Mitigate Security Risks Research Articles

Anomaly Detection in Blockchain Using Machine Learning

Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques. The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.

SECURITY PARADIGMS FOR IOT IN TELECOM NETWORKS: CONCEPTUAL CHALLENGES AND SOLUTION PATHWAYS

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: Security, IoT, Telecom, Solutions, Network.

A TECHNIQUE FOR PROVIDING SECURED UNINTERRUPTED SERVICES OF A DATA CENTER

This paper presents a comprehensive technique for ensuring the secured, uninterrupted operation of data centers, addressing the dual challenges of resilience against disruptions and robust security against cyber-physical threats. By integrating advanced cryptographic methods, dynamic access control, AI-based anomaly detection, and sustainable power solutions, the proposed approach offers a novel framework for enhancing data center reliability and security. The study synthesizes multidisciplinary research, including cybersecurity, power engineering, and environmental sustainability, to construct a resilient infrastructure capable of supporting the critical functions dependent on data centers. The effectiveness of the technique is evaluated through a series of simulated scenarios, demonstrating its potential to significantly reduce downtime and mitigate security risks. This research contributes to the field by proposing a holistic strategy that not only secures data centers from emerging threats but also ensures their continuous operation, thereby supporting the digital ecosystem's growth and reliability.

Fortifying Software Security: Strategies, Implementation, and Challenges

Abstract: In an increasingly interconnected world, where software applications underpin critical infrastructure, financial systems, and everyday interactions, ensuring robust security measures throughout the software development lifecycle has become paramount. This research paper conducts a comprehensive investigation into the multifaceted landscape of software security, examining key strategies, implementation techniques, and challenges encountered by developers. Drawing from an extensive review of literature, case studies, and emerging trends, this paper provides nuanced insights into effective approaches for mitigating security risks and fostering a culture of security awareness in software development endeavors

A Detection for University Using Face Recognition

The implementation of face recognition technology in university environments has emerged as a pivotalsolution for bolstering security measures and ensuring the safety of students, faculty, and staff. This project focuses ondeveloping a robust face recognition system integrated into an admin panel tailored specifically for security purposeswithin a university setting. By leveraging advanced biometric factors, such as facial recognition, the system aims toenhance surveillance capabilities, mitigate security risks, and streamline access control processes. Through the creationof a comprehensive database of known individuals and proactive identification of unknown persons, the system offersreal-time monitoring and alerts to administrators, thereby fortifying security protocols within the university campus.

A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive Systems

Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modelled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrates the applicability and potency in mitigating security risks.

AI-powered biometrics for Internet of Things security: A review and future vision

Biometrics is a set of advanced technologies that use the physical or behavioral characteristics of individuals to provide reliable access control. With the rapid development in information and communication technology, biometrics has become an increasingly important field, especially with the integration of artificial intelligence (AI). Fingerprint- and facial-recognition technologies have been significantly improved by the use of AI. The widespread use of the Internet of Things (IoT) has led to security challenges and created new opportunities for biometrics to be used in various applications. This review provides a comprehensive analysis of intelligent or AI-powered biometrics, focusing on the integration of AI and biometrics for building security measures to improve IoT security. Due to their enhanced security and usability, biometric-based authentication methods are becoming increasingly popular in IoT environments. AI algorithms are essential for improving biometric systems in IoT applications by enabling advanced pattern recognition and adaptive decision-making. Furthermore, the integration of biometrics with AI and the IoT can help mitigate security risks, ensuring the protection of data and user privacy. This review makes three main contributions: it provides a comprehensive analysis of the interdependencies between the AI, biometrics, and IoT domains; it covers the applications of AI and biometrics in the context of the IoT; and it highlights the current challenges and future research directions for the deployment of intelligent biometrics in various IoT application domains. Furthermore, this review facilitates further research in the area of the application of intelligent biometrics in IoT applications.

Unlocking the Potential of AI/ML in DevSecOps: Effective Strategies and Optimal Practices

In the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering security, efficiency, and innovation in software development and deployment processes. This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework. Commencing with an overview of DevSecOps principles and the integral role of AI/ML, the document delves into specific tactics such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Additionally, it addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through illuminating case studies and real-world illustrations, the document showcases how organizations can leverage AI/ML technologies to streamline their DevSecOps pipelines, mitigate security risks, and cultivate a culture of ongoing enhancement. By embracing these strategies and adhering to best practices, organizations can harness the full potential of AI/ML to propel innovation, fortify resilience, and enhance agility in their DevSecOps endeavors.

Unlocking the Potential of AI/ML in DevSecOps: Effective Strategies and Optimal Practices

In the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering security, efficiency, and innovation in software development and deployment processes. This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework. Commencing with an overview of DevSecOps principles and the integral role of AI/ML, the document delves into specific tactics such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Additionally, it addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through illuminating case studies and real-world illustrations, the document showcases how organizations can leverage AI/ML technologies to streamline their DevSecOps pipelines, mitigate security risks, and cultivate a culture of ongoing enhancement. By embracing these strategies and adhering to best practices, organizations can harness the full potential of AI/ML to propel innovation, fortify resilience, and enhance agility in their DevSecOps endeavors.

Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements.

Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model's performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.

Mitigating Security Risks in Firewalls and Web Applications using Vulnerability Assessment and Penetration Testing (VAPT)

Mitigating Security Risks in Firewalls and Web Applications using Vulnerability Assessment and Penetration Testing (VAPT)

Information Security in the Metaverse: A Systematic and Prospective Review

The Metaverse, a constantly evolving three-dimensional virtual space, has captured the attention of users eager for immersive experiences. However, this exciting digital world has also raised significant concerns regarding information security. This study delves into the extensive academic literature to conduct a comprehensive analysis of security tools in the Metaverse and explore future applications in this emerging context. Three key trends emerge from this in-depth analysis. Firstly, authentication and authorization stand out as cornerstones in protecting users and their data in the Metaverse. The increasing adoption of multifactor authentication underscores the importance of safeguarding access to this virtual environment. Additionally, end-to-end encryption is presented as a vital shield against potential data breaches. The second trend focuses on data protection and privacy. The concern for preserving the integrity of personal information drives the development and implementation of data anonymization technologies, marking a significant advancement in ensuring user privacy. The third trend relates to threat detection and response. In response to the growing sophistication of risks in the Metaverse, there is an observed increase in the adoption of artificial intelligence and machine learning technologies. These advanced tools play a crucial role in identifying and proactively mitigating security risks. In summary, this analysis reveals a dynamic landscape of security in the Metaverse, characterized by the constant evolution of tools and strategies to safeguard information. As this virtual environment continues to expand, information security emerges as a fundamental component to ensure immersive and secure digital experiences.

OEC[sbnd]Net: Optimal feature selection-based email classification network using unsupervised learning with deep CNN model

The escalating prevalence of email communication in global interactions underscores the critical need for robust security measures. However, the existing methods to safeguard email communication, including spam, phishing, and ham emails, have exhibited suboptimal classification performance and inadequate security. So, this work introduces the Optimized Email Classification Network (OECNet) to categorize emails and mitigate security risks effectively. Leveraging a hybrid dataset comprising spam, phishing, and ham email information, we perform meticulous preprocessing to ensure data uniformity. Our approach incorporates principal component analysis (PCA) to extract email-specific pattern properties, enhancing feature discernment through eigenvalue analysis. Inspired by natural swarm behaviours, we employ Particle Swarm Optimization (PSO) to select highly correlated features from PCA outcomes, optimizing the model's efficacy. The OECNet uses PSO-optimised features to integrate a Deep Learning Convolutional Neural Network (DLCNN) model for classification. This novel methodology surpasses traditional methods, yielding an accuracy of 98.43 %, precision of 97.78 %, recall of 96.41 %, and an impressive F1-score of 97.07 %. The proposed OECNet advances email classification technology and establishes a robust security framework against malicious intrusions in global communication. The hybrid dataset incorporates UCI, CSDMC, and SpamAssassin information, ensuring comprehensive coverage and realistic scenarios for effective model training and evaluation.

Security Implications of AI Chatbots in Health Care.

Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people's data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care.

Endpoint security reinforcement via integrated zero-trust systems: A collaborative approach

In the rapidly evolving field of information technology, network security, especially enterprise endpoint security, has emerged as a major challenge. This paper presents a comprehensive and interactive method for network access and user authentication utilizing a zero-trust framework. This method integrates key elements, including the Network Access (NA) Agent, Identity and Access Management (IAM) Agent, Policy Enforcement Point (PEP) Agent, and Situational Awareness (SA) Agent, to mitigate security risks associated with critical business information exposure and unauthorized network access. Leveraging a zero-trust approach, the method dynamically controls user permissions, thereby enhancing endpoint security. It also introduces an efficient solution that coexists with legacy infrastructures, balancing security necessities with user accessibility, and offering a unified solution for both internal corporate networks and the Internet. We present a thorough analysis of potential risks associated with this method and propose preventative measures to minimize these threats. We conclude that our method provides a more secure and efficient approach to enterprise network security compared to traditional static rule-based systems, offering a promising direction for future research and implementation.

Customers Awareness and Knowledge level towards Digital payment of Public and Private Sector Banks at Thoothukudi District

In today's rapidly digitizing financial landscape, understanding the awareness and knowledge levels of customers regarding digital payment methods is paramount. This study aims to assess and compare these levels among various gender categories and age groups, shedding light on potential disparities. Key objectives include promoting financial inclusion, mitigating security risks, and enhancing customer satisfaction. The implications of this research extend to policymakers, financial institutions, and businesses navigating the dynamic world of digital payments, where informed decision-making is essential for success and societal progress.

Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

Early attack detection is essential to ensure the security of complex networks, especially those in critical infrastructures. This is particularly crucial in networks with multi-stage attacks, where multiple nodes are connected to external sources, through which attacks could enter and quickly spread to other network elements. Bayesian attack graphs (BAGs) are powerful models for security risk assessment and mitigation in complex networks, which provide the probabilistic model of attackers’ behavior and attack progression in the network. Most attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring, which is unrealistic given the ever-growing complexity of threats. This paper derives the optimal minimum mean square error (MMSE) attack detection and monitoring policy for the most general form of BAGs. By exploiting the structure of BAGs and their partial and imperfect monitoring capacity, the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman filtering. An adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined value. Exact and efficient matrix-form computations of the proposed policies are provided, and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

A Lightweight True Random Number Generator for Root of Trust Applications

There are many schemes proposed to protect integrated circuits (ICs) against an unauthorized access and usage, or at least to mitigate security risks. They lay foundations for hardware roots of trust whose crucial security primitives are generators of truly random numbers. In particular, such generators are used to yield one-time challenges (nonces) supporting the IC authentication protocols employed to counteract potential threats such as untrusted users accessing ICs. However, IC vendors raise several concerns regarding the complexity of these solutions, both in terms of area overhead, the impact on the design flow, and testability. These concerns have motivated this work presenting a simple, yet effective, all-digital lightweight and self-testable random number generator to produce a nonce. It builds on a generic ring generator architecture, i.e., an area and time optimized version of a linear feedback shift register, driven by a multiple-output ring oscillator. A comprehensive evaluation, based on three statistical test suits from NIST and BSI, show feasibility and efficiency of the proposed scheme and are reported herein.

Unlocking the Power of AI/ML in DevSecOps: Strategies and Best Practices

In today's rapidly evolving technological landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps practices has emerged as a critical enabler for enhancing security, efficiency, and innovation in software development and deployment processes. This paper explores the strategies and best practices for harnessing the full potential of AI/ML within the DevSecOps framework. Beginning with an overview of DevSecOps principles and the role of AI/ML, the paper delves into specific strategies such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Furthermore, it examines key challenges and considerations associated with implementing AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through case studies and real-world examples, the paper illustrates how organizations can leverage AI/ML technologies to optimize their DevSecOps pipelines, mitigate security risks, and foster a culture of continuous improvement. By embracing these strategies and best practices, organizations can unlock the full potential of AI/ML to drive innovation, resilience, and agility in their DevSecOps initiatives.

Subseasonal prediction framework for tropical cyclone activity in the Solomon Islands region

AbstractRecently, we developed seasonal prediction schemes with improved skill to predict tropical cyclone (TC) activity up to 3 months in advance for the Solomon Islands (SI) region (5°–15°S, 155°–170°E) using sophisticated Bayesian regression techniques. However, TC prediction at subseasonal timescale (i.e., 1–4 weeks in advance) is not being researched for that region despite growing demands from decision makers at sectoral level. In this paper, we first assess the feasibility of developing subseasonal prediction frameworks for the SI region using a pool of predictors that are known to affect TC activity in the region. We then evaluate multiple predictor combinations to develop the most appropriate models using a statistical approach to forecast weekly TC activity up to 4 weeks in advance. Predictors used include indices of various natural climate variability modes, namely the Madden–Julian Oscillation (MJO), the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and the Interdecadal Pacific Oscillation (IPO). These modes often have robust physical and statistical relationships with TC occurrences in the SI region and the broader southwest Pacific territory as shown by preceding studies. Additionally, we incorporate TC seasonality as a potential predictor given the persistence of TCs occurring more in certain months than others. Note that a model with seasonality predictor alone (hereafter called the “climatology” model) forms a baseline for comparisons. The hindcast verifications of the forecasts using leave‐one‐out cross‐validation procedure over the study period 1975–2019 indicate considerable improvements in prediction skill of our logistic regression models over climatology, even up to 4 weeks in advance. This study sets the foundation for introducing subseasonal prediction services, which is a national priority for improved decision making in sectors like agriculture and food security, water, health and disaster risk mitigation in the Solomon Islands.