Infrastructure Devices Research Articles

Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems

With the growing integration of IoT devices in critical infrastructure, cybersecurity threats such as Distributed Denial of Service (DDoS) attacks on Energy Hubs (EH) have become a significant concern. This study aims to address these challenges by evaluating the effectiveness of various supervised machine learning (ML) algorithms in predicting DDoS attacks targeting EH systems through IoT devices. Using the CICDDOS2019 and KDD-CUP datasets, a comprehensive analysis was conducted on several classifiers, including Decision Tree (DT), Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. The results highlight Gradient Boosting as the most effective model, particularly for the CICDDOS2019 dataset, demonstrating superior accuracy and predictive capability. Additionally, hybrid models combining Gradient Boosting with SVM or DT showed strong performance, though with varying precision and recall. This study provides valuable insights into the selection and tailoring of ML models for specific security challenges, emphasizing the need for ongoing research to enhance the resilience of EH systems and IoT devices against evolving DDoS threats.

A Sustainable City Made By Resident-Experts - How Designerly Intervention Enacted Rights of the Public and Urban Infrastructure

This paper proposes an investigation of how enacting infrastructure is intertwined with historically specific processes of constructing the public, which necessitates a focus on the co-production of public problems and the public, and the establishment of infrastructural connections. Intervening in the context where reflexive planning and design methodologies are deployed to implement urban regeneration through public engagement (what I call “designerly intervention”), this paper investigates what type of collectivity is constituted as infrastructural publics by designerly interventions and what forms of rights they can have. Drawing on ethnographic accounts of urban regeneration in Seoul, it shows how designerly interventions mobilize residents as possessing a novel form of expertise, as “resident-experts” who can design a sustainable city, problematizing the renewal of the worn-out infrastructure as how to stimulate the resilience of a city. This results in an infrastructural connection that focuses on immediate results and the ordinary scale of urban regeneration, which enacts the rights of the public as city-users’ rights, namely not as rights protected by law but as a practical capacity that infrastructural devices in everyday life provide. This paper explores how designerly interventions invoke an experimental approach that opens up the normative questions of public engagement with planning.

Ex nihilo nihil fit – New cities as territorial infrastructures in Brazil and Portugal

The pre-foundational stages of new cities are marked by several decisive elements that ultimately led to their genesis. The reason for their existence and where they materialize necessarily depend on decisions made by agents, institutions and specific political-economic-social circumstances that have guided each of these undertakings. In some cases, new cities have not only involved the destruction of local ecosystems (human and natural) but even their eventual disappearance, within a context whereby the human species has simply viewed them as being subsidiary (almost always superfluous) to pursuing its colonizing determinations. The aim of this article, which is the result of post-doctoral research, is to characterize new cities, those intentionally created and professionally designed, as infrastructural devices for territorial activation and urbanization. Rather than being the end-products of a political-spatial dynamic, could these cities be understood as intermediary cogwheels for productive and extractive activities? In the pursuit of an answer, attention has been focused on the cities founded in the central-south region of the Amazon (Brazil) and in the Sines region (Portugal) during the 1970s, all of which emerged from developmentalism, state authoritarianism, the dynamics of capitalization and accumulation and the expanded participation of private sectors. While maintaining the due proportions and scalar differences, the processes of occupation, colonization, infrastructure development, and exploitation of natural assets characterize both territories, including the predatory urban planning practices that are harmful to local ecosystems. Thus, these cities have become our exploratory field in this investigation, conducted through an historical perspective, underpinned by consultations with primary and secondary sources and on-site visits. With an understanding that “nothing comes from nothing”, this study qualifies each new city as a representation of the conditions that led to its birth and its strategic position in the recent capitalist system.

Advancements in Infrastructure Devices: A Literature Review

Advancements in Infrastructure Devices: A Literature Review

Impact of Cyber Security Operations on Hardware Requirements for Stable and Workable Industrial Environments

Securing electricity distribution is one of the most important principles of the EU cyber security strategy. For example, European cyber security regulations, such as NIS2 (Network and Information Security Directive), CER (Critical Entities Resilience Directive), and Cyber Resilience Act (CRA) together aim to create a foundation and guidelines for international standards in various industries and the operation of critical infrastructure. Securing critical infrastructure is a common goal for Western operators. The new European Union (EU) directives bring new requirements to critical infrastructure administrators, device manufacturers and operators. Previously, member states have had responsibility for compliance with the directives, but they have been given freedom in the method by which they approach the requirements. Currently, member states' solutions are not always uniform, which has led to increased difficulties in coordination on a multi-national level. This, in turn, may lead to difficulties in coordination when responding to cybersecurity threats and attacks on critical infrastructure. The new regulation focuses on unifying the reporting between member states, reporting requirements of severe critical infrastructure events, and creating cybersecurity risk management procedures. In this study, we will provide a novel solution on how critical infrastructure administrators, device manufacturers, and operators may respond and become compliant with the new EU directives. To reach compliance and to enable the responsibilities that are required by the directive, the critical infrastructure devices and environment must have the capability to enable the responsible parties to identify, protect, detect, respond, and report. This sequence of actions is cyclical in nature since the identification of threats and vulnerabilities requires reports, which in turn requires data and detection. Our study focuses on the hardware requirements this causes on the manufacturing specifications, such as data collection and detection capabilities. The research belongs to the CSG project, and the purpose is to develop a governance model to minimize Operational Technology related risks and create a new standardized operating environment for the seamless utilization of energy solutions and industrial environment. The results of the study will be used in the analysis of requirements definitions in the OT environment.

Security and Privacy Issues in Internet of Things (IoT) Devices Using COPRAS Method

This paper explores various security and privacy issues inherent in IoT devices, ranging from vulnerabilities in device firmware and software to data breaches and unauthorized access. We delve into the challenges of securing IoT devices due to their resource constraints, diverse communication protocols, and often lax security practices during development. Additionally, we discuss privacy implications stemming from the collection and sharing of sensitive personal data by IoT devices, as well as the potential for surveillance and data misuse. Furthermore, we examine the implications of IoT devices in critical infrastructure and industrial systems, where security breaches can have severe consequences. Finally, we propose potential solutions and best practices to address these challenges, including robust encryption methods, regular security updates, and improved authentication mechanisms, to ensure the security and privacy of IoT devices in an increasingly connected world. The exponential growth of IoT devices across various sectors such as smart homes, healthcare, transportation, and industrial automation underscores the importance of ensuring their security and privacy. As these devices become more integrated into daily life and critical infrastructure, any vulnerability can have widespread and severe consequences. The evaluation of alternative performances through Complex Proportionality Assessment (COPRAS) requires an understanding of key criteria, exploration of options, and comparison of relevant facts. Meeting the decision-makers' desire for comparing grades involves choosing among multiple options based on predetermined competing requirements. COPRAS offer a method for such assessments in real-world scenarios, where criteria are nuanced and values cannot be quantified numerically. From the result smart thermostat got the first rank whereas wearable fitness tracker is having the lowest rank.

Secure Message Dissemination with QoS Guaranteed Routing in Internet of Vehicles

Internet of Vehicles (IoV) is a variant of vehicular adhoc network (VANET) where vehicles can communicate with other vehicles, infrastructure devices, parking lots and even pedestrians. Communication to other entities is facilitates through various services like DSRC, C2C-CC. Fake messages can be propagated by attackers for various selfish needs. Complex authentication procedures can affect the propagation of emergency messages. Thus a light weight mechanism to ensure the trust of messages without affecting the delivery deadlines for emergency messages. Addressing this problem, this work proposes a clustering based network topology for IoV where routing is optimized for message dissemination of various classes using hybrid meta-heuristics. In addition, two stage message authentication technique combining collaborative authentication with Bayesian filtering is proposed to verify the authenticity of message. Through simulation analysis, the proposed solution is found to detect fake messages with an accuracy of 96% with 10% lower processing delay compared to existing works.

An approach to preventing vehicular ad-hoc networks from malicious nodes based on blockchain

This study presents a revolutionary blockchain-based security mechanism that authenticates vehicles and instantly alerts other vehicles to unlawful messages. Access should only be allowed to authenticated vehicles in order to increase the network security, and access should be restricted or revoked for any vehicles that exhibit inappropriate behaviour. Vehicles linked to the Vehicular Ad Hoc Network (VANET) can share data and take immediate action with the help of adjacent infrastructure devices, like streetlights and traffic signals, as well as with one another. In order to maintain efficiency and safety, an intelligent transportation system depends on the flow of information between vehicles. Intelligent vehicles are vulnerable to hacking attacks because they have numerous technical flaws. A proposed security scheme identifies malicious nodes and detects forged messages, based on multiple factors like sender reputation, time and distance effectiveness. This is accomplished by employing environmental sensory data and authenticating the packets, allowing a vehicle to add numerous blocks to the blockchain. Research has been conducted on the bases of safety protection for vehicle systems as a result of security concerns. When the proposed approach is tested on different vehicle and attacker densities, the results indicate that it has a lower authentication delay and higher communication overhead than the other existing approaches. This study explored various threats from malicious nodes and the techniques in which our proposed methodology handles such attacks.

Data Interworking Model and Analysis for Harmonization of Smart Metering Protocols in IoT-Based AMI System

In the energy sector, since the adoption of remote device management for massive advanced metering infrastructure (AMI) devices and Internet of Things (IoT) technology using a representational state transfer (RESTful) architecture, a blurred boundary has been developed between traditional AMI and IoT. With respect to smart meters, the standard-based smart metering protocol, called the device language message specification (DLMS) protocol, still has a predominant role in the AMI industry. Thus, we aim to propose a novel data interworking model in this article that embraces the DLMS protocol in AMI using the most promising IoT protocol, the so-called lightweight machine-to-machine (LwM2M) protocol. We provide a 1:1 conversion model using the correlation of the two protocols with an analysis of the object modeling and resource management methods of both the LwM2M and DLMS protocols. The proposed model utilizes a complete RESTful architecture, which is the most beneficial in the LwM2M protocol. It improves the average packet transmission efficiency and packet delay on the plaintext and encrypted text (session establishment and authenticated encryption) by 52.9%p and 9.9%p, respectively, and by 11.86 ms for both cases, compared to the encapsulation method of the LwM2M protocol, KEPCO's current approach. This work provides the key idea to unify the protocol for the remote metering and device management of field devices into the LwM2M protocol, and it is expected that this work will improve the efficiency in the operation and management of KEPCO's AMI system.

Coping with Extortion: On Violence, Parasites, and Water Infrastructures in Buenaventura, Colombia

In this paper, I explore the coping mechanisms deployed by shopkeepers in the face of extortion in the port city of Buenaventura, in southwestern Colombia. Relying on six months of fieldwork experience in a hardware shop of Ciudad Blanca, an impoverished neighborhood, I examine the everyday violence and the responses of the population. Due to the deficient infrastructure in the city, people are forced to build small infrastructural arrangements, such as water storage systems, to access public services. Therefore, the circulation and availability of infrastructural devices such as pipes, hoses, and tanks are vital for the reproduction of life in the city. In this article, I argue that extortion (locally calledvacuna(‘vaccine’)) interferes in the circulation of these vital material commodities, making coping mechanisms crucial in the face of vulnerability and uncertainty. Recent scholarly literature has pointed to the influence of illegal economies in urban planning and organization. Going beyond that, I argue that organized crime interferes in the material practices of the urban poor, turning to the term ‘parasite’ posed by French philosopher Michel Serres to conceptualize this form of interference in the circulation of commodities.

A Flow-Based Anomaly Detection Approach With Feature Selection Method Against DDoS Attacks in SDNs

Software Defined Networking (SDN) is an emerging network platform, which facilitates centralised network management. The SDN enables the network operators to manage the overall network consistently and holistically, regardless the complexity of infrastructure devices. The promising features of the SDN enhance network security and facilitate the implementation of threat detection systems through software applications using open APIs. However, the emerging technology creates new security concerns and new threats that do not exist in the current traditional networks. Distributed Denial of Service attacks (DDoS) are one of the most rampant attacks that can interrupt the functionality of the network and make most of the network services unreachable for network users. The efficient identification of DDos attacks on SDN environments in literature is still a challenge because of the number of network features taken into account and the overhead of applying machine learning based anomaly detection techniques. Hence, in this paper, we aim to use two popular feature selection methods, i.e., Information Gain (IG) and Random Forest (RF) in order to analyse the most comprehensive relevant features of DDoS attacks in SDN networks. Using the most relevant features will improve the accuracy of the anomaly detection system and reduce the false alarm rates. Moreover, we propose a Deep Learning (DL) technique based on Long Short Term Memory (LSTM) and Autoencoder to tackle the problem of DDoS attacks in SDNs. We perform our analysis and evaluation on three different datasets, i.e., InSDN, CICIDS2017 and CICIDS2018. We also measure the overhead of the proposed DL model on the SDN controller and test the network performance in terms of network throughput and end-to-end latency. The results validate that the DL approach can efficiently identify DDoS attacks in SDN environments without any significant degradation in the controller performance.

Orfeon: An AIOps framework for the goal-driven operationalization of distributed analytical pipelines

The use of Artificial Intelligence solutions keeps raising in the business domain. However, this adoption has not brought the expected results to companies so far. There are several reasons that make Artificial Intelligence solutions particularly complicated to adopt by businesses, such as the knowledge gap between the data science and operations teams. In this paper, we tackle the operationalization of distributed analytical pipelines in heterogeneous production environments, which span across different computational layers. In particular, we present a system called Orfeon, which can leverage different objectives and yields an optimized deployment for these pipelines. In addition, we offer the mathematical formulation of the problem alongside the objectives in hand (i.e. resilience, performance, and cost). Next, we propose a scenario utilizing cloud and edge infrastructural devices, in which we demonstrate how the system can optimize these objectives, without incurring scalability issues in terms of time nor memory. Finally, we compare the usefulness of Orfeon with a variety of tools in the field of machine learning operationalization and conclude that it is able to outperform these tools under the analyzed criteria, making it an appropriate system for the operationalization of machine learning pipelines.

Development and Validation of a Transient Heat Transfer Model for Evaluating Thermal Management Solutions for Packaging Next-Generation Smart City Infrastructure Devices

Abstract Outdoor digital displays have become increasingly popular and common for smart city applications, and more recently provides a concealment solution and integration point for outdoor communications devices meant to be attached to buildings, street lamps, or traffic poles. Given the larger energy requirements for powering next generation 5G cellular networks, these devices create unique difficulties in developing and evaluating thermal management solutions. The present study develops and validates the extreme condition transient (ECT) climate model using a computational fluid dynamics/heat transfer numerical model, to evaluate diurnal thermal responses from a representative 5G small cell devices. The model is validated for local conditions present in Atlanta, GA for two unique days. The thermal response from the ECT climate model is presented alongside three real case study locations, Miami, FL, New York, NY, and Phoenix, AZ.

Genetic-based optimization in fog computing: Current trends and research opportunities

Fog computing is a new computational paradigm that emerged from the need to reduce network usage and latency in the Internet of Things (IoT). Fog can be considered as a continuum between the cloud layer and IoT users that allows the execution of applications or storage/processing of data in network infrastructure devices. The heterogeneity and wider distribution of fog devices are the key differences between cloud and fog infrastructure. Genetic-based optimization is commonly used in distributed systems; however, the differentiating features of fog computing require new designs, studies, and experimentation. The growing research in the field of genetic-based fog resource optimization and the lack of previous analysis in this field have encouraged us to present a comprehensive, exhaustive, and systematic review of the most recent research works. Resource optimization techniques in fog were examined and analyzed, with special emphasis on genetic-based solutions and their characteristics and design alternatives. We defined a taxonomy of the optimization scope in fog infrastructures and used this optimization taxonomy to classify the 70 papers in this survey. Subsequently, the papers were assessed in terms of genetic optimization design. Finally, the benefits and limitations of each surveyed work are outlined in this paper. Based on these previous analyses of the relevant literature, future research directions were identified. We concluded that more research efforts are needed to address the current challenges in data management, workflow scheduling, and service placement. Additionally, there is still room for improved designs and deployments of parallel and hybrid genetic algorithms that leverage, and adapt to, the heterogeneity and distributed features of fog domains.

Сокращение ресурсозатрат при реализации планово-предупредительного технического обслуживания устройств железнодорожной автоматики на основе цифровых моделей

This article considers a row of issues and problems in the area of optimal planning of jobs on technical servicing of railway automation and telemechanics devices in the process of planned-preventive servicing realization including the analysis of modern level and possibilities to increase task number being solved during automation of planning. Technologies for infrastructure device servicing, providing for minimizing and ideally - for influence elimination of so-called «human factor» on work implementation quality, are considered presently to be the most promising. One of the ways to reduce unproductive resource costs is to develop new approaches to technical servicing planning at that to, both, long-range planning (distribution of individual jobs by dates) and operational planning (making up an order to fulfill individual jobs within a shift). A vital task is a work planning automation in the frames of planned-preventive servicing of railway automation and telemechanics devices on the basis of fixation of technological situations that’re equivalent to functioning imitation of devices in various modes. In order to realize the set task, it’s proposed to develop corresponding digital models, which provide for technological situation identification and for the definition of work set, which will be considered as implemented, as well as the set of activities, which’re needed to be implemented on time.

RAPCHI: Robust authentication protocol for IoMT-based cloud-healthcare infrastructure.

With the fast growth of technologies like cloud computing, big data, the Internet of Things, artificial intelligence, and cyber-physical systems, the demand for data security and privacy in communication networks is growing by the day. Patient and doctor connect securely through the Internet utilizing the Internet of medical devices in cloud-healthcare infrastructure (CHI). In addition, the doctor offers to patients online treatment. Unfortunately, hackers are gaining access to data at an alarming pace. In 2019, 41.4 million times, healthcare systems were compromised by attackers. In this context, we provide a secure and lightweight authentication scheme (RAPCHI) for CHI employing Internet of medical Things (IoMT) during pandemic based on cryptographic primitives. The suggested framework is more secure than existing frameworks and is resistant to a wide range of security threats. The paper also explains the random oracle model (ROM) and uses two alternative approaches to validate the formal security analysis of RAPCHI. Further, the paper shows that RAPCHI is safe against man-in-the-middle and reply attacks using the simulation programme AVISPA. In addition, the paper compares RAPCHI to related frameworks and discovers that it is relatively light in terms of computation and communication. These findings demonstrate that the proposed paradigm is suitable for use in real-world scenarios.

Security and Privacy Issues in IoT

Internet of Things (IoT) is a global network of physical and virtual ‘things’ connected to the internet. Each object has unique ID which is used for identification. IoT is the emerging technology which will change the way we interact with devices. In future almost every electronic device will be a smart device which can compute and communicate with hand-held and other infrastructure devices. As most of the devices may be battery operated, due to less processing power the security and privacy is a major issue in IoT. Authentication, Identification and device heterogeneity are the major security and privacy concerns in IoT. Major challenges include integration, scalability, ethics communication mechanism, business models and surveillance. In this paper major issues related to security and privacy of IoT are focused.

An access control protocol for IoT‐based critical infrastructure in smart grid environment

SummaryInformation and communication technology (ICT) can add appealing attributes such as transparency, energy conservation, cost reduction, reliability, and efficiency to the traditional power grid. The addition of these attributes is possible due to the concept of a smart grid (SG). However, ICT applications engender new access control challenges. Therefore, many studies have been conducted in recent years to investigate that the most significant challenge of the smart grid is privacy. In the light of these studies, two significant problems have been identified for SG access control protocols that must be addressed. First, we have all essential security requirements such as integrity, confidentiality, and authentication. Moreover, the cryptographic algorithms impose significant computational and communication costs on the system, however, in most of the contemporary infrastructure's devices usually have limited‐resource. Therefore, second, the lightweight design of access control protocols is also a significant challenge. To address these challenges, we introduce a provably secure and lightweight access control protocol for SGs that eliminates key exchange issues while increasing privacy and security. We evaluate the proposed protocol to determine its security strength against well‐known security attacks. Finally, we present a performance evaluation of the proposed protocol to demonstrate its efficiency in terms of computation and communication costs.

Toward Autonomous Physical Security Defenses Using Machine Learning

The sheer increase in interconnected devices, reaching 50 B in 2025, makes it easier for adversaries to have direct access to the target system and perform physical attacks. This risk is exacerbated by the proliferation of Internet-of-Battlefield Things (IoBT) and increased reliance on the use of embedded devices in critical infrastructure and industrial control systems. Existing anti-tamper designs protect against limited forms of attacks and have deterministic tamper responses, which can undermine the availability of systems. Advancements in physical inspection techniques have enabled stealthier attacks. Therefore, there is a pressing need for more intelligent defenses that ensure a longer operational time while keeping up with the expected increase in the capabilities of adversaries. This study proposes to enhance existing physical protection methods by developing an intelligent anti-tamper using machine learning algorithms. It uses an analytic system capable of detecting and classifying multiple types of behaviors (e.g., normal operation conditions, known attack vectors, and anomalous behavior). The system also has a layered response mechanism and recovery scheme, which reduces false alarms and prolongs the operational time. An experimental platform was constructed and used for data collection and machine learning model training. This study also explored the impact of adversarial learning attacks on the proposed system and subsequently developed a countermeasure. The final prototype was capable of recognizing two types of normal operating conditions (sheltered and exposed environments) and four types of physical attacks. It also has adaptive response and recovery mechanisms.

APIVADS: A Novel Privacy-Preserving Pivot Attack Detection Scheme Based on Statistical Pattern Recognition

Advanced cyber attackers often “pivot” through several devices in such complex infrastructure to obfuscate their footprints and overcome connectivity restrictions. However, prior pivot attack detection strategies present concerning limitations. This paper addresses an improvement of cyber defence with APIVADS, a novel adaptive pivoting detection scheme based on traffic flows to determine cyber adversaries’ presence based on their pivoting behaviour in simple and complex interconnected networks. Additionally, APIVADS is agnostic regarding transport and application protocols. The scheme is optimized and tested to cover remotely connected locations beyond a corporate campus’s perimeters. The scheme considers a hybrid approach between decentralized host-based detection of pivot attacks and a centralized approach to aggregate the results to achieve scalability. Empirical results from our experiments show the proposed scheme is efficient and feasible. For example, a 98.54% detection accuracy near real-time is achievable by APIVADS differentiating ongoing pivot attacks from regular enterprise traffic as TLS, HTTPS, DNS and P2P over the internet.