Biblio

In this paper we consider cyber security requirements of the smart buildings. We identify cyber risks, threats, attack scenarios, security objectives and related security controls. The work was done as a part of a smart building design and construction work. From the controls identified w e concluded security practices for engineering-in smart buildings security. The paper provides an idea toward which system security engineers can strive in the basic design and implementation of the most critical components of the smart buildings. The intent of the concept is to help practitioners to avoid ad hoc approaches in the development of security mechanisms for smart buildings with shared space.

The increasing demand for the interconnected IoT based smart grid is facing threats from cyber-attacks due to inherent vulnerability in the smart grid network. There is a pressing need to evaluate and model these vulnerabilities in the network to avoid cascading failures in power systems. In this paper, we propose and evaluate a vulnerability assessment framework based on attack probability for the protection and security of a smart grid. Several factors were taken into consideration such as the probability of attack, propagation of attack from a parent node to child nodes, effectiveness of basic metering system, Kalman estimation and Advanced Metering Infrastructure (AMI). The IEEE-300 bus smart grid was simulated using MATPOWER to study the effectiveness of the proposed framework by injecting false data injection attacks (FDIA); and studying their propagation. Our results show that the use of severity assessment standards such as Common Vulnerability Scoring System (CVSS), AMI measurements and Kalman estimates were very effective for evaluating the vulnerability assessment of smart grid in the presence of FDIA attack scenarios.

Integration of technology with power grid emerged Smart grid. The advancement of power grid into smart grid faces some security issues like message mod-ification attacks, message injection attacks etc. If these issues are correctly not addressed, then the performance of the smart grid is degraded. Smart grid has bidirectional communication among the smart grid entities. The flow of user energy consumption information between all smart grid entities may lead the user privacy violation. Smart grids have various components but service providers and smart meters are the main components. Smart meters have sensing and communication functionality, while service providers have control and communication functionality. There are many privacy preservation schemes proposed that ensure the cus-tomer's privacy in the smart grid. To preserve the customer's data privacy and communication, authentication and key agreement schemes are required between the smart meter and the service provider. This paper proposes an efficient key agreement protocol to handle several security challenges in smart grid. The proposed protocol is tested against the various security attributes necessary for a key establishment protocol and found safe. Further the performance of the proposed work is compared with several others existing work for smart grid application and it has been observed that the proposed protocol performs significantly better than the existing protocols available in the literature.

Event detection and classification are crucial to power system stability. The Wide Area Measurement System (WAMS) technology helps in enhancing wide area situational awareness by providing useful synchronized information to the grid control center in order to accurately identify various power system events. This paper demonstrates the viability of using EWAMS (Egyptian Wide Area Measurement System) data as one of the evolving technologies of smart grid to identify extreme events within the Egyptian power grid. The proposed scheme is based on online synchronized measurements of wide-area monitoring devices known as Frequency Disturbance Recorders (FDRs) deployed at selected substations within the grid. The FDR measures the voltage, voltage angle, and frequency at the substation and streams the processed results to the Helwan University Host Server (HUHS). Each FDR is associated with a timestamp reference to the Global Positioning System (GPS) base. An EWAMS-based frequency disturbance detection algorithm based on the rate of frequency deviation is developed to identify varies types of events such as generator trip and load shedding. Based on proper thresholding on the frequency and rate of change of frequency of the Egyptian grid, different types of events have been captured in many locations during the supervision and monitoring the operation of the grid. EWAMS historical data is used to analyze a wide range of data pre-event, during and post-event for future enhancement of situational awareness as well as decision making.

An accurate state estimation (SE) considering increased uncertainty by the high penetration of renewable energy systems (RESs) is more and more important to enhance situational awareness, and the optimal and resilient operation of the renewable-rich power grids. However, it is anticipated that adversaries who plan to manipulate the target power grid will generate attacks that inject inaccurate data to the SE using the vulnerabilities of the devices and networks. Among potential attack types, false data injection attack (FDIA) is gaining popularity since this can bypass bad data detection (BDD) methods implemented in the SE systems. Although numerous FDIA detection methods have been recently proposed, the uncertainty of system configuration that arises by the continuously increasing penetration of RESs has been been given less consideration in the FDIA algorithms. To address this issue, this paper proposes a new FDIA detection scheme that is applicable to renewable energy-rich power grids. A deep learning framework is developed in particular by synergistically constructing a Bidirectional Long Short-Term Memory (Bi-LSTM) with modern smart grid characteristics. The developed framework is evaluated on the IEEE 14-bus system integrating several RESs by using several attack scenarios. A comparison of the numerical results shows that the proposed FDIA detection mechanism outperforms the existing deep learning-based approaches in a renewable energy-rich grid environment.

Smart city management is going through a remarkable transition, in terms of quality and diversity of services provided to the end-users. The stakeholders that deliver pervasive applications are now able to address fundamental challenges in the big data value chain, from data acquisition, data analysis and processing, data storage and curation, and data visualisation in real scenarios. Industry 4.0 is pushing this trend forward, demanding for servitization of products and data, also for the smart cities sector where humans, sensors and devices are operating in strict collaboration. The data produced by the ubiquitous devices must be processed quickly to allow the implementation of reactive services such as situational awareness, video surveillance and geo-localization, while always ensuring the safety and privacy of involved citizens. This paper proposes a modular architecture to (i) leverage innovative technologies for data acquisition, management and distribution (such as Apache Kafka and Apache NiFi), (ii) develop a multi-layer engineering solution for revealing valuable and hidden societal knowledge in smart cities environment, and (iii) tackle the main issues in tasks involving complex data flows and provide general guidelines to solve them. We derived some guidelines from an experimental setting performed together with leading industrial technical departments to accomplish an efficient system for monitoring and servitization of smart city assets, with a scalable platform that confirms its usefulness in numerous smart city use cases with different needs.

Similar to any spoof detection systems, power grid monitoring systems and devices are subject to various cyberattacks by determined and well-funded adversaries. Many well-publicized real-world cyberattacks on power grid systems have been publicly reported. Phasor Measurement Units (PMUs) networks with Phasor Data Concentrators (PDCs) are the main building blocks of the overall wide area monitoring and situational awareness systems in the power grid. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. In this paper, we consider a stealthier data spoofing attack against PMU networks, called a mirroring attack, where an adversary basically injects a copy of a set of packets in reverse order immediately following their original positions, wiping out the correct values. To the best of our knowledge, for the first time in the literature, we consider a more challenging attack both in terms of the strategy and the lower percentage of spoofed attacks. As part of our countermeasure detection scheme, we make use of novel framing approach to make application of a 2D Convolutional Neural Network (CNN)-based approach which avoids the computational overhead of the classical sample-based classification algorithms. Our experimental evaluation results show promising results in terms of both high accuracy and true positive rates even under the aforementioned stealthy adversarial attack scenarios.

Increasing connectivity and automation in vehicles leads to a greater potential attack surface. Such vulnerabilities within vehicles can also be used for auto-theft, increasing the potential for attackers to disable anti-theft mechanisms implemented by vehicle manufacturers. We utilize patterns derived from Controller Area Network (CAN) bus traffic to verify driver “behavior”, as a basis to prevent vehicle theft. Our proposed model uses semi-supervised learning that continuously profiles a driver, using features extracted from CAN bus traffic. We have selected 15 key features and obtained an accuracy of 99% using a dataset comprising a total of 51 features across 10 different drivers. We use a number of data analysis algorithms, such as J48, Random Forest, JRip and clustering, using 94K records. Our results show that J48 is the best performing algorithm in terms of training and testing (1.95 seconds and 0.44 seconds recorded, respectively). We also analyze the effect of using a sliding window on algorithm performance, altering the size of the window to identify the impact on prediction accuracy.

The features of socio-cyber-physical systems are presented, which dictate the need to revise traditional management methods and transform the management system in such a way that it takes into account the presence of a person both in the control object and in the control loop. The use of situational control mechanisms is proposed. The features of this approach and its comparison with existing methods of situational awareness are presented. The comparison has demonstrated wider possibilities and scope for managing socio-cyber-physical systems. It is recommended to consider a wider class of types of relations that exist in socio-cyber-physical systems. It is indicated that such consideration can be based on the use of pseudo-physical logics considered in situational control. It is pointed out that it is necessary to design a classifier of situations (primarily in cyberspace), instead of traditional classifiers of threats and intruders.

Real-time situational awareness (SA) plays an essential role in accurate and timely incident response. Maintaining SA is, however, extremely costly due to excessive false alerts generated by intrusion detection systems, which require prioritization and manual investigation by security analysts. In this paper, we propose a novel approach to prioritizing alerts so as to maximize SA, by formulating the problem as that of active learning in a hidden Markov model (HMM). We propose to use the entropy of the belief of the security state as a proxy for the mean squared error (MSE) of the belief, and we develop two computationally tractable policies for choosing alerts to investigate that minimize the entropy, taking into account the potential uncertainty of the investigations' results. We use simulations to compare our policies to a variety of baseline policies. We find that our policies reduce the MSE of the belief of the security state by up to 50% compared to static baseline policies, and they are robust to high false alert rates and to the investigation errors.

SaaS is a cloud-based application service that allows users to use applications that work in a cloud environment. SaaS is a subscription type, and the service expenditure varies depending on the license, the number of users, and duration of use. For efficient network management, security and cost management, accurate detection of user behavior for SaaS applications is required. In this paper, we propose a rule-based traffic analysis method for the user behavior detection. We conduct comparative experiments with signature-based method by using the real SaaS application and demonstrate the validity of the proposed method.

The attacker’s server plays an important role in sending attack orders and receiving stolen information, particularly in the more recent cyberattacks. Under these circumstances, it is important to use network-based signatures to block malicious communications in order to reduce the damage. However, in addition to blocking malicious communications, signatures are also required not to block benign communications during normal business operations. Therefore, the generation of signatures requires a high level of understanding of the business, and highly depends on individual skills. In addition, in actual operation, it is necessary to test whether the generated signatures do not interfere with benign communications, which results in high operational costs. In this paper, we propose SIGMA, a system that automatically generates signatures to block malicious communication without interfering with benign communication and then automatically evaluates the impact of the signatures. SIGMA automatically extracts the common parts of malware communication destinations by clustering them and generates multiple candidate signatures. After that, SIGMA automatically calculates the impact on normal communication based on business logs, etc., and presents the final signature to the analyst, which has the highest blockability of malicious communication and non-blockability of normal communication. Our objectives with this system are to reduce the human factor in generating the signatures, reduce the cost of the impact evaluation, and support the decision of whether to apply the signatures. In the preliminary evaluation, we showed that SIGMA can automatically generate a set of signatures that detect 100% of suspicious URLs with an over-detection rate of just 0.87%, using the results of 14,238 malware analyses and actual business logs. This result suggests that the cost for generation of signatures and the evaluation of their impact on business operations can be suppressed, which used to be a time-consuming and human-intensive process.

Managing electricity effectively also means knowing as accurately as possible when, where and how electricity is used. Detailed metering and timely allocation of consumption can help identify specific areas where energy consumption is excessive and therefore requires action and optimization. All those interested in the measurement process (distributors, sellers, wholesalers, managers, ultimately customers and new prosumer figures - producers / consumers -) have an interest in monitoring and managing energy flows more efficiently, in real time.Smart meter plays a key role in sending data containing consumer measurements to both the producer and the consumer, thanks to chain 2. It allows you to connect consumption and production, during use and the customer’s identity, allowing billing as Time-of-Use or Real-Time Pricing, and through the new two-way channel, this information is also made available to the consumer / prosumer himself, enabling new services such as awareness of energy consumption at the very moment of energy use.This is made possible by latest generation devices that "talk" with the end user, which use chain 2 and the power line for communication.However, the implementation of smart meters and related digital technologies associated with the smart grid raises various concerns, including, privacy. This paper provides a comparative perspective on privacy policies for residential energy customers, moreover, it will be possible to improve security through the blockchain for the introduction of smart contracts.

For some countries around the world, meeting demand is a serious concern. Power supply market is increasingly increasing, posing a big challenge for various countries throughout the world. The increasing expansion in the market for power needs upgrading system dependability to increase the smart grid's resilience. This smart electric grid has a sensor that analyses grid power availability and sends regular updates to the organisation. The internet is currently being utilized to monitor processes and place orders for running variables from faraway places. A large number of scanners have been used to activate electrical equipment for domestic robotics for a long period in the last several days. Conversely, if it is not correctly implemented, it will have a negative impact on cost-effectiveness as well as productivity. For something like a long time, home automation has relied on a large number of sensor nodes to control electrical equipment. Since there are so many detectors, this isn't cost-effective. In this article, develop and accept a wireless communication component and a management system suitable for managing independent efficient network units from voltage rises and voltage control technologies in simultaneous analyzing system reliability in this study. This research paper has considered secondary method to collect relevant and in-depth data related to the wireless sensor network and its usage in smart grid monitoring.

Traditional power consumption management systems are not showing enough reliability and thus, smart grid technology has been introduced to reduce the excess power wastages. In the context of smart grid systems, network communication is another term that is used for developing the network between the users and the load profiles. Cloud computing and clustering are also executed for efficient power management. Based on the facts, this research is going to identify wireless network communication systems to monitor and control smart grid power consumption. Primary survey-based research has been carried out with 62 individuals who worked in the smart grid system, tracked, monitored and controlled the power consumptions using WSN technology. The survey was conducted online where the respondents provided their opinions via a google survey form. The responses were collected and analyzed on Microsoft Excel. Results show that hybrid commuting of cloud and edge computing technology is more advantageous than individual computing. Respondents agreed that deep learning techniques will be more beneficial to analyze load profiles than machine learning techniques. Lastly, the study has explained the advantages and challenges of using smart grid network communication systems. Apart from the findings from primary research, secondary journal articles were also observed to emphasize the research findings.

The electrical grid connects all the generating stations to supply uninterruptible power to the consumers. With the advent of technology, smart sensors and communication are integrated with the existing grid to behave like a smart system. This smart grid is a two-way communication that connects the consumers and producers. It is a connected smart network that integrates electricity generation, transmission, substation, distribution, etc. In this smart grid, clean, reliable power with a high-efficiency rate of transmission is available. In this paper, a highly efficient smart management system of a smart grid with overall protection is proposed. This management system checks and monitors the parameters periodically. This future technology also develops a smart transformer with ac and dc compatibility, for self-protection and for the healing process.

This paper presents a MATLAB Graphical User Interface (GUI) based tool that determines the performance evaluation metrics of the physically unclonable functions (PUFs). The PUFs are hardware security primitives which can be utilized in several hardware security applications like integrated circuits protection, device authentication, secret key generation, and hardware obfuscation. Like any other technology approach, PUFs evaluation requires testing different performance metrics, each of which can be determined by at least one mathematical equation. The proposed tool (PUFs Tool) reads the PUF instances’ output and then computes and generates the values of the main PUFs’ performance metrics: uniqueness, reliability, uniformity, and bit-aliasing. In addition, it generates a bar code for each PUF instance considered in the evaluation process. The PUFs Tool is designed and developed using the app designer of MATLAB software 2021b.

In this study, the effect of surface treatment on the boding strength between Quad flat package (QFP) and quartz was investigated for establishing a QFP/quartz glass bonding technique. This bonding technique is necessary to prevent bond failure at the nano-artifact metrics (NAM) chip and adhesive interface against physical attacks such as counterfeiting and tampering of edge AI devices that use NAM chips. Therefore, we investigated the relationship between surface roughness and tensile strength by applying surface treatments such as vacuum ultraviolet (VUV) and Ar/O2 plasma. All QFP/quartz glass with surface treatments such as VUV and Ar/O2 plasma showed increased bond strength. Surface treatment and bonding technology for QFP and quartz glass were established to realize NAM chip mounting.

This paper belongs to a sequence of manuscripts that discuss generic and easy-to-apply security metrics for Strong PUFs. These metrics cannot and shall not fully replace in-depth machine learning (ML) studies in the security assessment of Strong PUF candidates. But they can complement the latter, serve in initial PUF complexity analyses, and are much easier and more efficient to apply: They do not require detailed knowledge of various ML methods, substantial computation times, or the availability of an internal parametric model of the studied PUF. Our metrics also can be standardized particularly easily. This avoids the sometimes inconclusive or contradictory findings of existing ML-based security test, which may result from the usage of different or non-optimized ML algorithms and hyperparameters, differing hardware resources, or varying numbers of challenge-response pairs in the training phase.This first manuscript within the abovementioned sequence treats one of the conceptually most straightforward security metrics on that path: It investigates the effects that small perturbations in the PUF-challenges have on the resulting PUF-responses. We first develop and implement several sub-metrics that realize this approach in practice. We then empirically show that these metrics have surprising predictive power, and compare our obtained test scores with the known real-world security of several popular Strong PUF designs. The latter include (XOR) Arbiter PUFs, Feed-Forward Arbiter PUFs, and (XOR) Bistable Ring PUFs. Along the way, our manuscript also suggests techniques for representing the results of our metrics graphically, and for interpreting them in a meaningful manner.

Simulations have gained paramount importance in terms of software development for wireless sensor networks and have been a vital focus of the scientific community in this decade to provide efficient, secure, and safe communication in smart cities. Network Simulators are widely used for the development of safe and secure communication architectures in smart city. Therefore, in this technical survey report, we have conducted experimental comparisons among ten different simulation environments that can be used to simulate smart-city operations. We comprehensively analyze and compare simulators COOJA, NS-2 with framework Mannasim, NS-3, OMNeT++ with framework Castalia, WSNet, TOSSIM, J-Sim, GloMoSim, SENSE, and Avrora. These simulators have been run eight times each and comparison among them is critically scrutinized. The main objective behind this research paper is to assist developers and researchers in selecting the appropriate simulator against the scenario to provide safe and secure wired and wireless networks. In addition, we have discussed the supportive simulation environments, functions, and operating modes, wireless channel models, energy consumption models, physical, MAC, and network-layer protocols in detail. The selection of these simulation frameworks is based on features, literature, and important characteristics. Lastly, we conclude our work by providing a detailed comparison and describing the pros and cons of each simulator.

This document takes an in-depth approach to identify WhatsApp's Security risk management, governance and controls. WhatsApp is a communication mobile application that is available on both android and IOS, recently acquired by Facebook and allows us to stay connected. This document identifies all necessary assets, threats, vulnerabilities, and risks to WhatsApp and further provides mitigations and security controls to possibly utilize and secure the application.

The article deals with the issues of improving the quality of corporate information systems functioning and ensuring the information security of financial organizations that have a complex structure and serve a significant number of customers. The formation of the company's informational system and its integrated information security system is studied based on the process approach, methods of risk management and quality management. The risks and threats to the security of the informational system functioning and the quality of information support for customer service of a financial organization are analyzed. The methods and tools for improving the quality of information services and ensuring information security are considered on the example of an organization for social insurance. Recommendations are being developed to improve the quality of the informational system functioning in a large financial company.

Open Source Software plays an important role in many software ecosystems. Whether in operating systems, network stacks, or as low-level system drivers, software we encounter daily is permeated with code contributions from open source projects. Decentralized development and open collaboration in open source projects introduce unique challenges: code submissions from unknown entities, limited personpower for commit or dependency reviews, and bringing new contributors up-to-date in projects’ best practices & processes.In 27 in-depth, semi-structured interviews with owners, maintainers, and contributors from a diverse set of open source projects, we investigate their security and trust practices. For this, we explore projects’ behind-the-scene processes, provided guidance & policies, as well as incident handling & encountered challenges. We find that our participants’ projects are highly diverse both in deployed security measures and trust processes, as well as their underlying motivations. Based on our findings, we discuss implications for the open source software ecosystem and how the research community can better support open source projects in trust and security considerations. Overall, we argue for supporting open source projects in ways that consider their individual strengths and limitations, especially in the case of smaller projects with low contributor numbers and limited access to resources.

The Internet of Things devices is rapidly becoming widespread, as are IoT services. Their achievement has not gone unnoticed, as threats as well as attacks towards IoT devices as well as services continue to grow. Cyber attacks are not unique to IoT, however as IoT becomes more ingrained in our lives as well as communities, it is imperative to step up as well as take cyber defense seriously. As a result, there is a genuine need to protect IoT, which necessitates a thorough understanding of the dangers and attacks against IoT infrastructure. The purpose of this study is to define threat types, as well as to assess and characterize intrusions and assaults against IoT devices as well as services

Firewalls are security devices that perform network traffic filtering. They are ubiquitous in the industry and are a common method used to enforce organizational security policy. Security policy is specified on a high level of abstraction, with statements such as "web browsing is allowed only on workstations inside the office network", and needs to be translated into low-level firewall rules to be enforceable. There has been a lot of work regarding optimization, analysis and platform independence of firewall rules, but an area that has seen much less success is automatic translation of high-level security policies into firewall rules. In addition to improving rules’ readability, such translation would make it easier to detect errors.This paper surveys of over twenty papers that aim to generate firewall rules according to a security policy specified on a higher level of abstraction. It also presents an overview of similar features in modern firewall systems. Most approaches define specialized domain languages that get compiled into firewall rule sets, with some of them relying on formal specification, ontology, or graphical models. The approaches’ have improved over time, but there are still many drawbacks that need to be solved before wider application.