Mitigate Security Risks Research Articles

Web Vulnerability Scanning Framework

Abstract: The increasing reliance on web-based systems has brought cybersecurity to the forefront of concerns for organizations and individuals alike. In this paper, we present a framework that leverages open-source tools for information gathering (reconnaissance) and vulnerability assessment in web-based systems. The framework is designed to be hosted as a website, providing a user-friendly interface for cybersecurity practitioners to conduct reconnaissance and identify vulnerabilities in their target systems. Through integration of various open-source tools, our framework enables efficient and effective information gathering and vulnerability scanning, aiding in the identification and mitigation of potential security risks. Our framework contributes to the field of cybersecurity by providing a unified solution for reconnaissance and vulnerability assessment in webbased systems. The integration of open-source tools and the flexibility of our framework make it a valuable resource for cybersecurity practitioners.

A Supervised Machine Learning Ransomware Host-Based Detection Framework

Today, the term ransomware is frequently used in cybercrime headlines, its consequences have been on the rise leaving a trail of terrible losses in its wake. Both people and businesses have been victimized by ransomware, costing the victims millions of dollars in ransom payments. In addition, victims who were unable to pay the ransom or decrypt the data experienced data losses. This study uses dynamic malware analysis artifacts and supervised machine learning to detect ransomware at the host level. It takes on a thorough examination of the operational specifics of ransomware and suggests a supervised machine-learning approach to detection using various ransomware features derived from dynamic malware analysis. According to the findings, a Logistic Regression algorithm model with a 97.7% accuracy score offers a 99% success rate in ransomware detection. This demonstrates how well machine learning and dynamic malware analysis work together to detect ransomware activity at the host level. Systems security administrators can mitigate security risks by using this method.

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with燚eep燣earning

Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.

Application of AI and ML in the Field of DevSecOps

Security is a paramount concern in DevOps. The adoption of Infrastructure as Code (IaC) has increased the potential impact of even minor flaws, particularly in critical domains like healthcare and maritime applications. Existing solutions typically focus on either Static Application Security Testing (SAST) or run-time behavior analysis. This paper introduces the IaC Scan Runner, an open-source tool developed in Python for inspecting various IaC languages during application design, and LOMOS, a run-time anomaly detection tool. Both tools work together to enhance the security of DevOps processes. In today’s rapidly evolving technological landscape, the vulnerability of infrastructure and applications is growing due to a combination of factors. Attackers are becoming more sophisticated, leveraging improved intelligence to exploit weaknesses. At the same time, there is a lack of technical capability in many organizations to effectively secure their systems. This paper explores a dual approach to cybersecurity: static security monitoring through rule matching and the application of self-supervised machine learning. By combining these approaches, organizations can better defend against cyber threats. One area of focus is supply chain resilience and smart logistics, where the integration of these methods is particularly critical. This approach emphasizes a self-learning and self-healing approach, allowing systems to adapt and respond to new threats autonomously. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps practices is essential for improving security, efficiency, and innovation in software development and deployment. This paper delves into strategies and best practices for leveraging AI/ML within the DevSecOps framework. It discusses automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. However, this integration also presents challenges, such as data privacy, algorithm transparency, and ethical implications. The paper addresses these challenges and showcases how organizations can use AI/ML to optimize their DevSecOps pipelines, mitigate security risks, and foster continuous improvement. The adoption of Infrastructure as Code (IaC) has increased the potential impact of even minor flaws, especially in critical domains like healthcare and maritime applications. Existing solutions typically focus on either Static Application Security Testing (SAST) or run-time behavior analysis.

Penetration Testing Methodologies for Serverless Cloud Architectures

As organizations increasingly adopt serverless cloud architectures to enhance scalability and reduce operational costs, the security landscape has evolved, introducing new challenges and vulnerabilities. Serverless computing, characterized by its abstraction of infrastructure management and dynamic resource allocation, presents unique security concerns that traditional penetration testing methodologies may not adequately address. This research paper explores penetration testing methodologies specifically tailored for serverless cloud environments, aiming to identify effective strategies for evaluating and mitigating security risks in these modern architectures. The paper begins by defining serverless computing and its key characteristics, such as event-driven execution, automatic scaling, and micro-billing models. Unlike traditional server-based environments, serverless architectures often rely on Functions-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components, which can obscure the underlying infrastructure and introduce complex attack vectors. Consequently, traditional penetration testing approaches, designed for monolithic or microservices-based systems, may fall short in identifying and exploiting vulnerabilities specific to serverless environments.

Implement strategies to mitigate security risks

LAS VEGAS — From venue lighting, ticket scanners, and signage to websites, cloud services, social media accounts, and wearable devices, technology has become an increasingly critical component of college athletics operations. And in the push and rush to keep up with the latest technology, the security aspect often takes a backseat to speed. But when it comes to cybersecurity, athletics departments really can't afford to cut corners, according to Erik Anderson, Assistant Director of Technical Operations, and Cass Ferguson, Associate Athletic Director for Facilities and Events, both with the University of North Carolina Charlotte Athletics Department. They spoke at the annual convention for the Collegiate Event and Facility Management Association.

Facilitating Cyber Security Threat Modelling: A Social Capital Perspective

To identify and manage their cyber security risks, organisations need to form a thorough understanding of various factors that may expose them to these risks. While cyber security professionals and scholars have developed a plethora of practical methodologies and frameworks to support cyber security risk identification and mitigation, the theoretical foundations on what promotes effective knowledge creation when using these methodologies and frameworks are nascent. Yet, theories developed in the field of knowledge management and intellectual capital may provide valuable insight on how to enhance cyber security risk related knowledge creation in organisations. For example, social capital is considered as an important prerequisite for knowledge exchange and combination when creating new intellectual capital (Nahapiet & Ghoshal, 1998). However, more focused research is required to understand how social capital affects knowledge creation in the context of organisational cyber security risk related activities.
 
 Using qualitative data gathered from three cyber security threat modelling workshops, this paper examines how social capital enables conditions for exchanging and combining knowledge on cyber security threats. By comparing the empirical observations with Nahapiet and Ghoshal’s (1998) model, this study identifies practical approaches that are used by threat modelling workshop facilitators to create conditions for effective knowledge exchange and combination. This study provides both cyber security scholars and professionals with an example on how to use knowledge creation related academic theories to analyse and further enhance cyber security risk management approaches by creating a connection between Nahapiet and Ghoshal’s (1998) social capital model and cyber security threat modelling.

Cyber security of railway cyber-physical system (CPS) – A risk management methodology

Along with the increasing application of different cyber-physical systems (CPSs) to connect various components in the rail industry, rising connectivity through communication technologies has also introduced cyber threats against rail-CPSs, causing failures with huge consequences. Implementations of rail-CPSs demand proactive identification, clear-cut definition, and proper handling of their cyber security risks. In this paper, we formulate a risk management methodology for cyber security in rail-CPSs and conduct a retrospective case study on the Advanced Train Control System (ATCS) that has been deployed in many U.S. freight railways. The methodology provides two alternative approaches to fill knowledge gaps in contingency preparation, threat prevention, consequence analysis, and security risk mitigation. In the case study, we demonstrate two cyber threats of ATCS, using attack sequence modeling and consequence analysis, and provide recommendations for risk mitigation. By practicing the methodology with the case study, this work can be used as a general reference to conduct cyber risk management and cyber-robustness evaluations for other existing systems.

Fine-Grained Identification for Large-Scale IoT Devices: A Smart Probe-Scheduling Approach Based on Information Feedback

A large number of IoT devices access the Internet. While enriching our lives, IoT devices bring potential security risks. Device identification is one effective way to mitigate security risks and manage IoT assets. Typical identification algorithms generally separate data capture and target identification into two parts. As a result, it is inefficient and coarse-grained to evaluate the results only once the identification process is complete and then adjust the data capture strategy afterward. To solve this problem, we propose a fine-grained probe-scheduling approach based on information feedback. First, we model the probe surface as three layers for IoT devices and define their relationships. Then, we improve the policy gradient algorithm to optimize the probe policy and generate the optimal probe sequence for the target device. We implement a prototype system and evaluate it on 53,000 IoT devices across various categories to show its wide applicability. The results indicate that our approach can achieve success rates of 96.89%, 93.43%, and 83.71% for device brand, model, and firmware version, respectively, and reduce the identification time by 55.96%.

The integration of renewable energy sources in the BRICS countries: Development of assessment methods

Subject. Forecasting risks in the energy sector. Objectives. Analysis of tools for assessing the functioning of national energy complexes. Methods. Methods of comparative analysis are applied. Results. A system of indicators for assessing the level of use of renewable energy sources both in the national economy and within the framework of a large economic union is proposed. Conclusions. The distribution, absorption and mitigation of energy security risks requires the creation of special additional infrastructure. An objective assessment of the investment component and localization of the production base are needed.

Leveraging UAV-assisted communications to improve secrecy for URLLC in 6G systems

Unmanned Aerial Vehicles (UAVs) will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication (URLLC) in futuristic Sixth-Generation (6G) networks. However, several security vulnerabilities and attacks have plagued previous generations of communication systems; thus, physical layer security, especially against eavesdroppers, is vital, especially for upcoming 6G networks. In this regard, UAVs have appeared as a winning candidate to mitigate security risks. In this paper, we leverage UAVs to propose two methods. The first method utilizes a UAV as Decode-and-Forward (DF) relay, whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices. Moreover, we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception, i.e. increasing secrecy rate, and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay. Additionally, simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height, blocklength, decoding error probability and transmitter-receiver separation distance. Lastly, we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

On-Farm Data Security: Practical Recommendations for Securing Farm Data

The growth in the use of Information and Communications Technology (ICT) and Artificial intelligence (AI) has improved the productivity and efficiency of modern agriculture, which is commonly referred to as precision farming. Precision farming solutions are dependent on collecting a large amount of data from farms. Despite the many advantages of precision farming, security threats are a major challenge that is continuously on the rise and can harm various stakeholders in the agricultural system. These security issues may result in security breaches that could lead to unauthorized access to farmers' confidential data, identity theft, reputation loss, financial loss, or disruption to the food supply chain. Security breaches can occur because of an intentional or unintentional actions or incidents. Research suggests that humans play a key role in causing security breaches due to errors or system vulnerabilities. Farming is no different from other sectors. There is a growing need to protect data and IT assets on farms by raising awareness, promoting security best practices and standards, and embedding security practices into the systems. This paper provides recommendations for farmers on how they can mitigate potential security threats in precision farming. These recommendations are categorized into human-centric solutions, technology-based solutions, and physical aspect solutions. The paper also provides recommendations for Agriculture Technology Providers (ATPs) on best practices that can mitigate security risks.

Analyzing System Security Architecture in Concept Phase Using UAF Domains

Analyzing System Security Architecture in Concept Phase Using UAF Domains

Development of an online assessment system to evaluate knowledge on chemical safety and security

Abstract Education and information dissemination are fundamental to safety and security risk management and mitigation. A web-based examination system called OPCW eQChemSS was developed to assess individuals on their knowledge in chemical safety and security. This can be used as an evaluation tool for chemical safety and security courses, workshops, and seminars. A database of questions was included in this web-based software, which was divided into three categories. Category A is general chemistry level safety, Category B is chemical safety related to organic and inorganic compounds, and Category C is on chemical safety and security and the Chemical Weapons Convention (CWC). The system was designed to automatically check the examination and instantly provide the result of the test in terms of percentage correct answers. A feedback mechanism from the examinees was also included to assess the effectiveness of this e-learning educational material. The importance of e-learning materials in this time of COVID-19 pandemic cannot be more emphasized. The need for readily accessible e-sources that will aid in virtual learning for various topics will be significant even in the coming new normal of hybrid or flexible learning where both online and face-to-face learning may be implemented.

Impact of Security Risks Mitigation on the Adoption of E-Government in Kenya

Inadequate security risks mitigation is one of the major concerns that is related with the adoption of e-Government in Kenya. Therefore, the purpose of this research was to determine the impact of security risk and how it influences behaviour and intention for using e- government services. The study targeted a population consisting of 612 Bungoma County employees. Specifically, ICT Officers, procurement officers, revenue officers, Chief Officers, finance officers and suppliers. A survey research design that provided a quantitative or numeric description of trends, attitudes, or opinions of a population was used. Purposive and random sampling were used and data was collected through content analysis, questionnaires, questioning protocol, and interview and think-aloud protocol. Face validity and content validity was done to ascertain the validity of the research instruments. Data were analysed using both statistical and narrative analysis, validity and reliability and the generalizability and transferability of data collected. The study found that the county government was committed to ensuring quality performance metrics through software development and safe business metrics. The study assessed the effect of security risk mitigation on adoption of E-government in Bungoma County through Availability, Disclosure of information and Confidentiality of the E-government users. The study concluded that County governments trusted their employees when disclosing information to the public. The study found that there was a significant contribution of security risk mitigation measures to the adoption of E-government. The study will benefit the following organizations, County Governments, National governments and also researchers for their further research in the areas of security risks in E-government system.

Elucidating the impact of goethite-modified biochar on arsenic mobility, bioaccumulation in paddy rice (Oryza sativa L.) along with soil enzyme activities

Contamination of paddy soils with arsenic (As) poses imminent threat to the environment and public health. This research work explored the effect of goethite-modified biochar (GMBC) on As immobilization in paddy soil and subsequent accumulation in rice grains. The results showed that the soil supplementation with GMBC significantly improved the biomass of rice plants. In addition, the GMBC application effectively decreased the As content in rice grains (0.72–0.16 mg kg−1). Compared with the control, GMBC 1.5% treatment augmented the iron plaque (Fe-plaque) buildup on rice roots and efficiently sequestered the As by 174%, and reduced its uptake in rice tissues. Soil supplementation with GMBC 1.5% greatly enhanced the activities of soil peroxidase (POD) and catalase (CAT) by 90% and 40%, respectively, compared to the control. Moreover, GMBC amendments improved the relative abundance of the soil bacterial communities and minimized the As mobility in the soil. GMBC 1.5% significantly enhanced the abundance of acidobacteria and Firmicutes by 211% and 95% while that of Chloroflexi decreased by 25%, respectively. The findings of the present investigation demonstrated that GMBC could be used as an environment-friendly approach to remediate As polluted paddy soils and minimize its accumulation in rice grains for mitigation of food security risks and protect public health.

Mitigating Security Risks and Potential Threats of Emerging Rendezvous and Proximity Operations

ABSTRACT Space actors will increasingly conduct rendezvous and proximity operations in space as the associated technologies advance and diffuse. As this occurs, multiple-use of such capabilities will present inherent security risks and potential threats to existing peaceful space activities. The United States must develop and promote a comprehensive operational and dissuasive construct in advance to mitigate these security risks and threats to stability in space. The inherent multiple-use attributes of rendezvous and proximity operations capabilities mean that the regulatory efforts must focus on regulating behavior and registration practices. In addition, improving space situational awareness alone insufficiently addresses the challenges of verifying compliance with the regulatory framework. There must be thoughtful approaches to expanding the space situational awareness network to partners, declassifying data to increase transparency, and improving communication practices to share data and resolve ambiguity. In addition, a space assurance strategy that includes credible deterrence options to deter those who would seek to undermine the regulatory framework is important.

Using open source software securely

These days, open source software provides many of the building blocks for most new applications, and with good reason: it empowers developers and organisations to innovate more flexibly and build applications faster and more efficiently. However, its powerful benefits need to be understood in the context of mitigating security risks and, in particular, preventing vulnerabilities that might arise during the development process.

An Efficient Approach of Security Risk Mitigation in Cloud

An Efficient Approach of Security Risk Mitigation in Cloud

Executing Effective Social Engineering Penetration Tests: A Qualitative Analysis

Penetration testing is an increasingly common strategy adopted by organizations to mitigate security risks including those posed by social engineering—the deception of individuals for the purposes of circumventing information security measures. Drawing from 54 interviews with security auditors, IT professionals, and social engineers, this study explores participant descriptions of the (1) importance of social engineering penetration tests, (2) measurement of assessment outcomes, (3) use of penetration tests as part of security awareness programs, and (4) attitude social engineers should adopt in working with client organizations and their employees. Implications for security research and penetration testing are considered.