Secure Identification Research Articles

Comparative analysis of impact of classification algorithms on security and performance bug reports

Abstract Identification and classification of bugs, e.g., security and performance are a preemptive and fundamental practice which contributes to the development of secure and efficient software. Software Quality Assurance (SQA) needs to classify bugs into relevant categories, e.g., security and performance bugs since one type of bug may have a higher preference over another, thus facilitating software evolution and maintenance. In addition to classification, it would be ideal for the SQA manager to prioritize security and performance bugs based on the level of perseverance, severity, or impact to assign relevant developers whose expertise is aligned with the identification of such bugs, thus facilitating triaging. The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. We first label the existing dataset from the Bugzilla repository with the help of a software security expert to train the algorithms. Our research type is explanatory, and our research method is controlled experimentation, in which the independent variable is prediction accuracy and the dependent variables are ANN, SVM, NB, DT, LR, and KNN. First, we applied preprocessing, Term Frequency-Inverse Document Frequency feature extraction methods, and then applied classification algorithms. The results were measured through accuracy, precision, recall, and F-measure and then the results were compared and validated through the ten-fold cross-validation technique. Comparative analysis reveals that two algorithms (SVM and LR) perform better in terms of precision (0.99) for performance bugs and three algorithms (SVM, ANN, and LR) perform better in terms of F1 score for security bugs as compared to other classification algorithms which are essentially due to the linear dataset and extensive number of features in the dataset.

Machine Learning and Metaheuristic Algorithms for Voice-Based Authentication: A Mobile Banking Case Study

These days, every bank requires secure identification before granting access to personal accounts. Voice authentication is increasingly popular for important mobile processes. The hybrid authentication technique is used in this work to address concerns about forgery attacks in the voice authentication system. Here, we accomplish this goal by using a user authentication approach that decodes passwords from acoustic signals. This method involves the user speaking into the phone to enter their private password. Then, to crack the code, an artificial neural network is employed along with retrieved statistics and speech parameters such as energy and Mel frequency cepstral coefficient. The password numbers are read and saved. The planned application divides the read integers by using the pauses in between the digits. On the other hand, by employing the frequency and phase properties to compare the target's voice to the discovered password key, this method will prevent speech forgeries. This speech and password matching verification system uses our fuzzy nonlinear support vector machine network classification system, which was trained using the Ali Baba and the forty thieves algorithm. Our method is evaluated on a dataset of 30 individuals and three smartphones, achieving an accuracy rate of more than 98.29%. Our system is resistant against a wide range of challenges, including variations in authentication angle, authentication distance, passphrase length, ambient noise, and more, in addition to being device independent.

Enhancing societal security: a multimodal deep learning approach for a public person identification and tracking system

In public spaces, threats to societal security are a major concern, and emerging technologies offer potential countermeasures. The proposed intelligent person identification system monitors and identifies individuals in public spaces using gait, face, and iris recognition. The system employs a multimodal approach for secure identification and utilises deep convolutional neural networks (DCNNs) that have been pretrained to predict individuals. For increased accuracy, the proposed system is implemented on a cloud server and integrated with citizen identification systems such as Aadhar/SSN. The performance of the system is determined by the rate of accuracy achieved when identifying individuals in a public space. The proposed multimodal secure identification system achieves a 94% accuracy rate, which is higher than that of existing public space person identification systems. Integration with citizen identification systems improves precision and provides immediate life-saving assistance to those in need. Utilising secure deep learning techniques for precise person identification, the proposed system offers a promising solution to security threats in public spaces. This research is necessary to investigate the efficacy and potential applications of the proposed system, including accident identification, theft identification, and intruder identification in public spaces.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Comprehensive Analysis of Security Requirements Engineering Approaches with Assurance Perspective

Given the wide distribution of software and its current growth, security is inevitable, and software systems have become more complex. To avoid the complexity and protect valuable assets of software systems, researchers, and practitioners in the fields of security requirement engineering (SRE) and security assurance advise incorporating security requirements early in software development life cycle (SDLC). Nowadays, security requirements specification and identification are an active research area for ensuring the effectiveness of software systems. However, security requirement identification is a challenging task due to sheer numbers of threats and attacks to a software system. Previous researchers have identified different SRE techniques, each looking at the same problem from a different perspective. Therefore, a literature review conducted on SRE techniques. We compared and analyzed different SRE techniques to find the most suitable SRE techniques for researchers and practitioners. Selected SRE techniques compared based on various parameters, such as different attributes, requirement elicitation, requirement analysis, project size, and integration of standards. The literature also indicates that among SRE approaches, security quality requirements engineering (SQAURE), secure tropos, security requirements engineering process (SREP), and comprehensive lightweight application security process (CLASP) are the most used SRE approaches that focus on activities such as threats, security requirements identification, and security objectives identification. Unfortunately, several SRE approaches fail to explicitly integrate the security standard and security assurance perspective. Three questions developed for this study: Question 1 is based on comparison of existing SRE approaches, Question 2 is based on SRE with security requirements elicitation and analysis, and Question 3 highlights the incorporation of security requirements assurance by SRE approaches implicitly or explicitly. The researcher must work with the developer to easily adopt SRE approaches to elicit and ensure security requirements for the software system.

Features of creating a multi-level model of cyber security of critical infrastructure data: an interdisciplinary approach

The article examines the model of ensuring cyber security of Ukraine and documents the organizational and legal support for the protection of critical infrastructure objects from cyber attacks. The main concepts are described: multi-level data model, identification of cyber security, and critical infrastructure facilities. The levels of protection of critical infrastructure objects and measures at each level that ensure cyber security are highlighted and described. The stability of the critical infrastructure object is substantiated, and the formula for determining risks in critical infrastructure objects is described. The developed basic tuple model for the classification of critical information infrastructure objects by multi-level data models is highlighted. The prospects and risks of implementing multi-level data models in critical infrastructure facilities have been identified. It was concluded that multi-level data models are an important tool for ensuring cyber security in today's digital world and critical infrastructure enterprises in private. They allow for the effective protection of confidential information by isolating and controlling different levels of data access

Realizing Bright-Dark Dual-Field Multimode Optical Signals in Photochromic Apatite Phosphors for Security Identification.

Regulating defect distribution in inorganic phosphors is paramount for realizing multimode dual-field optical signals for high security level identification but remains an ongoing challenge. Here, we propose a strategy of equivalent anion doping and nonequivalent cation doping to successfully regulate the trap distribution and density in Ba5(PO4)3Cl:F-,Eu2+,Ce3+ (BPCF-AG) phosphors. Due to the coexistence of shallow and deep traps for different photon processes, the BPCF-AG exhibits simultaneous photochromism in a bright field and tetramode luminescence (photoluminescence, afterglow, 980 nm photostimulated luminescence, and 650/532 nm photostimulated afterglow) in a dark field. The trap roles responsible for versatile optical behaviors are investigated by thermoluminescence curves, and a reasonable mechanism is proposed. In addition, we design a series of demonstrations for security identification and information encryption based on the dual-field multimode optical signals of BPCF-AG to illustrate its potential application scenarios.

Secure discovery of genetic relatives across large-scale and distributed genomic data sets.

Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging owing to the burden of estimating kinship between all the pairs of individuals across data sets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing (LSH) approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us data sets. On a data set of 200,000 individuals split between two parties, SF-Relate detects 97% of third-degree or closer relatives within 15 h of runtime. Our work enables secure identification of relatives across large-scale genomic data sets.

NIST CSF-2.0 Compliant GPU Shader Execution

This article introduces a mechanism for ensuring trusted GPU shader execution that adheres to the NIST Cybersecurity Framework (CSF) 2.0 standard. The CSF is a set of best practices for reducing cybersecurity risks. We focus on the CSF’s identification, protection, detection, and response mechanisms for GPU-specific security. To this end, we exploit recent advancements in side-channel analysis and hardware-assisted security for the real-time and introspective monitoring of shader execution. We prototype our solution and measure its performance across different GPU platforms. The evaluation results demonstrate the effectiveness of the proposed mechanism in detecting anomalous shader behaviors that only incur modest overhead at runtime. Integrating the CSF 2.0 principles into the proposed GPU shader pipeline leads to an organizational recipe for securing heterogeneous computing resources.

Effective Intrusion Detection through Hybrid CNN-LSTM and Grey Wolf Optimization for Feature Selection in Complex Network Environments

Security and threat identification have always been critical in the interconnected world we live in today, and with emerging and cutting-edge threats, they still remain vital. Consequently, this paper proposes an intrusion detection solution whereby GWO for feature selection to optimize the detection model and a CNN–LSTM for attack detection. This paper describes the potential threat attributed to high dimensionality and non-linearity of network traffic data and aims to improve the overall detection rate and accuracy of different types of cyber-attacks. The GWO algorithm efficiently addresses the selection of relevant features from the data, hence demystifying the data while enhancing the model’s impact. CNN-LSTM architecture adapted from convolution neural networks and long short-term memory nets which performed the best in feature recognizing on networks traffic, allows the detection model to get understanding about complicated patterns in traffic. The described approach is compared with common methods, including Logistic Regression, Gaussian Naive Bayes, Decision Tree, and Random Forest and outperforms each of those regarding precision, recall and F1-score. Evaluation of the results obtained indicates that the proposed system correctly identifies various types of attacks with high precision, and very sensitive to false positive, and scenes the DoS/DDoS, PortScan, BruteForce, Web Attacks, and Botnet attacks correctly and distinctly. This paper shows the efficiency of integrating the state-of-art optimization methods with DL-based models for the development of the sound and highly scalable intrusion detection framework. The proposed method provides a much higher level of security enhancement in the proposed network against the emerging threat of cyber-attacks on existing and new complex networks.

Evaluating CNN Models for Gait Recognition: A Study on the CASIA-B Dataset

Gait recognition, a form of biometric identification, has garnered considerable interest for its ability to identify individuals from a distance. Yet, its effectiveness depends significantly on the precision of the underlying categorization models. This study thoroughly evaluates many Convolutional Neural Network (CNN) models to determine their effectiveness in properly identifying and categorizing gait patterns. We train and test several CNN architectures using advanced deep learning techniques on the widely known CASIA-B dataset, which is a benchmark dataset for gait recognition research. The analyses involve models like as VGG16, VGG19, NASNetLarge, NASNetMobile, EfficientNetB0, and Xception, each well-known for their effectiveness in picture categorization tasks. We evaluate the performance of these models by conducting thorough experiments and detailed analysis, focusing on accuracy, validation loss, and other pertinent metrics. The Xception model demonstrated the highest accuracy of 97.17% of the models assessed. This model frequently surpasses similar models, demonstrating its strength and effectiveness in precisely recognizing gait patterns. On the other hand, the accuracy of other models varied from 9.78% to 93.85%, demonstrating the variable levels of efficacy among various designs. The findings have significant implications for the creation and implementation of gait recognition systems. The high precision demonstrated by the Xception model highlights the promise of CNN-based methods in progressing gait identification technology. Our work highlights the significance of choosing suitable model architectures for achieving the best performance in gait detection tasks. In the future, research may focus on using larger datasets and exploring other CNN architectures to improve the accuracy and reliability of gait detection systems. Our goal is to enhance gait recognition technology to improve biometric identification for security, surveillance, and healthcare applications. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 17-26

Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion

Abstract Today, advancements in science and technology have spurred the rapid evolution of systems like electronic banking, demanding precise, swift, and secure identification of individuals based on their distinct traits. Among these traits, fingerprints stand out as a dependable means of identification, finding application in realms such as crime investigation and national border control due to their simplicity and heightened security. The qualities inherent in fingerprint-based identification have led to its widespread adoption over other identification methods. This article proposes a hybrid biometric system that integrates the Gabor filter and scale-invariant feature transform features and then uses support vector machine and K-nearest neighbors as classifiers, aiming to notably enhance authentication systems by mitigating issues seen in single-method biometric systems. Also, principal component analysis is used to reduce dimensions and eliminate redundancy. In this article, the famous database FVC2004 is used. Test results highlight the considerable reliability and accuracy of the proposed combined approach compared to systems reliant on a singular biometric method.

Опыт строительства системы обеспечения национальной безопасности в США

The conceptual framework of national security in the United States of America has undergone significant transformation from the identification of national security with the military component of state policy to the modern systemic approach of “security through development”. This paper delineates the peculiarities of USA national security policy, including the principles of exclusivity, messianism, the interpretation by American elites of national security as the ability to counter external threats, the formulation of policies from a position of strength using coercive means, and a solid scientific basis. It is emphasized that the institutional contours of the modern USA national security system were laid down at the turn of the 1940–1950s with the establishment of the National Security Council. The analysis of the experience in constructing the USA national security system is multifaceted and reflects its fundamental characteristics, which have both theoretical and practical significance, enabling the systematic strengthening of national security. Based on the US experience, the author formulates proposals for improving the Russian national security system. The scientific novelty is expressed in the proposal and justification of the establishment of a special position of coordinator for strategic communications within the Administration of the President of the Russian Federation.

Dynamic Security Rule Optimization Based on Deep Learning and Adaptive Algorithms

Compared with traditional computer network security identification techniques, deep learning algorithms are based on their own distributed network structure for storage, processing, classification, comparison, and automatic identification functions. The distribution and dynamism of cloud databases increase the difficulty of route prediction and recognition in the cloud, affecting the efficiency of cloud computing. In response to the above issues, the author proposes a dynamic path optimization process for cloud databases based on adaptive immune grouping polymorphic ant colony algorithm. By setting up two states of ant colony, reconnaissance ant and search ant, and introducing an adaptive polymorphic ant colony competition strategy, the defect of general ant colony algorithms being prone to falling into local optima is improved; On this basis, an artificial immune algorithm with fast global search capability is further integrated to improve the search ant path optimization process, improving search speed and accuracy. Simulation experiments show that the IPANT algorithm outperforms the other three algorithms, maintaining a throughput of 1000 kbps and relatively stable; The data of OSPF, SPF, and FR are not significantly different, significantly lower than IPANT. The immune polymorphic ant colony algorithm (IPANT) has the lowest time delay for packet routing and performs better than the other three algorithms, with FR and SPF having higher latency. It has been proven that this algorithm can better solve convergence speed and global optimization problems, and can quickly and reasonably find the database to be accessed in the cloud.

Biometric Breakthroughs for Sustainable Travel: Transforming Public Transportation through Secure Identification

This study investigates the use of biometric technology in public transit to improve trip safety and effectiveness. The incorporation of biometric technology into transit networks improves efficiency and security but also poses substantial challenges of privacy, standardisation, and public acceptability. Conducted via a poll of 25 specialists in Lithuania, the objective was to assess the level of acceptability and practicality of using biometric identification for both drivers and passengers. The results suggest a divided view about the specific demographic that biometric applications should target. However, there is agreement on the considerable potential of these technologies to enhance transportation safety. Face recognition has been the favoured approach due to its non-intrusive nature and simplicity of integration. The statistical research demonstrated significant positive correlations between different biometric approaches, indicating that a multimodal strategy is effective for providing full security coverage. The research highlights the significance of resolving privacy issues, emphasising that public acceptability depends on the open management and strong safeguarding of biometric data. The findings support the deliberate use of biometric technologies in sustainable public transportation, emphasising their ability to improve safety, optimise operations, and even revolutionise the passenger experience. This emphasises the equitable examination of technology, security, and privacy in the progress of sustainable public transportation systems. Biometric technology in public transport, especially for monitoring driver health and ensuring passenger safety, is supported by experts as a means to enhance service quality, reduce accidents, and optimize route planning.

The Application of Cathodoluminescence (CL) for the Characterization of Blue Pigments

The combined application of Cathodoluminescence (CL) with Scanning Electron Microscopy (SEM) on paintings and painted surfaces has the potential to identify both organic and inorganic pigments on a micrometre or even nanometre scale. Additionally, the combination with Energy-Dispersive Spectrometry (EDS) allows for a more holistic, elemental, and mineralogical characterization of pigments. In addressing the need for the creation of a robust, open access database of characteristic CL spectra of pigments, a large project has been undertaken, focusing primarily on common organic and inorganic pigments. The present paper focuses on the CL characterization of 10 significant blue pigments in pure powder form: cerulean blue, Egyptian blue, Han blue, indigo, lapis lazuli, Maya blue, phthalo blue, vivianite, ultramarine blue, and zirconium blue. The CL spectra present characteristic bands for most of the pigments, allowing their secure identification, especially when combining the results with the EDS analyses. The effect of binding media and of the mixture of different pigments was also studied, via the analysis of mixtures of pigments with oil painted over canvas. Overall, both the binding medium and the mixture of pigments do not appear to create significant differences in the occurring spectra, thus allowing the identification of individual pigments. EDS and RAMAN spectra are included in order to facilitate comparison with other databases.

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration.

Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual's identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.

Simultaneous NIR Emission and Thermal Stability Enhancement in Garnet‐Type NIR Phosphors through the Synergistic Effect of Lattice Distortion and Enhanced Rigidity

AbstractEven though there have been significant advancements in the development of Cr3+‐activated near‐infrared (NIR) phosphors, the challenge still remains to develop highly efficient and thermally stable NIR phosphors. Here, the Ca4‐xZnxHfGe3O12:0.03Cr3+ solid solution phosphors with 834–806 nm NIR emission are constructed by substituting Zn2+ for Ca2+, thereby facilitating the formation of [ZnO6] luminescence site. The coexistence of [HfO6] and [Zn/CaO6] luminescence centers is confirmed through DFT calculation, time‐resolved photoluminescence (TRPL) spectroscopy, and low‐temperature‐photoluminescence (77 K) spectroscopy. The formation of [ZnO6] effectively resolves the issue of lattice mismatch between Cr3+ and Ca2+. Furthermore, the simultaneous enhancement of luminescence intensity and thermal stability is realized through a synergistic combination of lattice distortion and rigidity enhancement. By optimizing the substitution concentration of Cr3+, the internal quantum efficiency (IQE) of 92% and an external quantum efficiency (EQE) of 29% are finally achieved. Meanwhile, the thermal stability is also enhanced from 59%@400 K (x = 0) to 81%@400 K (x = 0.8). The developed NIR phosphor‐converted light‐emitting diodes (pc‐LEDs) exhibit promising prospects in the fields of security, biomedicine, non‐destructive testing and rapid identification.

Triboelectric sensor-empowered intelligent mouse combined with machine learning technology strides toward a computer security system

The computer mouse is one of the most common tools used for human-machine interfacing. The human behavior studying by click operation using an intelligent mouse and related security identification of computers remains a great challenge. Here, we develop an intelligent mouse embedded with a contact-separation adaptable triboelectric sensor based on the intrinsic mouse button click operation. With the unique characteristics of different individuals who use the intelligent mouse and machine learning algorithms, an identity recognition system was designed. Through processes such as signal processing, feature extraction, and model training, high-accuracy (98.4 %) identification of different individuals based on the signals from the embedded triboelectric sensor in the mouse was achieved. In addition, we have developed a security alarm system for detecting unauthorized users operating the computer, and successfully validated the practicality of this system. A system for monitoring user behaviors while operating the computer has been designed to meet the monitoring needs of user computer usage states. This system can monitor users' gaming, working, or studying states, while effectively protecting users' privacy. In general, this work reveals a new strategy for security identification of computers which can be potentially applied in cyber and information security.

All-silicon multidimensionally-encoded optical physical unclonable functions for integrated circuit anti-counterfeiting

Integrated circuit anti-counterfeiting based on optical physical unclonable functions (PUFs) plays a crucial role in guaranteeing secure identification and authentication for Internet of Things (IoT) devices. While considerable efforts have been devoted to exploring optical PUFs, two critical challenges remain: incompatibility with the complementary metal-oxide-semiconductor (CMOS) technology and limited information entropy. Here, we demonstrate all-silicon multidimensionally-encoded optical PUFs fabricated by integrating silicon (Si) metasurface and erbium-doped Si quantum dots (Er-Si QDs) with a CMOS-compatible procedure. Five in-situ optical responses have been manifested within a single pixel, rendering an ultrahigh information entropy of 2.32 bits/pixel. The position-dependent optical responses originate from the position-dependent radiation field and Purcell effect. Our evaluation highlights their potential in IoT security through advanced metrics like bit uniformity, similarity, intra- and inter-Hamming distance, false-acceptance and rejection rates, and encoding capacity. We finally demonstrate the implementation of efficient lightweight mutual authentication protocols for IoT applications by using the all-Si multidimensionally-encoded optical PUFs.