Security Experts Research Articles

A Case Study: An Android Security

Software security has made great progress; code analysis tools perform extensive checks for code defects, it is useful to have a basic understanding of the different warnings and emphasize the bug. However, this is beyond the state of the art for many types of application security flaws. Thus, such tools frequently serve for an analyst to help them zero in on security relevant portions of code so they can find flaws more efficiently, rather than a tool that simply finds flaws automatically. In cooperation with a security expert, we carried out a case study with the mobile phone platform Android, and employed the reverse engineering tool-suite Bauhaus for this security assessment. During the investigation we found some inconsistencies in the implementation of the Android security concepts. Based on the lessons learned from the case study, we propose several research topics in the area of reverse engineering that would support a security analyst during security assessments.

FCG-MFD: Benchmark function call graph-based dataset for malware family detection

Cyber crimes related to malware families are on the rise. This growth persists despite the prevalence of various antivirus software and approaches for malware detection and classification. Security experts have implemented Machine Learning (ML) techniques to identify these cyber-crimes. However, these approaches demand updated malware datasets for continuous improvements amid the evolving sophistication of malware strains. Thus, we present the FCG-MFD, a benchmark dataset with extensive Function Call Graphs (FCG) for malware family detection. This dataset guarantees resistance against emerging malware families by enabling security systems. Our dataset has two sub-datasets (FCG & Metadata) (1,00,000 samples) from VirusSamples, Virusshare, VirusSign, theZoo, Vx-underground, and MalwareBazaar curated using FCGs and metadata to optimize the efficacy of ML algorithms. We suggest a new malware analysis technique using FCGs and graph embedding networks, offering a solution to the complexity of feature engineering in ML-based malware analysis. Our approach to extracting semantic features via the Natural Language Processing (NLP) method is inspired by tasks involving sentences and words, respectively, for functions and instructions. We leverage a node2vec mechanism-based graph embedding network to generate malware embedding vectors. These vectors enable automated and efficient malware analysis by combining structural and semantic features. We use two datasets (FCG & Metadata) to assess FCG-MFD performance. F1-Scores of 99.14% and 99.28% are competitive with State-of-the-art (SOTA) methods.

A Robust Hybrid Approach for Malware Detection: Leveraging CNN and LSTM for Encrypted Traffic Analysis

The rapid growth in Internet usage and advancements in network technologies have escalated the risk of network attacks. As the adoption of encryption protocols increases, so does the difficulty in identifying malware within encrypted traffic. Malware represents a significant danger in cyberspace, as it compromises personal data and harms computer systems. Network attacks involve unauthorized access to networks, often aiming to disrupt or damage them, with potentially severe consequences. To counter these threats, researchers, developers, and security experts are constantly innovating new malware detection techniques. Recently, deep learning has gained traction in network security and intrusion detection systems (IDSs), with models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) showing promise in detecting malicious traffic. Despite these advancements, extracting relevant features from diverse malware types remains a challenge. Current solutions demand substantial computational resources and are often inefficient for large datasets. Additionally, existing image-based feature extraction methods consume significant resources. This study tackles these issues by employing a 1D CNN alongside LSTM for the detection and classification of encrypted malicious traffic. Using the Malware Analysis benchmark dataset, which consists of 42,797 malware and 1,079 goodware API call sequences, the proposed model achieved an accuracy of 99.2%, surpassing other state-of-the-art models

The neuropolitical imaginaries of cognitive warfare

The concept of cognitive warfare is currently gaining ground in the policy discussion and in academic research as a way of conceptualizing the ‘weaponization of the neurosciences’. Introducing the Science and Technology Studies-inspired concept of ‘neuropolitical imaginaries’ and assessing discussions on cognitive warfare launched by the NATO Innovation Hub in 2017, this article explores how the contemporary turn to the brain sciences in defense and security reimagines politics. The article argues that seeing the human mind as increasingly vulnerable to external interference redefines the nature of human agency by giving precedence to the skilful ‘cartographer’ employing neuroscientific techniques and methods for persuasion. This emerging vulnerability of the human mind reshapes security into a zero-sum game for ‘cognitive superiority’, making brain science scholars authorities in security. Finally, speaking to the literature on security expertise and science in security, the article argues that the envisaged arms race for cognitive superiority gives rise to a neurosecurity dilemma: as the neurosciences becomes entangled with security, neuroscientific practices simultaneously arise as a core vulnerability, making their work conditional upon compliance with security practices.

RUSSIAN STRATEGIC CULTURE: INSIGHTS FROM THE FULL-SCALE WAR AGAINST UKRAINE

Russia’s full-scale invasion of Ukraine on 24th February 2022 marks a security policy paradigm shift akin to ‘9/11’ for Europe. For the international community of security experts, the invasion represents an anomaly for their rational choice analysis. Their ‘logic of consequence’ must be replaced by the alternative ‘logic of appropriateness’. Hence, the author of this paper employs a broad approach to strategic culture stressing Kremlin regime security as Putin’s operational code. Under this approach, interventionism characterizes the Russian ‘way of war’ as does sheer brutality. The decision to invade Ukraine resulted from Putin’s personal hubris and idiosyncrasies as much as groupthink within the Russian security elite. Putin’s calculus behind invading Ukraine is reconstructed as geopolitical and geo-economic imperialism, along with domestic concerns for pacifying Russia. The first year of war exposed a dysfunctional Russian strategic culture - a collapsing Blitzkrieg effort and a failure to prevail in the subsequent battle for Donbas. The Russian conduct of war reiterated the signature Russian strategic culture of massive destruction and unpunished war crimes. Putin often doubles down and escalates, as seen in the partial mobilization on 21st September 2022. The winter of 2022–2023 brought both systematic Russian attacks on critical Ukrainian energy infrastructure and fierce battles in Vuhledar, Soledar and Bakhmut in which Russia used meatgrinder tactics. The Russian military performance improved during the second year of the war due to the construction of massive fortifications along the frontline. Putin’s strategy has thus turned in favour of a long war of attrition seeking to outlast the combined effect of successive Western upgrading of the weapons supplied to Ukraine and the stamina of the Ukrainians themselves. The Ukrainian counteroffensive shows Ukraine to have the initiative, but progress is painfully slow. This reflects the fact that the war is a war of attrition between two different militaries supported by two dissimilar coalitions of productive power and high-tech innovative capacity.

Perception and Trust in Autonomous Vehicles Post Cyber Security Incidents

The integration of Autonomous Vehicles (AVs) into modern systems of transportation brings with it a new and transformative era. Central to the successful realisation of this transformation is the public’s trust in these vehicles and their safety, particularly in the aftermath of cyber security breaches. The following research therefore explores the various factors underpinning this trust in the context of cyber security incidents. A dual-methodological approach was used in the study. Quantitative data was gathered from structured questionnaires distributed to and completed by a cohort of 151 participants and qualitative data, from comprehensive semi-structured interviews with AV technology and cyber security experts. Rigorous Structural Equation Modelling of the quantitative data then allowed for the identification of the key factors influencing public trust from the standpoint of the research participants including the perceived safety of AV technology, the severity of cyber security incidents, the historic cyber security track record of companies and the frequency of successful cyber security breaches. The role of government regulations, though also influential, emerged as less so. The qualitative data, processed via thematic analysis, resonated with the findings from the quantitative data. This highlighted the importance of perceived safety, incident severity, regulatory frameworks and corporate legacy in shaping public trust. Whilst cyber incidents no doubt erode trust in AVs, a combination of technological perception, regulatory scaffolding and corporate history critically impacts this. These insights are instrumental for stakeholders, from policymakers to AV manufacturers, in charting the course of AV assimilation successfully in future.

XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles

The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100 %, precision of 100 %, recall of 100 %, F1-score of 100 %, and Matthew Correlation Coefficient of 100 % in binary-class situations for internal vehicular (CAN) networks and achieved 99.88 % accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.

An intuitive approach to cybersecurity risk assessment for non-governmental organizations

Purpose This study proposes a guided tool for cybersecurity risk assessment tailored for nongovernmental organizations (NGOs), enabling them to comply with cybersecurity policies despite limitations in security awareness, funding and expertise. Design/methodology/approach A digital transformation is indispensable for ensuring the sustainable operation of NGOs. Embracing a digital manifesto necessitates an awareness of cybersecurity risks, highlighting the critical need for a robust cybersecurity risk assessment methodology. Initial research phases revealed significant shortages in security awareness, funding and expertise. Consequently, this study introduces an intuitive approach tailored specifically for NGOs, supported by a customized tool designed to address their unique requirements. The NIST cybersecurity risk assessment framework and National Cyber-security Authority (NCA) were adopted to define the risk assessment approach. The efficacy of this approach is evaluated qualitatively through a case study involving three NGOs in Saudi Arabia, aimed at assessing their capability to utilize the tool effectively. Following the implementation, a Likert-scale survey gauged satisfaction among NGOs regarding the tool’s utility. Findings Results from the case study indicate high satisfaction, affirming its alignment with their operational needs and enhancement of compliance with NCA controls. Furthermore, the use of the tool enhances the awareness of NCA’s cybercity requirements and controls. Originality/value Based on theoretical and empirical grounds, this research proposes a novel design of security assessment framework tailored for NGO requirements and supported by initiative tool enabling complying with cybersecurity policies and enhances the awareness of cybersecurity controls.

New Fermatean Fuzzy Distance Metric and Its Utilization in the Assessment of Security Crises Using the MCDM Technique

The problem of insecurity is a global phenomenon that has several forms like terrorism, banditry, kidnappings, etc. Insecurity has taken hold in the Sub-Saharan Region of West Africa, especially in Nigeria, for over two decades. Nigeria’s security crisis is more pronounced in the Northern Region, with a new wave in the North-Central Region of Nigeria. It is herculean to assess insecurity in the North-Central Region of Nigeria because of the region’s fuzzy or imprecise nature of insecurity. This constitutes the rationale for deploying the Fermatean fuzzy technique to assess insecurity due to the capacity of the Fermatean fuzzy scheme to handle imprecision. To this end, a new Fermatean fuzzy distance metric is presented to evaluate insecurity in the North-Central Region of Nigeria using a multi-criteria decision-making technique. To express the logic for creating the new Fermatean fuzzy distance metric, some existing Fermatean fuzzy distance metrics are discussed, along with their drawbacks. The mathematical properties of the new technique are discussed, and the new method is applied computationally to assess insecurity in the North-Central Region of Nigeria. The data for the security assessment are collected via Fermatean fuzzy linguistic variables using the opinions of security experts and analyzed using the technique for order of preference by similarity to ideal solution, which is a commonly used multi-criteria decision-making method. Finally, the numerical validity of the new technique is expressed with comparative results, and the finding shows the benefit of the new distance approach over the existing methodologies. The outcome of the work will provide reliable traveling advisories for safe voyages within the region.

An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networks

AbstractThe authors introduce an unsupervised Intrusion Detection System designed to detect zero‐day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This system can identify anomalies without needing prior knowledge or training on attack information. Zero‐day attacks exploit previously unknown vulnerabilities, making them hard to detect with traditional deep learning and machine learning systems that require pre‐labelled data. Labelling data is also a time‐consuming task for security experts. Therefore, unsupervised methods are necessary to detect these new threats. The authors focus on DDoS attacks, which have recently caused significant financial and service disruptions for many organisations. As IoT networks grow, these attacks become more sophisticated and harmful. The proposed approach detects zero‐day DDoS attacks by using random projection to reduce data dimensionality and an ensemble model combining K‐means, Gaussian mixture model, and one‐class SVM with a hard voting technique for classification. The method was evaluated using the CIC‐DDoS2019 dataset and achieved an accuracy of 94.55%, outperforming other state‐of‐the‐art unsupervised learning methods.

Transformation of international security mechanisms in the context of the russian-ukrainian war

The article provides a comprehensive analysis of the transformations in international security mechanisms in the context of the Russia-Ukraine war, ongoing since 2014, with a particular focus on the activities of NATO and the United Nations. The author aims to examine the crisis in the effectiveness of traditional security structures, assess their ability to adapt to new geopolitical realities, and identify the need for the creation of new forms of international security. The research methodology is based on the analysis and synthesis of scientific sources, including works dedicated to the activities of international security organizations, as well as the application of a systematic approach to evaluate the effectiveness of existing security mechanisms. The article examines NATO and the UN's decision-making mechanisms and responses to the Russia-Ukraine war, analyzing their capacity to adequately address the new challenges arising from the armed conflict. The author employs comparative analysis methods to study the changes in the activities of these organizations and analyzes expert assessments to identify key changes in their operations. The study concludes that the Russia-Ukraine war has acted as a catalyst for changes in international security mechanisms, revealing the weaknesses and shortcomings of traditional approaches to global stability. This underscores the need to revise existing strategies, seek innovative approaches to ensuring international security, and potentially create new organizations or coalitions capable of responding more effectively to similar conflicts in the future. The findings of this research may be useful for policymakers, international security experts, and all those interested in the transformation of the global security system.

Scientometric analysis of global cyber security research output based on Web of Science

Objective. This study aims to analyze global cyber security research using various scientometric indicators for 25 years, from 1999 to 2023. Design/Methodology/Approach. The study used the Web of Science international citation database to retrieve a total of 5,640 records. The records were extracted in the CSV file format and further analyzed using an MS-Excel spreadsheet, VosViewer, and Biblioshiny software. Findings. The findings revealed that a 300% annual growth rate was recorded for the publications in 2002. The USA emerged as the top contributor (31.15%) among the countries. The authors affiliated with the State University System of Florida have more publications (107 contributions). Authors Lingfeng Wang of the University of Wisconsin-Milwaukee and Kim-Kwang Raymond Choo of the University of Texas at San Antonio have contributed the equal and highest number of (21) papers. Originality/Value. Cyber security research helps to guardian the individual or nation’s digital sovereignty, protecting it against sophisticated cyber threats and potential attacks. The discipline has become the subject of global research. This study helps cyber security experts, software or web developers, and researchers identify the primary research evidence and its impact on the research community and policymakers for evidence-based policymaking to combat invisible global threats.

Making All Deserts Bloom: The Racist Space/Time of UAE-Israel Collaboration

ABSTRACT Since the normalisation of political relations between the United Arab Emirates and Israel, the so-called ‘desert tech’ industry has emerged as a key area of economic collaboration. Desert tech combines technological innovation with speculation and entrepreneurialism, claiming to offer lucrative yet sustainable solutions to the specific dangers of climate change in arid environments. By weaving together their respective climate and security expertise and technologies, political visions, and investment capital, Israel and the UAE promise to save arid states the world over from ecological catastrophe – making themselves and their political and territorial projects indispensable in the process. In this paper, we unravel their promethean vision of making ‘all deserts bloom’. We first situate this vision in the desert’s tangible histories of colonial environmentalism, retracing the inextricable relationship of climate science to the violent expropriation, securitisation and transformation of Indigenous life and land by white, European settlers. We then follow contemporary UAE-Israel desert-tech collaboration across the geographically expansive and historically layered sites and forms of violence that facilitate, circulate, and secure this industry’s vision of the future. In so doing, we argue that normalisation is underwritten by a racialised temporality – one that constructs desert landscapes as eternally on the threshold of climate catastrophe, erasing the deep, historical relationships of Palestinians, Bedouin and other racialised subjects to arid environments.

Cybersecurity Challenges in the Era of Chinese Electric Passenger Vehicles

The present research focuses on investigating the security of data transmitted by electric passenger vehicles originating from the People’s Republic of China, particularly when sent for remote processing. Initially, the study examines the factors contributing to the widespread presence of Chinese vehicles in the European market. Additionally, it explores the measures undertaken by the European Union to safeguard the information and data security of its member states. Following this investigation, an in-depth qualitative research method involving semi-structured interviews was conducted. Ten experts in information security were interviewed, and their insights were analysed concerning the identified challenges. The primary objective of this publication is to draw attention to the conceptual deficiencies that impede the protection of data and information, highlighting how a large volume of data collected from a third country, in this case, electric passenger vehicles, may pose national security risks.

Analysis of Financial and Digital Literacy Aspects in Modern Economy: Combating Internet Disinformation and Hybrid Threats in OECD

In the current political and economic context, the world is actively addressing the issue of combating disinformation and influencing public opinion. Within the OECD, various initiatives and projects focus on strengthening citizens' critical thinking, promoting media literacy and increasing information transparency. The organization also addresses digital influence and disinformation, considering ways to enhance society's resilience to these issues. Research examines the ability of individuals to combat internet traps, hoaxes, and disinformation in OECD countries, analyzing how individuals identify and detect disinformation and false information online. The research employs various statistical methods, primarily clustering analysis, as well as the analysis of statistical relationships between variables and country comparison analysis. The research indicators are based on OECD data from 2022. The study's results provide useful information for cyber security experts, policymakers, and the general public who are working to combat the spread of disinformation, hybrid threats and hoaxes on the internet.

Security Based SCURM Process (SUBSCRUM) For Secure Agile Development

This study presents an integrated approach for the consideration of security during agile development. A security-based SCRUM process (SBSCRUM) is introduced by taking advantage of the security patterns presented by security experts which contains the solution for a security threat. In the proposed security based SCRUM process the SCRUM team will be responsible for identifying the expected security threats to the system, in which all the expected threats to the system are modelled using mis-use case diagrams and then these modelled threats are mitigated using the security patterns which provide detailed solution to mitigate the threats and at final stage the security testing is performed to ensure that all the security threats identified are mitigated using security patterns. SBSCRUM is evaluated through the case studies in this paper. SBSCRUM will enable the software developers to develop secure software using agile development methods which lacks in the past.

Individual Differences in Psychological Stress Associated with Data Breach Experiences

Data breach incidents are now a regular occurrence, with millions of people affected worldwide. Few studies have examined the psychological aspects of data breach experiences, however, or the individual differences that influence how people react to these events. In this study, we examined the psychological stress associated with a personal experience with a data breach and several individual differences hypothesized to modulate such stress (age, gender, digital security awareness and expertise, trait anxiety, negative emotionality, and propensity to worry). A student sample (N = 166) and a community sample (N = 359) completed an online survey that asked participants to describe their most serious data breach and then complete the Impact of Events Scale—Revised (IES-R) to answer specific questions about the nature of the stress they experienced after the breach. Standard measures of trait anxiety, negative emotionality, and propensity to worry were also completed. A Data Breach Severity Index (DBSI) was created to quantify the invasiveness and consequences of each participant’s data breach. Hierarchical multiple regression analyses were used to identify demographic variables and psychological characteristics predictive of IES-R scores while controlling for DBSI scores. As expected, more invasive and consequential data breaches were associated with higher IES-R scores (greater data-breach-induced stress). Women had higher IES-R scores than men, and this difference persisted after controlling for gender differences in anxiety, negative emotionality, and propensity to worry. Greater daily social media use was associated with higher IES-R scores, whereas higher digital security expertise was associated with lower IES-R scores. The results illuminate several relationships between demographic and psychological characteristics and data-breach-induced stress that should be investigated further.

Ethical, Legal, and Practical Concerns Surrounding the Implemention of New Forms of Consent for Health Data Research: Qualitative Interview Study.

In Europe, within the scope of the General Data Protection Regulation, more and more digital infrastructures are created to allow for large-scale access to patients' health data and their use for research. When the research is performed on the basis of patient consent, traditional study-specific consent appears too cumbersome for many researchers. Alternative models of consent are currently being discussed and introduced in different contexts. This study explores stakeholder perspectives on ethical, legal, and practical concerns regarding models of consent for health data research at German university medical centers. Semistructured focus group interviews were conducted with medical researchers at German university medical centers, health IT specialists, data protection officers, and patient representatives. The interviews were analyzed using a software-supported structuring qualitative content analysis. Stakeholders regarded broad consent to be only marginally less laborious to implement and manage than tiered consent. Patient representatives favored specific consent, with tiered consent as a possible alternative. All stakeholders lamented that information material was difficult to understand. Oral information and videos were mentioned as a means of improvement. Patient representatives doubted that researchers had a sufficient degree of data security expertise to act as sole information providers. They were afraid of undue pressure if obtaining health data research consent were part of medical appointments. IT specialists and other stakeholders regarded the withdrawal of consent to be a major challenge and called for digital consent management solutions. On the one hand, the transfer of health data to non-European countries and for-profit organizations is seen as a necessity for research. On the other hand, there are data security concerns with regard to these actors. Research without consent is legally possible under certain conditions but deemed problematic by all stakeholder groups, albeit for differing reasons and to different degrees. More efforts should be made to determine which options of choice should be included in health data research consent. Digital tools could improve patient information and facilitate consent management. A unified and strict regulation for research without consent is required at the national and European Union level. Obtaining consent for health data research should be independent of medical appointments, and additional personnel should be trained in data security to provide information on health data research.

Impact of gain-loss framing on online scam susceptibility: the role of scam frames, warning frames, and risk perception

ABSTRACT In the digital era, technological advancements have enabled greater convenience, economic growth, and productivity, but also caused a significant increase in online scams, leading to both financial loss and emotional distress to individuals. It becomes critical to understand why individuals fall for these scams and identify effective protective measures. Central to the tactics used by scammers and security experts is gain-loss framing, a persuasive technique in communication that influences decision-making by altering how information is presented; it emphasises potation benefits (gain-framed) or losses (loss-framed) associated with the same core content. This study conducts a systematic test of how gain-loss framing is employed in scam tactics and warning messages and examines the role of risk perception in these processes. Results suggest that loss-based (versus reward-based) scams increase individuals’ perceived risk of not responding to scams, making them more susceptible to scams. Loss-framed (versus gain-framed) warnings are more effective in preventing people from responding to scams, particularly when individuals perceive moderate to high risks of responding to scams. These findings not only bridge a gap in our theoretical understanding of how gain-loss framing influences scam compliance and intervention processes but also inform the design of more targeted and effective anti-scam interventions.

Introducing Security Mechanisms in OpenFog-Compliant Smart Buildings

Designing smart building IoT applications is a complex task. It requires efficiently integrating a broad number of heterogeneous, low-resource devices that adopt lightweight strategies. IoT frameworks, especially if they are standard-based, may help designers to scaffold the applications. OpenFog, established as IEEE 1934 standard, promotes the use of free open source (FOS) technologies and has been identified for use in smart buildings. However, smart building systems may present vulnerabilities, which can put their integrity at risk. Adopting state-of-the-art security mechanisms in this domain is critical but not trivial. It complicates the design and operation of the applications, increasing the cost of the deployed systems. In addition, difficulties may arise in finding qualified cybersecurity personnel. OpenFog identifies the security requirements of the applications, although it does not describe clearly how to implement them. This article presents a scalable architecture, based on the OpenFog reference architecture, to provide security by design in buildings of different sizes. It adopts FOS technologies over low-cost IoT devices. Moreover, it presents guidelines to help developers create secure applications, even if they are not security experts. It also proposes a selection of technologies in different layers to achieve the security dimensions defined in the X.805 ITU-T recommendation. A proof-of-concept Indoor Environment Quality (IEQ) system, based on low-cost smart nodes, was deployed in the Faculty of Engineering of Vitoria-Gasteiz to illustrate the implementation of the presented approach. The operation of the IEQ system was analyzed using software tools frequently used to find vulnerabilities in IoT applications. The use of state-of-the-art security mechanisms such as encryption, certificates, protocol selection and network partitioning/configuration in the OpenFog-based architecture improves smart building security.