1,721,031 research outputs found
Smart Cameras with onboard Signcryption for Securing IoT Applications
No full textCameras are expected to become key sensor devices for various internet of things (IoT) applications. Since cameras often capture highly sensitive information, security is a major concern. Our approach towards data security for smart cameras is rooted on protecting the captured images by signcryption based on elliptic curve cryptography (ECC).
Signcryption achieves resource-efficiency by performing data signing and encryption in a single step. By running the signcryption on the sensing unit, we can relax some security assumptions for the camera host unit which typically runs a complex software stack. We introduce our system architecture motivated by a typical case study for camera-based IoT applications, evaluate security properties and present performance results of an ARM-based implementatio
Backdoor Malware Detection in Industrial IoT Using Machine Learning
Full text linkWith the ever-increasing continuous adoption of Industrial Internet of Things (IoT) technologies, security concerns have grown exponentially, especially regarding securing critical infrastructures. This is primarily due to the potential for backdoors to provide unauthorized access, disrupt operations, and compromise sensitive data. Backdoors pose a significant threat to the integrity and security of Industrial IoT setups by exploiting vulnerabilities and bypassing standard authentication processes. Hence its detection becomes of paramount importance. This paper not only investigates the capabilities of Machine Learning (ML) models in identifying backdoor malware but also evaluates the impact of balancing the dataset via resampling techniques, including Synthetic Minority Oversampling Technique (SMOTE), Synthetic Data Vault (SDV), and Conditional Tabular Generative Adversarial Network (CTGAN), and feature reduction such as Pearson correlation coefficient, on the performance of the ML models. Experimental evaluation on the CCCS-CIC-AndMal-2020 dataset demonstrates that the Random Forest (RF) classifier generated an optimal model with 99.98% accuracy when using a balanced dataset created by SMOTE. Additionally, the training and testing time was reduced by approximately 50% when switching from the full feature set to a reduced feature set, without significant performance loss
Position-Agnostic Smartphone Placement Detection for Improved Reliability in Human Activity Recognition
No full textThis research aims to solve the problem of position-independent activity recognition, a critical aspect in accurately identifying human activities using smartphones. Our study addresses this challenge by employing Convolutional Neural Networks to classify activities such as walking, sitting, running, and more, regardless of the smartphone’s position on the body. Leveraging a real-world publicly available dataset, we demonstrate 98% accuracy obtained solely from accelerometer data, surpassing state-of-the-art techniques by 5.77%. This advancement holds promise for enhancing smartphone-based human activity recognition, particularly in security-related applications like adaptive user authentication. Overall, our research demonstrates progress toward improving the reliability and adaptability of activity recognition systems across diverse contexts
Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications
No full textSmart cameras are key sensors in Internet of Things (IoT) applications and often capture highly sensitive information. Therefore, security and privacy protection is a key concern. This paper introduces a lightweight security approach for smart camera IoT applications based on elliptic-curve (EC) signcryption that performs data signing and encryption in a single step. We deploy signcryption to efficiently protect sensitive data onboard the cameras and secure the data transfer from multiple cameras to multiple monitoring devices. Our multi-sender/multi-receiver approach provides integrity, authenticity, and confidentiality of data with decryption fairness for multiple receivers throughout the entire lifetime of the data. It further provides public verifiability and forward secrecy of data. Our certificateless multi-receiver aggregate-signcryption protection has been implemented for a smart camera IoT scenario, and the runtime and communication effort has been compared with single-sender/single-receiver and multi-sender/single-receiver setups
Behavioral Analysis of Backdoor Malware Exploiting Heap Overflow Vulnerabilities Using Data Mining and Machine Learning
No full textBackdoor malware remains a persistent and elusive threat that successfully evades conventional detection methods through intricate techniques, such as registry key concealment and API call manipulation. In this study, we introduce an approach to detect backdoor malware, drawing upon the diverse domains of cybersecurity. Our method combines static and dynamic analysis techniques with machine learning methodologies, particularly emphasizing classification and feature engineering. Through static analysis, we extract valuable raw features from malware binaries. Discerning the most significant attributes, we delve into the calling frequencies embedded within these raw features. Subsequently, these selected attributes undergo a meticulous refinement process facilitated by feature engineering techniques, culminating in a streamlined set of distinctive features. To accurately detect malware exploiting heap-based overflow vulnerabilities, we employ three distinct yet potent classifiers: J48, Naïve Bayes, and Simple Logistic. These classifiers are trained and tested using carefully curated feature sets. Our approach combines machine learning and data mining principles to develop a comprehensive malware detection methodology. We demonstrate the efficacy of our approach through rigorous validation using two distinct settings: a dedicated training/testing set and a comprehensive 10-fold validation. Our approach simultaneously achieves 90.29% and 84.46% accuracy in train/ test split and cross-validation strategies
Say-on-pay votes and their impact on CEO power, firm performance and firm strategic policies : evidence from Anglo-Saxon economies
No full textThis thesis aims to provide additional insights into the understanding and the importance of various types of SOP votes. Motived by a new regulation called “Pay Ratio Disclosure” in the UK and the USA, and the subsequent changes of SOP regulation in Australia and the UK, which have not been covered in previous studies, this thesis aims to investigate the impact of SOP votes on CEO power as measured by the ratio of CEO pay to the average employee pay; on firm performance; and on firm strategic policiesA data is obtained for a sample 1931 listed firms in the four countries, namely, Australia, Canada, the UK and the USA during the period from 2012 to 2015 in Australia and Canada, from 2014 to 2016 in the UK, and from 2011 to 2015 in the USA. These periods are different since they are based on the date of adopting the SOP law. By employing a Limited Information Maximum Likelihood (LIML) estimator, the findings of the empirical analyses show that CEO power is negatively impacted by SOP votes in the four countries. This indicates that shareholders’ voice is successful in reducing managerial power, regardless of the nature of votes.Furthermore, the current research suggests that efficiency improvement may come via some other mechanisms, for example, the pressure from shareholders’ active monitoring. In addition, multiple evidences that emerged from this study suggest that stock market returns are driven by factors beyond the control of corporate managers. This study also finds that the varying effects of SOP votes on firms’ strategic policies might be ascribed to either the adoption of a specific SOP practice or the effectiveness of the board . The thesis’s findings have several implications for shareholders, firms, government and policymakers
Analysis of financial structure, transparency and accountability in the care market in England
No full textFinancial indicators extracted where available for the 59 largest care providers in the English care market alongside ownership information and a benchmark score for these organisations modern slavery reporting using the CCLA Modern Slavery Benchmark too
Say-on-pay votes and their impact on CEO power, firm performance and firm strategic policies : evidence from Anglo-Saxon economies
Full text linkThis thesis aims to provide additional insights into the understanding and the importance of various types of SOP votes. Motived by a new regulation called “Pay Ratio Disclosure” in the UK and the USA, and the subsequent changes of SOP regulation in Australia and the UK, which have not been covered in previous studies, this thesis aims to investigate the impact of SOP votes on CEO power as measured by the ratio of CEO pay to the average employee pay; on firm performance; and on firm strategic policiesA data is obtained for a sample 1931 listed firms in the four countries, namely, Australia, Canada, the UK and the USA during the period from 2012 to 2015 in Australia and Canada, from 2014 to 2016 in the UK, and from 2011 to 2015 in the USA. These periods are different since they are based on the date of adopting the SOP law. By employing a Limited Information Maximum Likelihood (LIML) estimator, the findings of the empirical analyses show that CEO power is negatively impacted by SOP votes in the four countries. This indicates that shareholders’ voice is successful in reducing managerial power, regardless of the nature of votes.Furthermore, the current research suggests that efficiency improvement may come via some other mechanisms, for example, the pressure from shareholders’ active monitoring. In addition, multiple evidences that emerged from this study suggest that stock market returns are driven by factors beyond the control of corporate managers. This study also finds that the varying effects of SOP votes on firms’ strategic policies might be ascribed to either the adoption of a specific SOP practice or the effectiveness of the board . The thesis’s findings have several implications for shareholders, firms, government and policymakers
Transparent and Trustworthy Blockchain-based Scheme for the Protection of Vehicular Soft Integrity in Shared Mobility
Full text linkThe automotive industry is transforming from traditional private vehicle ownership to innovative shared mobility solutions, presenting unprecedented cybersecurity challenges. This transition introduces complex security vulnerabilities where malicious actors could exploit the access of a rental vehicle to manipulate the software systems on board. Unlike physical damage, which can be easily detected, software modifications represent an insidious threat that can compromise user safety and vehicle integrity. Our research proposes a blockchain-based approach to address these critical security challenges. We introduce a novel method for ensuring data authenticity and integrity within vehicle systems by leveraging blockchain’s immutable ledger and advanced encryption technologies. Our methodology utilizes the Trusted Platform Module (TPM) to securely archive vehicle data in the central gateway, creating a tamper-evident environment that fundamentally transforms traditional data management approaches. The key innovation lies in the blockchain-based data binding process: when a user possesses a vehicle, they bind application-retrieved data with the vehicle’s existing data and commit them to the blockchain. Upon vehicle return, any potential tampering can be immediately detected by comparing newly acquired data against pre-existing blockchain records. We develop a proof-of-concept implementation and demonstrate significant improvements in security architecture that offer a reliable alternative to conventional database-centric approaches. Comparative evaluations between database-centric and blockchain-centric architectures testify to the operational effectiveness and practical viability of our proposed solution. By addressing the inherent vulnerabilities in shared mobility ecosystems, this research contributes to a sophisticated technological intervention that enhances user safety, data integrity, and trust in emerging transportation paradigms
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