adaptation and evolution of defense mechanisms. By assessing the results of CryptoGenSec’s performance when traditional security methods are used as baselines, we can use several metrics for evaluation, such as detection accuracy, response time, resilience and evolution ability. According to these findings, the superiority of CryptoGenSec over conventional mechanisms becomes evident. To be more specific, it even shows an overwhelming edge in terms of threat detection, resulting in a 20% increase in speed of response, a 30% decrease in speed of response, and resisting power, making it 25% harder than the other methods. Moreover, it has a greater possibility of eliminating false-positive effects, which usually come from new and even dawned jeopardy: 50%. Moreover, to highlight the making-a-fortune frauds in the zero-day world, a comparison of the cohorts makes CryptoGenSec a 40% upper step. Stopping attackers from taking away all their data is also its plus point, which gains 95% achievement, whereas using mere methods only results in a 70% possibility. An enormous step in cybersecurity was taken with the combination of GANs and RL within the CryptoGenSec algorithm. Instead of being defenceless against all attacks, this approach changes and matches the threat level when necessary. The highly promising results presented here demonstrate its potential as a crucial technology for addressing the growing complexities of cyber challenges. This is a large step toward making defensive mechanisms more efficient and reliable.

Ensuring Information Security of Information Systems to be Integrated Based on Trust

Relevance. Information systems are integrated with each other, which leads to the need to ensure the protection of the integrated system. The level of trust requires formalizing the concept of trust and studying its nature and structure.The purpose of the article is to remove the contradiction between the needs to provide access to the resources of the resulting integrated system and ensure compliance with the information security requirements of each of the integrated systems by formulating the concept of trust from the information security perspective. Methods used: systems analysis, risk management theory, resolutions, iSoft operator equation synthesis method.Results. Main shortcomings of existing approaches to the formalization of the concept of “trust” are identified. Based on the FIST information system model, a functional trust structure has been developed and formalized in IDEF0 notation for all levels of integrated information systems: supporting level, personnel level, hardware and software levels. Examples of violation of trust and examples of tools for creating trust for each level of the information system are given. The adequacy of the model is illustrated by the example of real integration of information systems. Application of the proposed trust model made it possible to identify features that increase information security risks for the integrated information system from the example. Novelty. An interpretation of trust as a measure of information security is proposed, in contrast to “risk” as a measure of danger. A tool for quantitative assessment of trust is proposed. A necessary and sufficient condition for creating maximum trust in an information system is formulated and proven by the resolution method Practical significance. The proposed trust model can be used in the development of guidance documents regulating the process of integration of information systems, in setting requirements for service personnel and creating training programs for them, for developing information security tools and methods for their application.

Artificial Intelligence and Machine Learning in Healthcare Cybersecurity of Current Applications and Future Directions

Data communication has increased due to the growth of technology-driven services and application deployment. Because there are always security risks to networks, the increasing throughput of networks has also increased the likelihood of cybercrimes. Additionally, the abrupt end of the COVID crisis, which caused nearly all services to go online, has increased awareness of the threat posed by cyberattacks in general. Thus, in the information and technology-driven world of today, reliable communication is crucial. The network is frequently discovered to be vulnerable to a variety of network attacks, such as spoofing, malware, and DDoS attacks. The difficulties caused by intrusions have also been covered in the current work, along with the methods to prevent and detect these kinds of intrusion attempts that have been under investigation for the past few years. Research needs have been identified by presenting and analysing the body of literature currently available on IDS. A hybrid approach has been suggested based on the information to provide a secure communication mechanism and protect the network from intruders. A cyber security strategy based on ABC and 3D CNN architecture is suggested as a solution to this. The qualities that reflect malware and DDoS attack aspects are chosen and optimised using the ABC. Neural networks are trained and classified using the retrieved data. The accuracy, recall, and f-measure analysis of the suggested cyber security method for malware and DDoS node detection were assessed. Comparative investigation demonstrated that the cyber security method was more effective at identifying network attacks.

Design of Robotics Implementation of A Fingerprint Biometric System

Biometrics, rooted in the Greek terms "bios" (life) and "metrikos" (measure), represents a modern approach to identifying individuals based on their unique physical characteristics, such as fingerprints, facial features, and iris patterns. In today's increasingly digital landscape, traditional security methods like passwords and keys have become inadequate in safeguarding sensitive information. The rise of sophisticated cyberattacks and unauthorized access has highlighted the need for more advanced and secure authentication methods. This project aims to design and implement a fingerprint biometric system using robotics and FPGA technology. The system is intended to provide a more reliable and secure method of verifying identities by capturing and processing fingerprint images. It will extract minutiae points, the unique features within a fingerprint, to create a robust authentication mechanism. This approach promises to enhance security by offering an alternative to conventional methods that are often vulnerable to breaches. In developing this biometric system, the focus will be on ensuring that it can be effectively integrated into various applications where secure access control is crucial. By leveraging the unique and immutable nature of fingerprints, the system will reduce the risks associated with password reuse, theft, and unauthorized sharing. The inclusion of continuous authentication techniques will further strengthen security, ensuring that the user’s identity is verified throughout a session, not just at the initial login. Additionally, the project will incorporate version control mechanisms to track the development and deployment of the system, ensuring that any changes or updates can be easily managed and reviewed. This will help maintain the system's reliability and effectiveness over time, allowing it to adapt to evolving security challenges. Through this work, we aim to contribute to the broader adoption of biometric technologies in securing digital and physical environments, paving the way for more secure and seamless user experiences.