Biblio

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms.

The test of security primitives is particularly strategic as any bias coming from the implementation or environment can wreck havoc on the security it is intended to provide. This paper presents how some security properties are tested on leading primitives: True Random Number Generation (TRNG), Physically Unclonable Function (PUF), cryptographic primitives and Digital Sensor (DS). The test of TRNG and PUF to ensure a high level of security is mainly about the entropy assessment, which requires specific statistical tests. The security against side-channel analysis (SCA) of cryptographic primitives, like the substitution box in symmetric cryptography, is generally ensured by masking. But the hardware implementation of masking can be damaged by glitches, which create leakages on sensitive variables. A test method is to search for nets of the cryptographic netlist, which are vulnerable to glitches. The DS is an efficient primitive to detect disturbances and rise alarms in case of fault injection attack (FIA). The dimensioning of this primitive requires a precise test to take into account the environment variations including the aging.

In Industry 4.0, Information Technology (IT) and Operational Technology (OT) tend to converge further with an increasing interdependence of safety and security issues to be considered. On one hand, cyber attacks are possible which can alter implemented safety functionality leading to situations where people are harmed, serious injuries may occur or the environment gets damaged. On the other side, safety can also impact security. For instance, the misuse of a Safety Instrumented System (SIS) may force a machine or a production line to shut down resulting in a denial of service. To prevent or mitigate risks from such scenarios, this paper proposes a threat modeling technique which addresses an integrated view on safety and security. The approach is tailored to the industrial automation domain considering plausible attacks and evaluating risks based on three different metrics. The metrics selected consist of Common Vulnerability Scoring System (CVSS) used as an international standard for rating cyber security vulnerabilities, Security Level (SL) from IEC 62443 to rate cyber security risks in OT environments w.r.t. the underlying architecture, and Safety Integrity Level (SIL) from IEC 61508 to rate safety risks. Due to the variety of use cases involving the chosen metrics, the approach is also feasible for followup analyses, such as integrated safety and security assessments or audits.

Technological progress has changed our world beyond recognition. However, along with the incredible benefits and conveniences we have received new dangers and risks. Mankind is increasingly becoming hostage to information technology and cyber world. Recently, cybercrime is one of the top 10 risks to sustainable development in the world. It poses serious new challenges to global security and economy. The aim of the article is to obtain an assessment of some of the financial effects of modern IT crimes based on an analysis of the main aspects of monetary costs and the hidden economic impact of cybercrime. A multifactor regression model has been proposed to determine the contribution of the cost of the main consequences of IT incidents: business disruption, information loss, revenue loss and equipment damage caused by different types of cyberattacks worldwide in 2019 to total cost of cyberattacks. Information loss has been found to have a major impact on the total cost of cyberattacks, reducing profits and incurring additional costs for businesses. It was built a canonical model for identifying the dependence of total submission to ID ransomware, total cost of cybercrime and the main indicators of economic development for the TOP-10 countries. There is a significant correlation between two sets of indicators, in particular, it is confirmed that most cyberattacks target countries - countries with a high level of development, and the consequences of IT crimes are more significant for low-income countries.

Maintenance of Crop health is essential for the successful farming for both yield and product quality. Pest and disease in crops are serious problem to be monitored. pest and disease occur in different stages or phases of crop development. Due to introduction of genetically modified seeds the natural resistance of crops to prevent them from pest and disease is less. Major crop loss is due to pest and disease attack in crops. It damages the leaves, buds, flowers and fruits of the crops. Affected areas and damage levels of pest and diseases attacks are growing rapidly based on global climate change. Weather Conditions plays a major role in pest and disease attacks in crops. Naked eye inspection of pest and disease is complex and difficult for wide range of field. And at the same time taking lab samples to detect disease is also inefficient and time-consuming process. Early identification of diseases is important to take necessary actions for preventing crop loss and to avoid disease spreads. So, Timely and effective monitoring of crop health is important. Several technologies have been developed to detect pest and disease in crops. In this paper we discuss the various technologies implemented by using AI and Deep Learning for pest and disease detection. And also, briefly discusses their Advantages and limitations on using certain technology for monitoring of crops.

The Government of Indonesia has implemented an Electronic-Based Government System (SPBE) to provide fast and unlimited services by distance, space, and time. The Case Investigation Information System (SIPP) is a service for the public that requires good integration of information so that legal services are provided quickly and easily. In 2020 court websites experienced many hacker attacks based on data from internal court sources, at least there were more than 20 courts throughout Indonesia that were affected by damage to both the website, the SIPP web feature, and the court library featured. Based on the Minister of Communication and Informatics Regulation No. 4 of 2016 in article 7, every SPBE organizer must implement information security following the information security standards described by the Ministry of Communication and Information. Since this regulation is conceived to minimize the risk of the security breach on SPBE, and there is such numerous security breach in 2020 therefore this indicates poorly implemented of the regulation. Based on this finding this research aims to investigate which area is neglected. To achieve this, this research employs gap analysis using ISO/IEC 27001:2013and maturity level to reveal the neglected areas in information security. Current findings show there are SIPP users that still use weak passwords and lack of awareness of top management personnel in several information security areas such as securing sensitive documents or policies that regulate information security specifically for SIPP. These findings entails that the security management in Court is most likely at the initiative level.

Today cyber-attacks to critical infrastructures can perform outages, economical loss, physical damage to people and the environment, among many others. In particular, the smart grid is one of the main targets. In this paper, we develop and evaluate software detectors for integrity attacks to smart meter readings. The detectors rely upon different techniques and models, such as autoregressive models, clustering, and neural networks. Our evaluation considers different “attack scenarios”, then resembling the plethora of attacks found in last years. Starting from previous works in the literature, we carry out a detailed experimentation and analysis, so to identify which “detectors” best fit for each “attack scenario”. Our results contradict some findings of previous works and also offer a light for choosing the techniques that can address best the attacks to smart meters.

Aiming at the working mechanism of optical backbone network, based on the theory of complex network, the invulnerability evaluation index of optical backbone network is extracted from the physical topology of optical backbone network and the degree of bandwidth satisfaction, finally, the invulnerability evaluation model of optical backbone network is established. At the same time, the evaluation model is verified and analyzed with specific cases, through the comparison of 4 types of attack, the results show that the number of deliberate point attacks ( DP) is 16.7% lower than that of random point attacks ( RP) when the critical collapse state of the network is reached, and the number of deliberate edge attacks ( DE) is at least 10.4% lower than that of random edge attacks ( RE). Therefore, evaluating the importance of nodes and edges and strengthening the protection of key nodes and edges can help optical network effectively resist external attacks and significantly improve the anti-damage ability of optical network, which provides theoretical support for the anti-damage evaluation of optical network and has certain practical significance for the upgrade and reconstruction of optical network.

Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) use advanced computing, sensors, control systems, and communication networks to monitor and control industrial processes and distributed assets. The increased connectivity of these systems to corporate networks has exposed them to new security threats and made them a prime target for cyber-attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent intensified sophisticated attacks on these systems have stressed the importance of methodologies and tools to assess the security risks of Industrial Control Systems (ICS). In this paper, we propose a novel game theory model and Monte Carlo simulations to assess the cybersecurity risks of an exemplary industrial control system under realistic assumptions. We present five game enrollments where attacker and defender agents make different preferences and we analyze the final outcome of the game. Results show that a balanced defense with uniform budget spending is the best strategy against a look-ahead attacker.

One of the main threats to the underwater communication cables identified in the recent years is possible tampering or damage by malicious actors. This paper proposes a solution with explanation abilities to detect and investigate this kind of threat within the evidence theory framework. The reasoning scheme implements the traditional “opportunity-capability-intent” threat model to assess a degree to which a given vessel may pose a threat. The scenario discussed considers a variety of possible pieces of information available from different sources. A source quality model is used to reason with the partially reliable sources and the impact of this meta-information on the overall assessment is illustrated. Examples of uncertain relationships between the relevant variables are modelled and the constructed model is used to investigate the probability of threat of four vessels of different types. One of these cases is discussed in more detail to demonstrate the explanation abilities. Explanations about inference are provided thanks to sensitivity spaces in which the impact of the different pieces of information on the reasoning are compared.

In this paper, we propose a novel method to obtain the damage model (connectivity) of a power distribution system (PDS) based on distributed consensus algorithm. The measurement and sensing units in the distribution network are modeled as an agent with limited communication capability that exchanges the information (switch status) to reach an agreement in a consensus algorithm. Besides, a communication graph is designed for agents to run the consensus algorithm which is efficient and robust during the disaster event. Agents can dynamically communicate with the other agent based on available links that are established and solve the distributed consensus algorithm quickly to come up with the correct topology of PDS. Numerical simulations are performed to demonstrate the effectiveness of the proposed approach with the help of an IEEE 123-node test case with 3 different sub-graphs.

The transmission line tower is affected by the surface subsidence in the mined out area of coal mine, which will appear the phenomenon of subsidence, inclination and even tower collapse, threatening the operation safety of the transmission line tower in the mined out area. Therefore, a Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence is proposed. Firstly, the geometric model of the coal seam in the goaf and the structural reliability model of the transmission line tower are constructed to evaluate the safety. Then, the random forest algorithm in artificial intelligence is used to evaluate the damage of the tower, so as to take protective measures in time. Finally, a finite element simulation model of tower foundation interaction is built, and its safety (force) and damage identification are experimentally analyzed. The results show that the proposed method can ensure high accuracy of damage assessment and reliable judgment of transmission line tower safety within the allowable error.

Routing protocol running over low power and lossy networks (RPL) is currently one of the main routing protocols for the Internet of Things (IoT). This protocol has some vulnerabilities that can be exploited by attackers to change its behavior and deteriorate its performance. In the RPL rank attack, a malicious node announces a wrong rank, which leads the neighboring’s nodes to choose this node as a preferred parent. In this study, we used different metrics to assess RPL protocol in the presence of misbehaving nodes, namely the overhead, convergence time, energy consumption, preferred parent changes, and network lifetime. Our simulations results show that a mobile environment is more damaged by the rank attack than a static environment.

In the past decade, cyber-attack events on the power grid have proven to be sophisticated and advanced. These attacks led to severe consequences on the grid operation, such as equipment damage or power outages. Hence, it is more critical than ever to develop tools for security assessment and detection of anomalies in the cyber-physical grid. For an extensive power grid, it is complex to analyze the causes of frequency deviations. Besides, if the system is compromised, attackers can leverage on the frequency deviation to bypass existing protection measures of the grid. This paper aims to develop a novel specification-based method to detect False Data Injection Attacks (FDIAs) in the multi-area system. Firstly, we describe the implementation of a three-area system model. Next, we assess the risk and devise several intrusion scenarios. Specifically, we inject false data into the frequency measurement and Automatic Generation Control (AGC) signals. We then develop a rule-based method to detect anomalies at the system-level. Our simulation results proves that the proposed algorithm can detect FDIAs in the system.

This paper is related to dissemination and exploitation of the InFoCPS PhD research project: Failure of Cyber-Physical Systems (CPS) may cause extensive damage. Safety standards emphasize the use of formal analysis in CPS development processes. Performance degradation assessment and estimation of lifetime of energy storage (electric batteries) are vital in supporting maintenance decisions and guaranteeing CPS reliability. Existing formal analysis techniques mainly focus on specifying energy constraints in simplified manners and checking whether systems operate within given energy bounds. Leading to overlooked energy features that impede development of trustworthy CPS. Prognostics and health management (PHM) estimate energy uncertainty and predict remaining life of systems. We aim to utilize PHM techniques to rigorously model dynamic energy behaviors; resulting models are amenable to formal analysis. This project will increase the degree of maintenance of CPS while (non)-functional requirements are preserved correctly.

Inter-networked control systems make smart buildings increasingly efficient but can lead to severe operational disruptions and infrastructure damage. It is vital the security state of smart buildings is properly assessed so that thorough and cost effective risk management can be established. This paper uniquely reports on an actual risk assessment performed in 2018 on one of the world's most densely monitored, state-of-the-art, smart buildings. From our observations, we suggest that current practice may be inadequate due to a number of challenges and deficiencies, including the lack of a recognised smart building risk assessment methodology. As a result, the security posture of many smart buildings may not be as robust as their risk assessments suggest. Crucially, we highlight a number of key recommendations for a more comprehensive risk assessment process for smart buildings. As a whole, we believe this practical experience report will be of interest to a range of smart building stakeholders.

In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.

Cyber-Physical Systems (CPS) are monitored and controlled by Supervisory Control and Data Acquisition (SCADA) systems that use advanced computing, sensors, control systems, and communication networks. At first, CPS and SCADA systems were protected and secured by isolation. However, with recent industrial technology advances, the increased connectivity of CPSs and SCADA systems to enterprise networks has uncovered them to new cybersecurity threats and made them a primary target for cyber-attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent research focuses on new methodologies for risk modeling and assessment using game theory and reinforcement learning. This paperwork proposes to frame CPS security on two different levels, strategic and battlefield, by meeting ideas from game theory and Multi-Agent Reinforcement Learning (MARL). The strategic level is modeled as imperfect information, extensive form game. Here, the human administrator and the malware author decide on the strategies of defense and attack, respectively. At the battlefield level, strategies are implemented by machine learning agents that derive optimal policies for run-time decisions. The outcomes of these policies manifest as the utility at a higher level, where we aim to reach a Nash Equilibrium (NE) in favor of the defender. We simulate the scenario of a virus spreading in the context of a CPS network. We present experiments using the MiniCPS simulator and the OpenAI Gym toolkit and discuss the results.

A prioritized cyber defense remediation plan is critical for effective risk management in cyber-physical systems (CPS). The increased integration of Information Technology (IT)/Operational Technology (OT) in CPS has to lead to the need to identify the critical assets which, when affected, will impact resilience and safety. In this work, we propose a methodology for prioritized cyber risk remediation plan that balances operational resilience and economic loss (safety impacts) in CPS. We present a platform for modeling and analysis of the effect of cyber threats and random system faults on the safety of CPS that could lead to catastrophic damages. We propose to develop a data-driven attack graph and fault graph-based model to characterize the exploitability and impact of threats in CPS. We develop an operational impact assessment to quantify the damages. Finally, we propose the development of a strategic response decision capability that proposes optimal mitigation actions and policies that balances the trade-off between operational resilience (Tactical Risk) and Strategic Risk.

Cyber breaches can result in disruption to business operations, reputation damage as well as directly affecting the financial stability of the targeted corporations, with potential impacts on future profits and stock values. Automatic network-stream monitoring becomes necessary for cyber situation awareness, and time-series anomaly detection plays an important role in network stream monitoring. This study surveyed recent research on time-series analysis methods in respect of parametric and non-parametric techniques, and popular machine learning platforms for data analysis on streaming data on both single server and cloud computing environments. We believe it provides a good reference for researchers in both academia and industry to select suitable (time series) data analysis techniques, and computing platforms, dependent on the data scale and real-time requirements.

This paper presents approach for critical energy infrastructure's (CEI) cyber resilience assessment. The CEI is the vital physical system of systems, whose accidents and failures lead to damage of economy, environment, impact on health and lives of people. The analysis of cyber incidents with Ukrainian CEI confirms the importance of the task of increasing its cyber resilience to external hostile influences and keeping of the appropriate level of functionality, safety and reliability. This paper is devoted to development of approach for CEI's cyber resilience assessment considering the important capacities of its systems (adaptivity, restoration, absorbability, preventive) and interdependencies between them. This approach is based on application of multilevel fuzzy logic models (called as logic-linguistic models, LLM) taking into consideration the data available from expert's knowledge. The comparison between risk management and resilience assurance is performed. The new risk-oriented definition of resiliency is suggested.

Smart power grid is a perfect model of Cyber Physical System (CPS) which is an important component for a comfortable life. The major concern of the electrical network is safety and reliable operation. A cyber attacker in the operation of power system would create a major damage to the entire power system structure and affect the continuity of the power supply by adversely changing its parameters. A risk assessment method is presented for evaluating the cyber security assessment of power systems taking into consideration the need for protection systems. The paper considers the impact of bus and transmission line protection systems located in substations on the cyber physical performance of power systems. The proposed method is to simulate the response of power systems to sudden attacks on various power system preset value and parameters. This paper focuses on the cyber attacks which occur in a co-ordinated way so that many power system components will be in risk. The risk can be modelled as the combined probability of power system impact due to attacks and of successful interruption into the system. Stochastic Petri Nets is employed for assessing the risks. The effectiveness of the proposed cyber security risk assessment method is simulated for a IEEE39 bus system.

Real-time supervisory control and data acquisition (SCADA) systems use remote terminal units (RTUs) to monitor and manage the flow of power at electrical substations. As their connectivity to different utility and private networks increases, RTUs are becoming more vulnerable to cyber-attacks. Some attacks seek to access RTUs to directly control power system devices with the intent to shed load or cause equipment damage. Other attacks (such as denial-of-service) target network availability and seek to block, delay, or corrupt communications between the RTU and the control center. In the most severe case, when communications are entirely blocked, the loss of an RTU can cause the power system to become unobservable. It is important to understand how losing an RTU impacts the system state (bus voltage magnitudes and angles). The system state is determined by the state estimator and serves as the input to other critical EMS applications. There is currently no systematic approach for assessing the cyber-physical impact of losing RTUs. This paper proposes a methodology for N-1 RTU cyber-physical security assessment that could benefit power system control and operation. We demonstrate our approach on the IEEE 14-bus system as well as on a synthetic 200-bus system.

The term “smart” has been used in many ways for describing systems and infrastructure such as smart city, smart home, smart grid, smart meter, etc. These systems may lie in the domain of critical security systems where security can be estimated in terms of confidentiality, integrity and some cases may involve availability for protection against the theft or damage of system resources as well as disruption of the system services. Although, in spite of, being a hot topic to enhance the quality of life, there is no concrete definition of what smart system is and what should be the characteristics of it. Thus, there is a need to identify what these systems actually are and how they can be designed securely. This work firstly attempts to describe attributes related to the smartness to define smart systems. Furthermore, we propose a secure smart system development life cycle, where the security is weaved at all the development phase of smart systems according to principles, guidelines, attack patterns, risk, vulnerability, exploits, and defined rules. Finally, the comparative study is performed for evaluation of traditional security modeling techniques for early assessment of threats and risks in smart systems.

Critical infrastructures of all countries unites common cyberspace. In this space, there are many threats that can disrupt the security of critical infrastructure in one country, but also cause damage in other countries. This is a reality that makes it necessary to agree on intergovernmental national views on the composition of critical infrastructures, an assessment of their security and protection. The article presents an overview of views on critical infrastructures of the United States, the European Union, the United Kingdom, and the Russian Federation, the purpose of which is to develop common positions.