Security Tools Research Articles

Enhancing DevSecOps practice with Large Language Models and Security Chaos Engineering

Recently, the DevSecOps practice has improved companies’ agile production of secure software, reducing problems and improving return on investment. However, overreliance on security tools and traditional security techniques can facilitate the implementation of vulnerabilities in different stages of the software lifecycle.. Thus, this paper proposes the integration of a Large Language Model to help automate threat discovery at the design stage and Security Chaos Engineering to support the identification of security flaws that may be undetected by security tools. A specific use case is described to demonstrate how our proposal can be applied to a retail company that has the business need to produce rapidly secure software.

AIEE-Active Green Fluorescence Imaging Technique for Latent Fingerprint Application.

Latent fingerprint imaging is a crucial tool for national security and crime recognition, requiring environmentally sustainable, nontoxic materials. To address this need, we have developed a green fluorescence-emitting material, THCHO, that binds effectively to latent fingerprints, enabling clear, high-resolution visualization. The solution produces strong green fluorescence that highlights fingerprint imprints with exceptional detail and contrast. The material is applicable across various substrates, making it efficient for on-site visualization. The synthesized organic conjugated material THCHO rapidly visualizes precise fingerprint substructures within 5 s using a simple spray method. It maintains high contrast and low interference across different substrates, such as glass, aluminum foil, plastic, and ironware, even in diverse backgrounds. Additionally, the THCHO material demonstrates high stability, retaining its fluorescence imaging capabilities for up to 35 days and after water washing. This is the first report of using a conjugated organic thiophene material for latent fingerprint fluorescence imaging, suggesting potential applications in other fields.

Mental Model-Based Designs: The Study in Privacy Policy Landscape

Users’ mental models influence secure and privacy-preserving behavior in a computing environment. Prior studies on users’ mental models of Internet, security tools, and digital privacy show that there is no one-size-fits-all solution when it comes to security and privacy design. However, little study to date has explored the ways to translate users’ mental models into interactive security and privacy designs. As we begin to address this gap, we focus on privacy policy in this paper. The typical text-based privacy policy suffers from poor readability and usability. A recent study proposed a Visual Interactive Privacy Policy (VIPP), showing promise to offer a better user experience as compared to prior designs – we used VIPP as a control condition and compared that with our mental model (MM)-based designs, inspired by users’ privacy mental models explored in the existing literature. We iteratively improved our MM-based designs through a series of user studies in the lab setting. We evaluated our updated designs in an online study with 182 participants over Amazon Mechanical Turk. The participants rated MM-based designs significantly better than the control in most of our evaluation parameters. Furthermore, we found that when a design is centered around the mental model of participants, study participants rated it higher in terms of personal connection to the design, perspicuity, attractiveness, being stimulated towards privacy protection, as well as the propensity for real-life adoption. Based on our findings, we discussed the successes and challenges of MM-based designs and provided guidelines on the scope of leveraging mental models in the broader area of privacy and security designs.

К вопросу реализации алгоритмов проектирования защищенных сетей передачи данных

Existing approaches to the design of secure data transmission networks (DRN) are often based on guidelines for the design of communication networks in a broad sense, at the moment there are no clear regulations for the design of communication networks in a secure version from the point of view of information security. In the process of advanced design of the SPDS, problems arise in finding effective solutions for the construction of such communication networks and ensuring the information security of the SPDS in terms of information security tools (SIS) in given areas determined by information security measurements, such as the availability, accessibility, secrecy of information circulating in the SPDS. The article proposes algorithms based on the parameters of non-stationary hyper-network models, which make it possible to implement the analysis and synthesis of ZSPD, as well as take into account the initial data on the intended users, services/applications implemented on ZSPD under conditions of external destructive effects (VDV).

A REVIEW PAPER ON ETHICAL HACKING

Ethical Hacking is a proactive approach to cybersecurity that involves authorizing and systematically testing information systems to find vulnerabilities before they can be exploited by criminals. Ethical hackers are network and computer experts who attack security systems on behalf of their owners, looking for vulnerabilities that can be exploited by malicious hackers. The explosive growth of the Internet has given rise to many good things, including electronic commerce, email, collaborative computing, and new areas of advertising and information distribution. Ethical hacking, also known as penetration testing, or red teaming, has become a major concern for businesses and governments. Organizations are concerned about the possibility of being "hacked" and potential customers are concerned about having their personal information under control. Hackers are classified according to their work and knowledge. White hat hackers are ethical hackers. Ethical hackers use hacking techniques to ensure security. Ethical hacking is necessary to protect systems from damage. KEYWORDS: Cybersecurity, Ethical Hacking, It’s Impact, Vulnerability Assessment, Security Tool, Cyberthreats

Open Soar

SOAR solutions are designed to integrate multiple security components, often from different vendors. A stack of compatible security tools enables companies to collect data about attacks and respond to them without human intervention. SOAR tool’s primary target is to increase the effectiveness of security operations. SOAR tools’ main mechanisms are security orchestration, automation, and response. To build and automate numerous security tools to function as Security orchestration, automation, and response (SOAR) that will help small and medium-sized organizations automate security processes, particularly incident response, by collecting threat data from multiple sources. It can also respond to low-level incidents without human intervention. The user experience is paramount. Both end users and administrators are best served by SOAR that enables maximum productivity.

Performance Analysis of symmetric and asymmetric Encryption Algorithms Based on File, Image and Video

The rapid evolution of digital technology has exponentially amplified the generation and sharing of diverse digital data, notably files, images, and videos, over the internet, intensifying the critical need for enhanced security measures to safeguard these prevalent data types. This research presents a comprehensive analysis of various encryption algorithms applied to different data types - files, images, and videos. The study categorizes encryption algorithms into symmetric and asymmetric types, with examples including AES, DES, Triple DES, RSA, Diffie-Hellman, and ECC. The paper further explores specific algorithms used for file, image, and video encryption. The objective is to identify the most efficient encryption algorithm for each data type, thereby enhancing data security in the digital age. The paper emphasizes that while encryption is a crucial tool for data security, it should be used in conjunction with other security measures for comprehensive protection.

FPGA based implementation of a perturbed Chen oscillator for secure embedded cryptosystems

This paper introduces an enhancement to the Chen chaotic system by incorporating a constant perturbation term d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d$$\\end{document} to one of the state variables, aiming to improve the performance of pseudo-random number generators (PRNGs). The perturbation significantly enhances the system's chaotic properties, resulting in superior randomness and increased security. An FPGA-based realization of a perturbed Chen oscillator (PCO)-derived PRNG is presented, tailored for embedded cryptosystems and implemented on a Nexys 4 FPGA card featuring the XILINX Artix-7 XC7A100T-1CSG324C integrated chip. The Xilinx-based system generator (XSG) tool is utilized to generate a digital version of the new oscillator, minimizing resource utilization. Experimental results demonstrate that the PCO-generated data successfully passes the NIST and TestU01 test suites. Additionally, statistical tests with key sensitivity are performed, validating the suitability of the designed PRNG for cryptographic applications. This establishes the PCO as a straightforward and efficient tool for multimedia security.

Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.

Ensuring Security and Compliance in Agile Cloud Infrastructure Projects

Purpose: This research paper investigates strategies for ensuring security and compliance in agile cloud infrastructure projects. Methodology: The study synthesizes current literature, industry reports, and expert insights to provide a comprehensive overview of the topic. Findings: Key challenges identified include rapid deployment cycles, shared responsibility models, and data sovereignty concerns. The research proposes strategies such as shift-left security approaches, continuous compliance monitoring, and automated security testing. The importance of organizational culture shifts and the evolving role of cloud service providers in shared security responsibilities are highlighted. Unique Contribution to Theory, Policy and Practice: The paper offers recommendations for practitioners navigating the complex landscape of security and compliance in agile cloud projects. It provides insights into integrating security and compliance into agile methodologies, leveraging cloud-native security tools, and the potential impact of AI and machine learning on cloud security.

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

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

Factors Influencing Information Security Policy Compliance Behavior “A Case – Study Healthcare Workers in a Private Hospital in Mogadishu – Somalia”

Information security policy compliance is crucial for safeguarding sensitive data in healthcare settings. This study investigates the factors influencing information security policy compliance behavior among healthcare workers at a private hospital in Mogadishu, Somalia. Findings reveal that beliefs in the importance of information security, effectiveness of communication and training programs, usability of security tools, and socio- cultural factors significantly impact compliance behavior. The study provides insights for healthcare administrators and policymakers to enhance compliance strategies in healthcare organizations.

Sudoku Solver – A Novel Approach Using Recognition of Printed Text Image

Detecting and analyzing text in multimedia is required for recognition of characters which can be used for performing multi-objectives in numerous research areas and applications. Characters or numbers in images can be hand-written, printed document, typewritten or artificially added for the purpose of searching or indexing, editing or image understanding. Various applications such as License Plate Detection, Handwritten documents recognition, Sudoku Solving, multi-oriented recognition focuses on character recognition. Image Sudoku solver works on Sudoku puzzle images. In this paper, the printed text Sudoku image is used as the input. The image is enhanced by filling the horizontal and vertical gaps in the grids that may occur due to shadow or distortion of the scanned image such that all the 81 grids are extracted. Secondly, the numbers and grids in the enhanced image are extracted and a dancing link approach using Algorithm X is adopted to solve the Sudoku. The proposed work is compared with a backtracking approach and dancing link approach without enhancing the image and its computation time for various ranks of difficulty of a puzzle taken. The Sudoku puzzle can be used to secure information stored in an image as the number of solutions to solve a Sudoku puzzle is very large, allowing it to be used as a security tool resistant to brute force attacks.

BYOD security behaviour and preferences among hospital clinicians – A qualitative study

Background/ObjectiveThe use of personal devices for work purposes (Bring-your-own-device) has increased in hospitals, as it facilitates productivity and mobility for clinicians. However, owing to increased risk of leaking patient information, and heavy reliance of patient data privacy on user actions, BYOD is a major challenge for hospitals. There has been a dearth of empirical research studying clinicians’ BYOD security behaviour. Therefore, the study’s aim was to attain subjective understanding of clinicians’ attitudes and preferences towards protecting patient data on their devices through a qualitative study. Methods14 semi-structured interviews were conducted among Australian hospital-based clinicians. A hybrid thematic analysis was conducted using the framework method to explore socio-technical themes pertaining to the clinicians’ BYOD security behavioural practices. ResultsLimited use of secure tools like antivirus and passcodes, and inadequate separation of patient and personal data on BYOD devices was found. Key technology concerns included malware introduction into hospital network, inadvertent patient data sharing, and slow remote access. Hospitals lacked dedicated BYOD policies and training, resulting in unsafe practices. Participants also cited misalignment of BYOD policies with workflow needs, privacy maintenance challenges and fears of personal data breaches, while calling for improved communication between technical and clinical staff and a strong cybersecurity culture. ConclusionThis study provides a comprehensive understanding of BYOD related user behaviour and the usefulness of security controls used in time-sensitive and complex hospital environments. It can inform future policies or processes by advocating for secure and productive BYOD use.

CYBERSECURITY ON IOT SYSTEMS BASED MULTI-LAYERED SECURITY RISK ASSESSMENT

IoT systems contribute to digital transformation through the development of smart concepts. However, the IoT has also generated new security challenges that require security tools to be adapted, such as risk analysis methodologies. Many organizations use a variety of methods and frameworks to assess cybersecurity risks. However, most current risk assessment methods are for generic software systems, so there is no holistic approach to risk assessment for IoT technologies, especially due to the diversity of IoT systems The purpose of our study is based on the following question: What IoT device factors should be considered in cybersecurity risk assessment methods? In this study we first propose a cybersecurity risk assessment model based on vectors such as vulnerabilities and attacks, which evaluates the security risk from different dimensions of physical layer, network layer and application layer. Secondly, we design the mathematical assessment model for computing risk value of IoT system and then establish the mapping relationship table that the risk value is transformed into risk level.

On the acceptance by code reviewers of candidate security patches suggested by Automated Program Repair tools

ObjectiveWe investigated whether (possibly wrong) security patches suggested by Automated Program Repairs (APR) for real world projects are recognized by human reviewers. We also investigated whether knowing that a patch was produced by an allegedly specialized tool does change the decision of human reviewers.MethodWe perform an experiment with n=72\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n= 72$$\\end{document} Master students in Computer Science. In the first phase, using a balanced design, we propose to human reviewers a combination of patches proposed by APR tools for different vulnerabilities and ask reviewers to adopt or reject the proposed patches. In the second phase, we tell participants that some of the proposed patches were generated by security-specialized tools (even if the tool was actually a ‘normal’ APR tool) and measure whether the human reviewers would change their decision to adopt or reject a patch.ResultsIt is easier to identify wrong patches than correct patches, and correct patches are not confused with partially correct patches. Also patches from APR Security tools are adopted more often than patches suggested by generic APR tools but there is not enough evidence to verify if ‘bogus’ security claims are distinguishable from ‘true security’ claims. Finally, the number of switches to the patches suggested by security tool is significantly higher after the security information is revealed irrespective of correctness.LimitationsThe experiment was conducted in an academic setting, and focused on a limited sample of popular APR tools and popular vulnerability types.

Defence against adversarial attacks on IoT detection systems using deep belief network

An Adversarial attack is a technique used to deceive machine learning models to make incorrect predictions by providing slightly modified inputs from the original. Intrusion detection system (IDS) is a crucial tool in computer network security for the detection of adversarial attacks. Deep learning is a trending method in both research and industry, and this study proposes the use of a deep belief network (DBN). DBN can recognize data with small differences, but is also vulnerable to adversarial attacks. Therefore, this research suggests an internet of things-intrusion detection system (IoT-IDS) architecture using a DBN that can counter adversarial attacks. The chosen adversarial attack for this study is the fast gradient sign method (FGSM) used to evaluate the IoT IDS using the DBN model. Testing was conducted in two scenarios: first, the model was trained without adversarial attacks; second, the model was trained with adversarial attacks. The test results indicate that the DBN model struggles to detect FGSM attacks, achieving an accuracy of only 46% when it is not trained with adversarial attacks. However, after training with the FGSM dataset, the DBN model successfully detected adversarial attacks with an accuracy of 97%.

Facial recognition - A literature review

This paper analyses the main technologies for face recognition, a critical biometric tool for identity verification and security across various sectors. A comprehensive overview of traditional and modern facial recognition technologies will be provided, examining their key features such as age, pose, and illumination. The study discusses the evolution and current state of facial recognition, highlighting significant advancements and applications in recent years. The objective is to offer a detailed understanding of how these technologies function and their implications for security and identity verification.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Assessing the Effectiveness of Network Security Tools in Mitigating the Impact of Deepfakes AI on Public Trust in Media

The rising threat of deepfake technology challenges public trust in media, necessitating robust countermeasures. This study proposes the Anti-DFK framework, a comprehensive strategy to mitigate the spread of deepfakes on major social platforms such as Instagram, Facebook, YouTube, and Twitter. The framework integrates deep learning-based detection engines, digital watermarking, and advanced network access controls, including URL filtering, domain reputation filtering, content-type filtering, and Geo-IP blocking. Analyzing historical deepfake data, user engagement metrics, and public sentiment from Kaggle Datasets, the study employed deep learning models—CNNs, LSTMs, and Transformer-based—to evaluate detection capabilities, achieving the highest controlled environment accuracy of 0.97. Digital watermarking techniques were tested for robustness against various attacks, with the DCT method displaying significant resilience. Network access controls were assessed for their effectiveness in curtailing the spread of deepfakes, with content filtering proving the most effective by reducing dissemination by nearly 80%. Findings indicate a critical negative impact of deepfakes on public trust, underscoring the need for the integrated approach offered by the Anti-DFK framework. The study concludes that implementing these sophisticated detection tools, combined with robust digital watermarking and stringent network controls, can significantly enhance the integrity of media content and restore public confidence.