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Securing Automated Systems with BT: Opportunities and Challenges
The use of automated systems is becoming increasingly prevalent in various industries; however, they pose significant security risks. In order to enhance the security of these systems, Blockchain Technology (BT) provides a promising solution. This chapter discusses the opportunities and challenges associated with using BT to secure automated systems. The role of BT in securing automated systems is discussed, emphasizing its ability to improve security and transparency. Additionally, BT-based systems with enhanced security are examined, such as decentralized data management, immutable and transparent ledgers, reduced cyber-attacks, and secure data sharing. Despite these opportunities, challenges such as high computational power requirements, integration challenges, BT scalability, and regulatory challenges must be addressed. Utilizing BT can create a more secure and transparent system that can help to prevent fraud, hacking, and other forms of cyber-attacks, ultimately enhancing the reliability and safety of automated systems. In conclusion, this paper highlights the potential of using BT for securing automated systems and the need for continued research and development to overcome the challenges associated with its implementation. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Securing cloud data against cyber-attacks using hybrid aes with MHT algorithm
Cloud computing is dealing with large amount of data during data communication. This data processing is named as big data. The big data is growth of the demand in accessing the storage, computation and communication. This big data has the major defects. A raising issue in emerging big data is cost minimization. The architecture of big data ranges over multiple machines and cluster which have sub system. The major challenge of this big data is preprocessing and analysing the data patterns. This research article is dealing with different data pre-processing and secure data storage. There are many research challenges during this data process. The possible gap and drawbacks in the technology are identified through this survey and the efficient big data service is provided through MHT and AES algorithm. The main aim of this proposed method is to provide better data security during larger data process. The proposed hybrid MHT with AES algorithm is to minimize the encryption and decryption time apart from that it reduces the attacker ratio. All these parameters automatically increase the Quality of Service. Copyright Research Institute for Intelligent Computer Systems, 2020. All rights reserved. -
Securing end user computing environments using blockchain
COVID-19's phenomenal effect has expedited the adoption of digital technologies over several years, indicating that several of these breakthroughs are here to stay. Several enabling technologies are currently being implemented as major solutions for improving and responding to the pandemic's numerous issues, with blockchain being one of the preferred solution options. Blockchain can be used to re-structure processes, resulting in most effective operational and business models (e.g., democratising quality cancer detection with advanced artificial intelligence based radiomics technologies). The authors posit here that while there is a lot of anticipation about using blockchain to improve business capabilities, the lessons learned from the many pilots and proofs of concept so far should be considered. The necessity for a structured, formal decision-making process, based on good business logic and an awareness of the problem's process lifecycle, is however critical. Blockchain is a means to an end, not an end in and of itself. 2024, IGI Global. All rights reserved. -
Securing grayscale image using improved Arnold transform and ElGamal encryption
The security of sensitive data is critical, and it opens up a wide area of research to find efficient and effective methods to prevent unauthorized access. Our study provides a secure framework for sending visual information over an untrusted channel, such as a social networking site. The proposed framework is a combination of scrambling and encryption techniques. Initially, a hybrid block-wise and pixel-wise scrambling approach is administered to the grayscale image, followed by the Arnold transform, which causes all pixel points to move within the image. Finally, to improve the efficiency of the diffusion process, the asymmetric encryption ElGamal algorithm has been mastered. Peak-signal-To-noise ratio (PSNR), structural similarity index metric (SSIM), number of pixels change rate (NPCR), and unified average changing intensity (UACI) are the metrics utilized to evaluate the efficiency of the proposed scheme. The efficiency of the suggested scheme validates the low-Average PSNR value of <9 dB and the SSIM average value of <0.01 for the encrypted images. The NPCR and UACI values achieved in our study are above the threshold values of 99.6% and 33.33%, respectively, exhibiting the strength of the proposed framework. 2022 SPIE and IS&T. -
Securing her digital footprint: AI for women's safety
This chapter emphasizes the importance of artificial intelligence (AI) tools, analysis about the existing AI tools, and recommendations for future AI tools for women's safety. AI is experiencing significant growth and influence in the current era. Several key trends and developments highlight the role of AI in various domains: AI is being used for medical diagnosis, drug discovery, and patient care. Machine learning models are helping doctors analyse medical images, predict disease outcomes, and personalize treatment plans. Self-driving cars and drones are utilizing AI algorithms for navigation, obstacle detection, and decision-making. These technologies are advancing transportation and logistics. Natural language processing models like GPT-3 are transforming language-related tasks, from chatbots and virtual assistants to content generation, translation, and sentiment analysis. This chapter highlights the AI tools that exist for women's safety in the digital world and future apps needs for the same. 2024, IGI Global. All rights reserved. -
Securing International Law Against Cyber Attacks through Blockchain Integration
Cyber-attacks have become a growing concern for governments, organizations, and individuals worldwide. In this paper, we explore the use of blockchain technology to secure international law against cyber-attacks. We discuss the advantages of blockchain technology in providing secure and transparent data storage and transmission, and how it can enhance the security of international law. We also review the current state of international law regarding cyber-attacks and the need for a robust and effective legal framework to address cyber threats. The study proposes a blockchain-based approach to secure international law against cyber-attacks. We examine the potential of blockchain technology in providing a decentralized and tamper-proof database that can record and track the implementation of international laws related to cyber-attacks. We also discuss how smart contracts can be utilized to automate compliance with international laws and regulations related to cybersecurity. The study also discusses the challenges and limitations of using blockchain technology to secure international law against cyber-attacks. These include the need for interoperability between different blockchain networks, the high energy consumption of blockchain technology, and the need for international cooperation in implementing and enforcing international laws related to cybersecurity. Overall, this study provides a comprehensive overview of the potential of blockchain technology in securing international law against cyber-attacks. It highlights the need for a robust legal framework to address cyber threats and emphasizes the importance of international cooperation in implementing and enforcing international laws related to cybersecurity. 2023 IEEE. -
Securing iot networks using an onion routing based approach
Internet of Things (IoT) comprises of small, connected, power-efficient devices with minimal to average computing power. The devices are autonomous, cyber-physical objects capable of sensing, processing, storing and networking. Due to their connected nature, they are exposed to a huge number of threats and vulnerabilities. A new gateway to ensure anonymity in IoT networks using The Onion Router (TOR) hidden services in a Single-Board Computer (SBC) is proposed in this paper. IAEME Publication. -
Securing medical images using encryption and LSB steganography
Medical imaging plays a vital role in the healthcare industry. Due to the advancements in the healthcare industry medical images are being transferred between geographically spread regions. As the medical images are transmitted through public networks several security challenges may arise such as authentication, integrity, and confidentiality. In this paper, the research focus is on preserving the confidentiality and integrity of the medical images. In general, integrity and confidentiality can be achieved by encryption (a piece of cryptography). To incorporate one more layer of security, steganography is applied to the medical image. In this paper, a one-time pad encryption algorithm is used to encrypt the medical image, then the encrypted image is implanted into a cover image to give a stego image making the system more resilient to the attacker. Further in this paper, the above said method is being implemented using MATLAB and the experimental results are compared with hiding the medical image using LSB steganography without encryption (one layer of security). Various formats of medical images have been taken into consideration such as DICOM, TIFF, BMP, and JPEG. Results show that the combination of encryption and steganography performs better than applying only steganography, concerning MSE and PSNR values. 2021 IEEE -
Securing patient information: A multilayered cryptographic approach in IoT healthcare
The increasing integration of devices utilising the of Internet of Things (IoT) in healthcare has resulted in the collection of an unparalleled volume of patient data. Personal identifiers, insurance information, medical history, and health monitoring measures are all included in a complete dataset. Ensuring security and privacy of IoT devices is crucial in the healthcare sector. The goal of this project is to combine steganography with three different cryptographic algorithms to develop a hybrid cryptographic technique. Among the algorithms under investigation are steganography, Caesar cipher, columnar transposition cipher, and one-time pad. Every encryption scheme uses three keys to encrypt patient data. The encrypted data is subsequently encoded into an image file through image-based steganography. To ensure confidentiality and authentication, an authorised user can decrypt the file through a designated decryption process, maintaining the integrity of patient data. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Securing patient information: A multilayered cryptographic approach in IoT healthcare
The increasing integration of devices utilising the of Internet of Things (IoT) in healthcare has resulted in the collection of an unparalleled volume of patient data. Personal identifiers, insurance information, medical history, and health monitoring measures are all included in a complete dataset. Ensuring security and privacy of IoT devices is crucial in the healthcare sector. The goal of this project is to combine steganography with three different cryptographic algorithms to develop a hybrid cryptographic technique. Among the algorithms under investigation are steganography, Caesar cipher, columnar transposition cipher, and one-time pad. Every encryption scheme uses three keys to encrypt patient data. The encrypted data is subsequently encoded into an image file through image-based steganography. To ensure confidentiality and authentication, an authorised user can decrypt the file through a designated decryption process, maintaining the integrity of patient data. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Securing Provenance Data with Secret Sharing Mechanism: Model Perspective
Elicitation about the genesis of an entity is referred to as provenance. With regards to data objects and their relationships the same is termed as data provenance. In majority of the instances, provenance data is sensitive and a small variation or adjustment leads to change in the entire chain of the data connected. This genesis needed to be secured and access is granted for authorized party. Individual control in preserving the privacy of data is common scenario and there are a good number of approaches with respect to cryptography. We propose a unique model, wherein the control of the data is available with multiple bodies however not with one; and when an access has to be granted for a genuine purpose, all the bodies holding their share will have to agree on a common platform. Combining these shares in a peculiar pattern allows the grant for accessing data. The method of allocating control to multiple bodies and allowing grant based on combining stakes is called as secret sharing mechanism. Division of the shares can be drawn from visual encryption approach. It provides transparencies for a given input message. This paper throws light on a framework associated to securing provenance via secret sharing security notion. 2019 IEEE. -
Securing the Digital Realm: Unmasking Fraud in Online Transactions Using Supervised Machine Learning Techniques
A key component of contemporary banking systems and e-commerce platforms is identifying fraud in online transactions. Traditional rule-based techniques are insufficient for preventing sophisticated fraud schemes because of the increasing complexity and number of expanding online transactions. This research study examines the development of fraud detection methods, emphasizing data analytics and machine learning (ML) models. The study also focuses on the fact that developing efficient fraud detection systems requires continuous observation, data preprocessing, feature selection, and testing of models. Seven ML models, Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbors (kNN), Nae Bayes (NB), Support Vector Machine (SVM), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) are considered for classifying the dataset into fraudulent or not. During the experimentation study, it was observed that XGBoost yielded the highest accuracy of 99% when compared to other models. Users can determine which features significantly influence the model's predictions by using XGBoost's feature significance insights. Additionally, XGBoost provides integrated support for managing missing values in data, negating the requirement for imputation and other preprocessing procedures. Due to these, it performed better. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Securing Trust in the Connected World: Exploring IoT Security for Privacy in Connected Environments
This abstract delves into IoT security measures to ensure privacy in connected environments. It examines encryption, authentication, access control, and data privacy techniques. Key considerations include end-to-end security, vulnerability mitigation, regulatory compliance, and user trust. By addressing these challenges, trust can be established in the connected world, enabling the widespread adoption of IoT technologies while safeguarding user privacy. 2024 IEEE. -
Security Analysis for a Revocable Multi-Authority ABE-Attribute-Based Mechanism
Due to the tremendous increase in data, groups or even organizations are storing data with third-party providers to solve storage problems. Ciphertext policy attribute based encryption helps to outsource data, which means encrypt the data at the data owners end and uploading it to third-party storage with some access policy. In normal Identity-based encryption, if a data owner wants to send information to a data user, it will be sent with some identity of the data user, such as mail id, so that only that particular user can read the message. The main problem is that the data owner should know each users identity. For instance, in some organizations where a data owner wants to send a message to a group of people with an identical designation, it can be sent with the help of the users attribute using attribute-based encryption. Here, the data owner does not need to know the specific details of each user; instead, with the help of attributes and the provided access policy, they can access this message. This research mainly focuses on three aspects of CP-ABE: access policy, number of attribute authorities, and revocation. When it comes to access policy, the currently existing access policies are not secure due to their linearity in nature because shares are always calculated using the same linear equation. So, for this problem, this work has developed a non-linear SS-secret-sharing model that increases the confidentiality of the model. 2024 Seventh Sense Research Group. -
Security analysis in multi-tenant cloud computing healthcare system
Cloud Computing is an innovation in the field of Information Technology and in healthcare system because of the deployment models which services as profitability for the tenants. Cloud Computing is cost-effective, flexible and a delivery platform which provides business and services over internet. Multi-tenancy with its hardware sharing and high degree of configurability is utilized in cloud computing health care system even though many health care organizations are unwilling to adapt due to infrastructure and security shortcoming. In order to store the sensitive health care data cloud service providers should include promising security features where both the trusted and untrusted parties should be addressed in it. This paper addresses the security requirements and security issues in multi-tenant healthcare system, a frame is proposed for analyzing the security issues based on the available requirements and possible counter measures been suggested. The security concerns are analyzed by trust, confidentiality, integrity, audit and compliances and furthermore insight for the security is provided in multi cloud with possible security recommended for healthcare system. IAEME Publication. -
Security and privacy aspects in intelligence systems through blockchain and explainable AI
Explainable AI (XAI) is a method of creating artificial intelligence (AI) systems that are transparent and understandable to humans. By allowing people to understand how the system arrived at its conclusions or suggestions, XAI systems strive to make AI more accountable, trustworthy, and ethical. Responsibility, trust, ethics, regulation, and innovation are some of the societal ramifications of XAI. By making AI systems more transparent, XAI fosters accountability. This means that consumers will be able to understand how the system made its decisions and hold it accountable if something goes wrong. By making the decision-making process more transparent, XAI fosters trust between people and AI systems. This boosts user trust in the system and encourages wider adoption of AI technologies. It also contributes to the ethical design of AI systems by making the decision-making process public in order to uncover and mitigate biases and other ethical issues that may occur in AI systems. It aids regulators and policymakers in understanding and regulating AI systems. XAI gives insight into how AI systems operate, which can assist regulators in developing laws that promote ethical and responsible AI use. Because XAI can help developers better and innovate new systems by making it easier for them to design new AI systems and by providing insights into how AI systems work. The proposed chapter will focus on important aspects of algorithmic bias and changing notions of privacy in XAI, which will necessitate the need for AI systems that can adapt accountability, trust, ethics, and compliance with regulations, as well as produce better innovation that can benefit humanity. More openness, greater control over personal data, new types of data privacy, and newer privacy networks are all required. To address algorithmic bias in XAI, it is critical to build the system so that it is aware of the possibility of bias and actively mitigates it. This can involve employing diverse and representative data, inspecting the system for unwanted features, offering detailed explanations, and incorporating a wide range of stakeholders in the system's development and deployment. The envisaged report provides a framework that combines XAI and blockchain to provide a secure and transparent way to store and track the provenance of data used by XAI systems, validate the performance of AI models stored on the blockchain on decentralized systems so that the models are stored and executed on a distributed network of nodes rather than a centralized server, and create a token-based economy that encourages data sharing and AI development. Tokens can be used to compensate individuals and organizations who contribute data or algorithms to the blockchain or who employ AI models stored on the blockchain. Overall, the combination of XAI and blockchain can lead to more trustworthy, transparent, and decentralized AI systems. This approach can have a significant impact on various industries such as finance, healthcare, and supply chain management by increasing efficiency, reducing costs, and improving data privacy and security. 2024 Elsevier Inc. All rights reserved. -
Security and Privacy in AI: IoT- Enabled Banking and Finance Services
The integration of Artificial Intelligence (AI) and the IoThas led to significant advancements in the banking and finance sector, providing personalized, efficient, and data- driven services. However, these AI- IoT enabled systems also introduce complex security and privacy challenges that need to be addressed to protect sensitive financial data and maintain customer trust. This paper surveys the key security and privacy issues in AI- IoT enabled banking, including data breaches, cyber- attacks, unauthorized access, and data misuse. We examine current methodologies for securing AI- IoT systems, such as encryption, blockchain, alongside AI- driven threat detection and response techniques.The survey explores regulatory considerations and compliance requirements that shape security protocols in financial services. By identifying gaps in existing security measures and highlighting advanced privacypreserving technologies, this study aims to provide a comprehensive understanding of the challenges and future directions in securing AI- IoT applications within banking and finance. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Security and Privacy in Biometric Authentication: Advancements and Risks Across Platforms
Biometric identification systems like fingerprint, face, iris, and voice recognition have revolutionized user authentication with more convenience and increased security over conventional password based authentication. Nonetheless, the fact that biometric features are immutable introduces unprecedented issues around privacy, spoofing, and database compromise because once biometric data leaks out, it cannot be withdrawn or substituted. This work advances empirical testing with analytical analysis to test biometric authentication for face, fingerprint, and iris modalities. Models based on convolutional neural networks (CNNs) were used and evaluated on benchmarked datasets, and findings indicated that face and iris recognition performed nearly perfectly with zero false acceptance and rejection rates in controlled environments, while fingerprint recognition performed poorly because of dataset size and quality constraints. The conclusions identify the significance of data preparation and variation in ascertaining the reliability of biometric information. In an effort to cross-verify the experiments further, case studies of Aadhaar, Apple Face ID, and Biostar 2 also clarified the threats of central storage, spoofing, and regulatory loopholes. The research concludes by suggesting privacy preserving frameworks, encryption, and multimodal methods for securing the future of biometric authentication. 2025 IEEE. -
Security and Privacy in Internet of Things (IoT) Environments
Although the proliferation of IoT devices has led to unparalleled ease of use and accessibility, it has also raised serious privacy and safety issues. Using a systematic approach that incorporates security and privacy modelling, data analysis, and empirical trials, this study provides a deep dive into the topic of IoT security and privacy. Our results show how crucial it is to take precautions against 'Information Disclosure' by using strong encryption and authorization protocols. The need to protect against 'Unencrypted Data' vulnerabilities is further emphasized by vulnerability analysis. Encryption (AES-256) and other access control rules fare very well in the assessment of security systems. Furthermore, 'Homomorphic Encryption' is identified as a potential strategy to protecting user privacy while retaining data usefulness based on our review of privacy preservation strategies. A more secure and privacyaware IoT environment may be fostered thanks to the findings of this study, which have ramifications for the industry, government, consumers, and academics. Addressing the ever-evolving security and privacy issues in the IoT will need a future focus on cutting-edge security mechanisms, privacy-preserving technology, regulatory compliance, user-centric design, multidisciplinary cooperation, and threat intelligence sharing. 2024 IEEE. -
Security and privacy issues in existing biometric systems and solutions
[No abstract available]