Biblio

Controller Area Network is the prominent communication protocol in automotive systems. Its salient features of arbitration, message filtering, error detection, data consistency and fault confinement provide robust and reliable architecture. Despite of this, it lacks security features and is vulnerable to many attacks. One of the common attacks over the CAN communication is the replay attack. It can happen even after the implementation of encryption or authentication. This paper proposes a methodology of supressing the replay attacks by implementing authenticated encryption embedded with timestamp and pre-shared initialisation vector as a primary key. The major advantage of this system is its flexibility and configurability nature where in each layer can be chosen with the help of cryptographic algorithms to up to the entire size of the keys.

The study presents the design and development of security solution pertaining to prevention of leakage of secret data that is in transit (DIT) to be deployed in a Network Gateway, the Gateway is the link connecting the Trusted Network with the Un-trusted Network. The entire solution includes, tasks such as classification of data flowing in the network, followed by the confinement of the identified data, the confinement of the identified data is done either by tagging the data or by means of encryption, however the later form is employed to achieve confinement of classified data under secret category thereby achieving confidentiality of the same. GNN is used for achieving the categorization function and the results are found to be satisfying with less processing time. The dataset that is used is the publicly available dataset and is available in its labeled format. The final deployment will however be based on the datasets that is available to meet a particular requirement of an Organization/Institution. Any organization can prepare a customized dataset suiting its requirements and train the model. The model can then be used for meeting the DLP requirement.

The paper deals with the modelling and simulation of a Fast Discharge Unit (FDU) for quench protection of the Toroidal Field (TF) magnets of the Divertor Tokamak Test, an experimental facility under design and construction in Frascati (Italy). The FDU is a safety key component that protects the superconducting magnets when a quench is detected through the fast extraction of the energy stored in superconducting magnets by adding in the TF magnets a dump (or discharge) resistor. In the paper, two different configurations of dump resistors (fixed and variable respectively) have been analysed and discussed. As a first result, it is possible to underline that the configuration with variable dump resistor is more efficient than the one with a fixed dump resistor.

Negative curvature hollow core fibers (NC-HCFs) realize great research attention due to their comparatively low losses with simplified design and fabrication simplicity. Recently, revolver type fibers that combine the NC-HCF and conventional lattice structured photonic crystal fiber (PCF) have opened up a new era in communications due to their low loss, power confinement capacity, and multi-bandwidth applications. In this study, we present a customized optical fiber design that comprises the PCF with the NC-HCF to get lowest confinement loss. Extensive numerical simulations are performed and a noteworthy low loss of 4.47×10-05dB/m at a wavelength of 0.85 μm has been recorded for the designed fiber, which is almost 4600 times lower than annular revolver type fibers. In addition, a conspicuous low loss transmission bandwidth ranging from 0.6 μm to 1.8 μm has found in this study. This may have potential applications in spectroscopy, material processing, chemical and bio-molecular sensing, security, and industry applications.

Here, we propose a simple design approach based on metal-masked titanium dioxide nanopillars, which can realize strong Mie resonance in metasurfaces and enables light confinement within itself over the range of visible wavelengths. By selecting the appropriate period and diameter of individual titanium dioxide nanopillars, the coincidence of resonance peak positions derived from excited electric and magnetic dipoles can be achived. And the optical properties in this design have been investigated with the Finite-Difference Time-Domain(FDTD) solutions.

In recent years, the computation security of edge computing has been raised as a major concern since the edge devices are often distributed on the edge of the network, less trustworthy than cloud servers and have limited storage/ computation/ communication resources. Recently, coded computing has been proposed to protect the confidentiality of computing data under edge device's independent attack and minimize the total cost (resource consumption) of edge system. In this paper, for the cooperative attack, we design an efficient scheme to ensure the information-theory security (ITS) of user's data and further reduce the total cost of edge system. Specifically, we take matrix multiplication as an example, which is an important module appeared in many application operations. Moreover, we theoretically analyze the necessary and sufficient conditions for the existence of feasible scheme, prove the security and decodeability of the proposed scheme. We also prove the effectiveness of the proposed scheme through considerable simulation experiments. Compared with the existing schemes, the proposed scheme further reduces the total cost of edge system. The experiments also show a trade-off between storage and communication.

The Internet of Things (IoT) as a demanding technology require the best features of information security for effective development of the IoT based smart city and technological activity. There are huge number of recent security threats searching for some loopholes which are ready to exploit any network. Against the back-drop of recent rapidly growing technological advancement of IoT, security-threats have become a critical challenge which demand responsive and continuous action. As privacy and security exhibit an ever-present flourishing issue, so loopholes detection and analysis are indispensable process in the network. This paper presents Block chain based security analysis of data generated from IoT devices to prevent malicious attacks and intrusion in the IoT network.

Statistical structure learning (SSL)-based approaches have been employed in the recent years to detect different types of anomalies in a variety of cyber-physical systems (CPS). Although these approaches outperform conventional methods in the literature, their computational complexity, need for large number of measurements and centralized computations have limited their applicability to large-scale networks. In this work, we propose a distributed, multi-agent maximum likelihood (ML) approach to detect anomalies in smart grid applications aiming at reducing computational complexity, as well as preserving data privacy among different players in the network. The proposed multi-agent detector breaks the original ML problem into several local (smaller) ML optimization problems coupled by the alternating direction method of multipliers (ADMM). Then, these local ML problems are solved by their corresponding agents, eventually resulting in the construction of the global solution (network's information matrix). The numerical results obtained from two IEEE test (power transmission) systems confirm the accuracy and efficiency of the proposed approach for anomaly detection.

Cyber physical systems (CPS) is a dominant technology in today's world due to its vast variety of applications. But in recent times, the alarmingly increasing breach of privacy and security in CPS is a matter of grave concern. Security and trust of CPS has become the need of the hour. Hardware Trojans are one such a malicious attack which compromises on the security of the CPS by changing its functionality or denial of services or leaking important information. This paper proposes the detection of Hardware Trojans at the system level in AES-256 decryption algorithm implemented in Atmel XMega Controller (Target Board) using a combination of side-channel power analysis and machine learning. Power analysis is done with help of ChipWhisperer-Lite board. The power traces of the golden algorithm (Hardware Trojan free) and Hardware Trojan infected algorithms are obtained and used to train the machine learning model using the 80/20 rule. The proposed machine learning model obtained an accuracy of 97%-100% for all the Trojans inserted.

Cyber-physical systems (CPS) can benefit by the use of learning enabled components (LECs) such as deep neural networks (DNNs) for perception and decision making tasks. However, DNNs are typically non-transparent making reasoning about their predictions very difficult, and hence their application to safety-critical systems is very challenging. LECs could be integrated easier into CPS if their predictions could be complemented with a confidence measure that quantifies how much we trust their output. The paper presents an approach for computing confidence bounds based on Inductive Conformal Prediction (ICP). We train a Triplet Network architecture to learn representations of the input data that can be used to estimate the similarity between test examples and examples in the training data set. Then, these representations are used to estimate the confidence of set predictions from a classifier that is based on the neural network architecture used in the triplet. The approach is evaluated using a robotic navigation benchmark and the results show that we can computed trusted confidence bounds efficiently in real-time.

Cyber-Physical Systems (CPS) often rely on network boundary defence as a primary means of access control; therefore, the compromise of one device threatens the security of all devices within the boundary. Resource and real-time constraints, tight hardware/software coupling, and decades-long service lifetimes complicate efforts for more robust, host-based access control mechanisms. Distributed capability systems provide opportunities for restoring access control to resource-owning devices; however, such a protection model requires a capability-based architecture for CPS devices as well as task compartmentalisation to be effective.This paper demonstrates hardware enforcement of network bearer tokens using an efficient translation between CHERI (Capability Hardware Enhanced RISC Instructions) architectural capabilities and Macaroon network tokens. While this method appears to generalise to any network-based access control problem, we specifically consider CPS, as our method is well-suited for controlling resources in the physical domain. We demonstrate the method in a distributed robotics application and in a hierarchical industrial control application, and discuss our plans to evaluate and extend the method.

For a class of Cyber-Physical Systems (CPSs), we address the problem of performing computations over the cloud without revealing private information about the structure and operation of the system. We model CPSs as a collection of input-output dynamical systems (the system operation modes). Depending on the mode the system is operating on, the output trajectory is generated by one of these systems in response to driving inputs. Output measurements and driving inputs are sent to the cloud for processing purposes. We capture this "processing" through some function (of the input-output trajectory) that we require the cloud to compute accurately - referred here as the trajectory utility. However, for privacy reasons, we would like to keep the mode private, i.e., we do not want the cloud to correctly identify what mode of the CPS produced a given trajectory. To this end, we distort trajectories before transmission and send the corrupted data to the cloud. We provide mathematical tools (based on output-regulation techniques) to properly design distorting mechanisms so that: 1) the original and distorted trajectories lead to the same utility; and the distorted data leads the cloud to misclassify the mode.

The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.

A cyber-physical system (CPS) is a term that implements mainly three parts, Physical elements, communication networks, and control systems. Currently, CPS includes the Internet of Things (IoT), Internet of Vehicles (IoV), and many other systems. These systems face many security challenges and different types of attacks, such as Jamming, DDoS.CPS attacks tend to be much smarter and more dynamic; thus, it needs defending strategies that can handle this level of intelligence and dynamicity. Last few years, many researchers use machine learning as a base solution to many CPS security issues. This paper provides a survey of the recent works that utilized the Q-Learning algorithm in terms of security enabling and privacy-preserving. Different adoption of Q-Learning for security and defending strategies are studied. The state-of-the-art of Q-learning and CPS systems are classified and analyzed according to their attacks, domain, supported techniques, and details of the Q-Learning algorithm. Finally, this work highlight The future research trends toward efficient utilization of Q-learning and deep Q-learning on CPS security.

A cyber-physical system (CPS) is expected to be resilient to more than one type of adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) objective in the presence of two kinds of adversaries. The first adversary has the ability to tamper with inputs to the CPS to influence satisfaction of the LTL objective. The interaction of the CPS with this adversary is modeled as a stochastic game. We synthesize a controller for the CPS to maximize the probability of satisfying the LTL objective under any policy of this adversary. The second adversary is an eavesdropper who can observe labeled trajectories of the CPS generated from the previous step. It could then use this information to launch other kinds of attacks. A labeled trajectory is a sequence of labels, where a label is associated to a state and is linked to the satisfaction of the LTL objective at that state. We use differential privacy to quantify the indistinguishability between states that are related to each other when the eavesdropper sees a labeled trajectory. Two trajectories of equal length will be differentially private if they are differentially private at each state along the respective trajectories. We use a skewed Kantorovich metric to compute distances between probability distributions over states resulting from actions chosen according to policies from related states in order to quantify differential privacy. Moreover, we do this in a manner that does not affect the satisfaction probability of the LTL objective. We validate our approach on a simulation of a UAV that has to satisfy an LTL objective in an adversarial environment.

The increasing development of smart grid facilitates that modern power grids inter-connect with each other and form a large power system, making it possible and advantageous to conduct coordinated power flow among several grids. The communication burden and privacy issue are the prominent challenges in the application of synchronous iteration power flow method. In this paper, a synchronous iterative method of power flow in inter-connected power grid considering privacy preservation is proposed. By establishing the masked model of power flow for each sub-grid, the synchronous iteration is conducted by gathering the masked model of sub-grids in the coordination center and solving the masked correction equation in a concentration manner at each step. Generally, the proposed method can concentrate the major calculation of power flow on the coordination center, reduce the communication burden and guarantee the privacy preservation of sub-grids. A case study on IEEE 118-bus test system demonstrate the feasibility and effectiveness of the proposed methodology.

The dummy-based method has been commonly used to protect the users location privacy in location-based services, since it can provide precise results and generally do not rely on a third party or key sharing. However, the close spatiotemporal correlation between the consecutively reported locations enables the adversary to identify some dummies, which lead to the existing dummy-based schemes fail to protect the users location privacy completely. To address this limit, this paper proposes a new algorithm to produce dummy location by generating dummy trajectory, which naturally takes into account of the spatiotemporal correlation all round. Firstly, the historical trajectories similar to the user's travel route are chosen as the dummy trajectories which depend on the distance between two trajectories with the help of home gateway. Then, the dummy is generated from the dummy trajectory by taking into account of time reachability, historical query similarity and the computation of in-degree/out-degree. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the users real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the users location privacy effectively and efficiently.

Smart mobile devices such as smartphones and tablets have become an integral part of our society. However, it also becomes a prime target for attackers with malicious intents. There have been a number of efforts on developing innovative courseware to promote cybersecurity education and to improve student learning; however, hands-on labs are not well developed for smart mobile devices and for mobile security topics. In this paper, we propose to design and develop a mobile security labware with smart mobile devices to promote the cybersecurity education. The integration of mobile computing technologies and smart devices into cybersecurity education will connect the education to leading-edge information technologies, motivate and engage students in security learning, fill in the gap with IT industry need, and help faculties build expertise on mobile computing. In addition, the hands-on experience with mobile app development will promote student learning and supply them with a better understanding of security knowledge not only in classical security domains but also in the emerging mobile security areas.

Machine learning (ML) techniques are changing both the offensive and defensive aspects of cybersecurity. The implications are especially strong for privacy, as ML approaches provide unprecedented opportunities to make use of collected data. Thus, education on cybersecurity and AI is needed. To investigate how AI and cybersecurity should be taught together, we look at previous studies on cybersecurity MOOCs by conducting a systematic literature review. The initial search resulted in 72 items and after screening for only peer-reviewed publications on cybersecurity online courses, 15 studies remained. Three of the studies concerned multiple cybersecurity MOOCs whereas 12 focused on individual courses. The number of published work evaluating specific cybersecurity MOOCs was found to be small compared to all available cybersecurity MOOCs. Analysis of the studies revealed that cybersecurity education is, in almost all cases, organised based on the topic instead of used tools, making it difficult for learners to find focused information on AI applications in cybersecurity. Furthermore, there is a gab in academic literature on how AI applications in cybersecurity should be taught in online courses.

Cybersecurity education is a critical component of today's computer science and IT curriculum. To provide for a highly effective cybersecurity education, we propose using machine-learning techniques to identify common learning modalities of cybersecurity students in order to optimize how cybersecurity core topics, threats, tools and techniques are taught. We test various hypothesis, e.g. that students of selected VARK learning styles will outperform their peers. The results indicate that for the class assignments in our study preference of read/write and kinesthetic modalities yielded the best results. This further indicates that specific learning instruments can be tailored for students based on their individual VARK learning styles.

Nowadays, private corporations and public institutions are dealing with constant and sophisticated cyberthreats and cyberattacks. As a general warning, organizations must build and develop a cybersecurity culture and awareness in order to defend against cybercriminals. Information Technology (IT) and Information Security (InfoSec) audits that were efficient in the past, are trying to converge into cybersecurity audits to address cyber threats, cyber risks and cyberattacks that evolve in an aggressive cyber landscape. However, the increase in number and complexity of cyberattacks and the convoluted cyberthreat landscape is challenging the running cybersecurity audit models and putting in evidence the critical need for a new cybersecurity audit model. This article reviews the best practices and methodologies of global leaders in the cybersecurity assurance and audit arena. By means of the analysis of the current approaches and theoretical background, their real scope, strengths and weaknesses are highlighted looking forward a most efficient and cohesive synthesis. As a resut, this article presents an original and comprehensive cybersecurity audit model as a proposal to be utilized for conducting cybersecurity audits in organizations and Nation States. The CyberSecurity Audit Model (CSAM) evaluates and validates audit, preventive, forensic and detective controls for all organizational functional areas. CSAM has been tested, implemented and validated along with the Cybersecurity Awareness TRAining Model (CATRAM) in a Canadian higher education institution. A research case study is being conducted to validate both models and the findings will be published accordingly.

The current trend of ubiquitous device use whereby computing is becoming increasingly context-aware and personal, has created a growing concern for the protection of personal privacy. Privacy is an essential component of security, and there is a need to be able to secure personal computers and networks to minimize privacy depreciation within cyberspace. Human error has been recognized as playing a major role in security breaches: Hence technological solutions alone cannot adequately address the emerging security and privacy threats. Home users are particularly vulnerable to cybersecurity threats for a number of reasons, including a particularly important one that our research seeks to address: The lack of cybersecurity education. We argue that research seeking to address the human element of cybersecurity should not be limited only to the design of more usable technical security mechanisms, but should be extended and applied to offering appropriate training to all stakeholders within cyberspace.

The purpose of this paper is threefold. First, it makes the case for incorporating cybersecurity principles into undergraduate Engineering Technology Education and for incorporating Industrial Control Systems (ICS) principles into undergraduate Information Technology (IT)/Cybersecurity Education. Specifically, the paper highlights the knowledge/skill gap between engineers and IT/Cybersecurity professionals with respect to the cybersecurity of the ICS. Secondly, it identifies several areas where traditional IT systems and ICS intercept. This interception not only implies that ICS are susceptible to the same cyber threats as traditional IT/IS but also to threats that are unique to ICS. Subsequently, the paper identifies several areas where cybersecurity principles can be applied to ICS. By incorporating cybersecurity principles into Engineering Technology Education, the paper hopes to provide IT/Cybersecurity and Engineering Students with (a) the theoretical knowledge of the cybersecurity issues associated with administering and operating ICS and (b) the applied technical skills necessary to manage and mitigate the cyber risks against these systems. Overall, the paper holds the promise of contributing to the ongoing effort aimed at bridging the knowledge/skill gap with respect to securing ICS against cyber threats and attacks.

As the worldwide demand for cybersecurity-trained professionals continues to grow, the need to understand and define what cybersecurity education really means at the college or university level. Given the relative infancy of these efforts to define undergraduate cybersecurity programs, the panelists will present different perspectives on how such programs can be structured. They will then engage with the audience to explore additional viewpoints on cybersecurity, and work toward a shared understanding of undergraduate cybersecurity programs.

This research-to-practice work-in-progress addresses a new approach to cybersecurity education. The cyber security skills shortage is reaching prevalent proportions. The consensus in the STEM community is that the problem begins at k-12 schools with too few students interested in STEM subjects. One way to ensure a larger pipeline in cybersecurity is to train more high school teachers to not only teach cybersecurity in their schools or integrate cybersecurity concepts in their classrooms but also to promote IT security as an attractive career path. The proposed research will result in developing a unique and novel curriculum and scalable program in the area of cybersecurity and a set of powerful tools for a fun learning experience in cybersecurity education. In this project, we are focusing on the potential to advance research agendas in cybersecurity and train the future generation with cybersecurity skills and answer fundamental research questions that still exist in the blended learning methodologies for cybersecurity education and assessment. Leadership and entrepreneurship skills are also added to the mix to prepare students for real-world problems. Delivery methods, timing, format, pacing and outcomes alignment will all be assessed to provide a baseline for future research and additional synergy and integration with existing cybersecurity programs to expand or leverage for new cybersecurity and STEM educational research. This is a new model for cybersecurity education, leadership, and entrepreneurship and there is a possibility of a significant leap towards a more advanced cybersecurity educational methodology using this model. The project will also provide a prototype for innovation coupled with character-building and ethical leadership.