Biblio

The Internet of Things (IoT) has transformed many application domains with realtime, continuous, automated control and information transmission. The smart grid is one such futuristic application domain in execution, with a large-scale IoT network as its backbone. By leveraging the functionalities and characteristics of IoT, the smart grid infrastructure benefits not only consumers, but also service providers and power generation organizations. The confluence of IoT and smart grid comes with its own set of challenges. The underlying cyberspace of IoT, though facilitates communication (information propagation) among devices of smart grid infrastructure, it undermines the privacy at the same time. In this paper we propose a new measure for quantifying the probability of privacy leakage based on the behaviors of the devices involved in the communication process. We construct a privacy stochastic game model based on the information shared by the device, and the access to the compromised device. The existence of Nash Equilibrium strategy of the game is proved theoretically. We experimentally validate the effectiveness of the privacy stochastic game model.

Cyber security is becoming a vital part of many information technologies and computing systems. Increasingly, High-Performance Computing systems are used in scientific research, academia and industry. High-Performance Computing applications are specifically designed to take advantage of the parallel nature of High-Performance Computing systems. Current research into High-Performance Computing systems focuses on the improvements in software development, parallel algorithms and computer systems architecture. However, there are no significant efforts in developing common High-Performance Computing security standards. Security of the High-Performance Computing resources is often an add-on to existing varied institutional policies that do not take into account additional requirements for High-Performance Computing security. Also, the users' terminals or portals used to access the High-Performance Computing resources are frequently insecure or they are being used in unprotected networks. In this paper we present Bearicade - a Data-driven Security Orchestration Automation and Response system. Bearicade collects data from the HPC systems and its users, enabling the use of Machine Learning based solutions to address current security issues in the High-Performance Computing systems. The system security is achieved through monitoring, analysis and interpretation of data such as users' activity, server requests, devices used and geographic locations. Any anomaly in users' behaviour is detected using machine learning algorithms, and would be visible to system administrators to help mediate the threats. The system was tested on a university campus grid system by administrators and users. Two case studies, Anomaly detection of user behaviour and Classification of Malicious Linux Terminal Command, have demonstrated machine learning approaches in identifying potential security threats. Bearicade's data was used in the experiments. The results demonstrated that detailed information is provided to the HPC administrators to detect possible security attacks and to act promptly.

Security and privacy in computer systems has always been an important aspect of computer engineering and will continue to grow in importance as computer systems become entrusted to handle an ever increasing amount of sensitive information. Classical exploitation techniques such as memory corruption or shell command injection have been well researched and thus there exists known design patterns to avoid and penetration testing tools for testing the robustness of programs against these types of attacks. When it comes to the notion of program security requirements being violated through indirect means referred to as side-channels, testing frameworks of quality comparable to popular memory safety or command injection tools are not available. Recent computer security research has shown that private information may be indirectly leaked through side-channels such as patterns of encrypted network traffic, CPU and motherboard noise, and monitor ambient light. This paper presents the design and evaluation of a side-channel detection and exploitation framework that follows a machine learning based plugin oriented architecture thus allowing side-channel research to be conducted on a wide-variety of side-channel sources.

End-to-end encryption is now ubiquitous on the internet. By securing network communications with TLS, parties can insure that in-transit data remains inaccessible to collection and analysis. In the IoT domain however, end-to-end encryption can paradoxically decrease user privacy, as many IoT devices establish encrypted communications with the manufacturer's cloud backend. The content of these communications remains opaque to the user and in several occasions IoT devices have been discovered to exfiltrate private information (e.g., voice recordings) without user authorization. In this paper, we propose Inspection-Friendly TLS (IF-TLS), an IoT-oriented, TLS-based middleware protocol that preserves the encryption offered by TLS while allowing traffic analysis by middleboxes under the user's control. Differently from related efforts, IF-TLS is designed from the ground up for the IoT world, adding limited complexity on top of TLS and being fully controllable by the residential gateway. At the same time it provides flexibility, enabling the user to offload traffic analysis to either the gateway itself, or cloud-based middleboxes. We implemented a stable, Python-based prototype IF-TLS library; preliminary results show that performance overhead is limited and unlikely to affect quality-of-experience.

Over the years, a number of vulnerability scoring frameworks have been proposed to characterize the severity of known vulnerabilities in software-dependent systems. These frameworks provide security metrics to support decision-making in system development and security evaluation and assurance activities. When used in this context, it is imperative that these security metrics be sound, meaning that they can be consistently measured in a reproducible, objective, and unbiased fashion while providing contextually relevant, actionable information for decision makers. In this paper, we evaluate the soundness of the security metrics obtained via several vulnerability scoring frameworks. The evaluation is based on the Method for DesigningSound Security Metrics (MDSSM). We also present several recommendations to improve vulnerability scoring frameworks to yield more sound security metrics to support the development of secure software-dependent systems.

Manufacturing limitations, configuration and maintenance flaws associated with the Internet of Things (IoT) devices have resulted in an ever-expanding attack surface. Attackers exploit IoT devices to steal private information, take part in botnets, perform Denial of Service (DoS) attacks and use their resources for the mining of cryptocurrency. In this paper, we experimentally evaluate a hypothesis that attacks on IoT devices follow the generalised Cyber Kill Chain (CKC) model. We used a medium-interaction honeypot to capture and analyse more than 30,000 attacks targeting IoT devices. We classified the steps taken by the attackers using the CKC model and extended CKC to an IoT Kill Chain (IoTKC) model. The IoTKC provides details about IoT-specific attack characteristics and attackers' activities in the exploitation of IoT devices.

Malicious login, especially lateral movement, has been a primary and costly threat for enterprises. However, there exist two critical challenges in the existing methods. Specifically, they heavily rely on a limited number of predefined rules and features. When the attack patterns change, security experts must manually design new ones. Besides, they cannot explore the attributes' mutual effect specific to login operations. We propose MLTracer, a graph neural network (GNN) based system for detecting such attacks. It has two core components to tackle the previous challenges. First, MLTracer adopts a novel method to differentiate crucial attributes of login operations from the rest without experts' designated features. Second, MLTracer leverages a GNN model to detect malicious logins. The model involves a convolutional neural network (CNN) to explore attributes of login operations, and a co-attention mechanism to mutually improve the representations (vectors) of login attributes through learning their login-specific relation. We implement an evaluation of such an approach. The results demonstrate that MLTracer significantly outperforms state-of-the-art methods. Moreover, MLTracer effectively detects various attack scenarios with a remarkably low false positive rate (FPR).

The importance of Cyber-Physical Systems (CPS) gains more and more weight in our daily business and private life. Although CPS build the backbone for major trends, like Industry 4.0 and connected vehicles, they also propose many new challenges. One major challenge can be found in achieving a high level of security within such highly connected environments, in which an unpredictable number of heterogeneous systems with often-distinctive characteristics interact with each other. In order to develop high-level security solutions, system designers must eventually know the current level of security of their specification. To this end, security metrics and scoring frameworks are essential, as they quantitatively express security of a given design or system. However, existing solutions may not be able to handle the proposed challenges of CPS, as they mainly focus on one particular system and one specific attack. Therefore, we aim to elaborate a security scoring mechanism, which can efficiently be used in CPS, while considering all essential information. We break down each system within the CPS into its core functional blocks and analyze a variety of attacks in terms of exploitability, scalability of attacks, as well as potential harm to targeted assets. With this approach, we get an overall assessment of security for the whole CPS, as it integrates the security-state of all interacting systems. This allows handling the presented complexity in CPS in a more efficient way, than existing solutions.

Balancing utility and differential privacy by shuffling or BUDS is an approach towards crowd sourced, statistical databases, with strong privacy and utility balance using differential privacy theory. Here, a novel algorithm is proposed using one-hot encoding and iterative shuffling with the loss estimation and risk minimization techniques, to balance both the utility and privacy. In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report. During empirical test of balanced utility and privacy, BUDS produces ε = 0.02 which is a very promising result. Our algorithm maintains a privacy bound of ε = ln[t/((n1-1)S)] and loss bound of c'\textbackslashtextbareln[t/((n1-1)S)]-1\textbackslashtextbar.

Privacy protection is becoming more and more important in the era of big data. Differential privacy is a rigorous and provable privacy protection method that can protect privacy for a single piece of data. But existing differential privacy online learning methods have great limitations in the scope of application and accuracy. Aiming at this problem, we propose a more general and accurate algorithm, named DPOL-CT, for differential privacy online learning. We first distinguish the difference in differential privacy protection between offline learning and online learning. Then we prove that the DPOL-CT algorithm achieves (∊, δ)-differential privacy for online learning under the Gaussian, the Laplace and the Staircase mechanisms and enjoys a sublinear expected regret bound. We further discuss the trade-off between the differential privacy level and the regret bound. Theoretical analysis and experimental results show that the DPOL-CT algorithm has good performance guarantees.

In recent years, smart grid has gradually become the common development trend of the world's power industry, and its security issues are increasingly valued by researchers. Smart grids have applied technologies such as physical control, data encryption, and authentication to improve their security, but there is still a lack of timely and effective detection methods to prevent the grid from being threatened by malicious intrusions. Aiming at this problem, a model based on machine learning to detect smart grid DoS attacks has been proposed. The model first collects network data, secondly selects features and uses PCA for data dimensionality reduction, and finally uses SVM algorithm for abnormality detection. By testing the SVM, Decision Tree and Naive Bayesian Network classification algorithms on the KDD99 dataset, it is found that the SVM model works best.

Fog Computing provides users with the cloud facilities at the network edge. It may be assumed to be a virtual platform with adequate storage., computation and processing facilities for latency-sensitive applications. The basic difference lies with the fact that this platform is decentralized in nature. In addition., the fog systems or devices process data locally., are conveyable and are capable of being installed on heterogenous hardware. This versatility in its behavior and it being at the network edge turns the attention towards the security of the users sensitive data (in transition or at rest). In this paper., the authors have emphasized on the security of the fog level in typical Fog- IoT architecture. Various security factors (along with their subfactors) persisting at fog level are identified and discussed in detail. The authors have presented a hierarchy of fog computing security factors that is expected to help in considering security in a systematic and efficient manner. Further., the authors have also ranked the same through Analytical Hierarchy Process (AHP) and compared the results with Fuzzy-AHP (F-AHP). The results are found to be highly correlated.

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.

Zigbee network security relies on symmetric cryptography based on a pre-shared secret. In the current Zigbee protocol, the network coordinator creates a network key while establishing a network. The coordinator then shares the network key securely, encrypted under the pre-shared secret, with devices joining the network to ensure the security of future communications among devices through the network key. The pre-shared secret, therefore, needs to be installed in millions or more devices prior to deployment, and thus will be inevitably leaked, enabling attackers to compromise the confidentiality and integrity of the network. To improve the security of Zigbee networks, we propose a new certificate-less Zigbee joining protocol that leverages low-cost public-key primitives. The new protocol has two components. The first is to integrate Elliptic Curve Diffie-Hellman key exchange into the existing association request/response messages, and to use this key both for link-to-link communication and for encryption of the network key to enhance privacy of user devices. The second is to improve the security of the installation code, a new joining method introduced in Zigbee 3.0 for enhanced security, by using public key encryption. We analyze the security of our proposed protocol using the formal verification methods provided by ProVerif, and evaluate the efficiency and effectiveness of our solution with a prototype built with open source software and hardware stack. The new protocol does not introduce extra messages and the overhead is as lows as 3.8% on average for the join procedure.

ARtect is an Augmented Reality application developed with Unity 3D, which envisions an educational interactive and immersive tool for architects, designers, researchers, and artists. This digital instrument renders the competency to visualize custom-made 3D models and 2D graphics in interior and exterior environments. The user-friendly interface offers an accurate insight before the materialization of any architectural project, enabling evaluation of the design proposal. This practice could be integrated into learning architectural design process, saving resources of printed drawings, and 3D carton models during several stages of spatial conception.

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.

This article describes the development of two mobile applications for learning Digital Electronics. The first application is an interactive app for iOS where you can study the different digital circuits, and which will serve as the basis for the second: a game of questions in augmented reality.

Differential privacy has emerged as a formal framework for protecting sensitive information in control systems. One key feature is that it is immune to post-processing, which means that arbitrary post-hoc computations can be performed on privatized data without weakening differential privacy. It is therefore common to filter private data streams. To characterize this setup, in this paper we present error and entropy bounds for Kalman filtering differentially private state trajectories. We consider systems in which an output trajectory is privatized in order to protect the state trajectory that produced it. We provide bounds on a priori and a posteriori error and differential entropy of a Kalman filter which is processing the privatized output trajectories. Using the error bounds we develop, we then provide guidelines to calibrate privacy levels in order to keep filter error within pre-specified bounds. Simulation results are presented to demonstrate these developments.

New technologies, such as augmented reality (AR) are used to enhance human capabilities and extend human functioning; nevertheless they may cause distraction and incorrect human functioning. Systems including socio entities (such as human) and technical entities (such as augmented reality) are called socio-technical systems. In order to do risk assessment in such systems, considering new dependability threats caused by augmented reality is essential, for example failure of an extended human function is a new type of dependability threat introduced to the system because of new technologies. In particular, it is required to identify these new dependability threats and extend modeling and analyzing techniques to be able to uncover their potential impacts. This research aims at providing a framework for risk assessment in AR-equipped socio-technical systems by identifying AR-extended human failures and AR-caused faults leading to human failures. Our work also extends modeling elements in an existing metamodel for modeling socio-technical systems, to enable AR-relevant dependability threats modeling. This extended metamodel is expected to be used for extending analysis techniques to analyze AR-equipped socio-technical systems.

Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.

The personal health record has been shared and preserved easily with cloud core storage. Privacy and security have been one of the main demerits of core CloudHealthData storage. By increasing the security concerns in this paper experimented Operative Access Regulator for Attribute Based Generalized Signcryption Using rough set theory. By using rough set theory, the classifications of the attribute have been improved as well as the compulsory attribute has been formatted for decrypting process by using reduct and core. The Generalized signcryption defined priority wise access to diminish the cost and rise the effectiveness of the proposed model. The PHR has been stored under the access priorities of Signature only, encryption only and signcryption only mode. The proposed ABGS performance fulfills the secrecy, authentication and also other security principles.

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

The position information leakage of under-water sensor networks has been widely concerned. However, the underwater environment has unique characteristics compared with the terrestrial environment, for example, the asynchronous clock, stratification compensation. Therefore, the privacy preserving localization algorithm for terrestrial is not suitable. At present, the proposed privacy preserving localization algorithm is at the cost of reducing the localization accuracy and increasing the complexity of the algorithm. In this paper, a privacy preserving localization algorithm for underwater sensor networks with ray compensation is proposed. Besides, the localization algorithm we designed hides the position information of anchor nodes, and eliminates the influence of asynchronous clock. More importantly, the positioning accuracy is improved. Finally, the simulation results show that the location algorithm with privacy preserving and without privacy preserving have the same location accuracy. In addition, the algorithm proposed in this paper greatly improves the positioning accuracy compared with the existing work.

Wireless sensor networks (WSNs) have been widely used in various applications for continuous event monitoring and detection. Dual to lack of a protected physical boundary, WSNs are vulnerable to trace-back attacks. The existing secure routing protocols are designed to protect source location privacy by increasing uncertainty of routing direction against statistic analysis on traffic flow. Nevertheless, the security has not been quantitatively measured and shown the direction of secure routing design. In this paper, we propose a theoretical security measurement scheme to define and analyze the quantitative amount of the information leakage from each eavesdropped message. Through the theoretical analysis, we identify vulnerabilities of existing routing algorithms and quantitatively compute the direction information leakage based on various routing strategy. The theoretical analysis results also indicate the direction for maximization of source location privacy.

Recently, a large amount of research studies aiming at the privacy-preserving data publishing have been conducted. We find that most K-anonymity algorithms fail to consider the characteristics of attribute values distribution in data and the contribution value differences in quasi-identifier attributes when service-oriented. In this paper, the importance of distribution characteristics of attribute values and the differences in contribution value of quasi-identifier attributes to anonymous results are illustrated. In order to maximize the utility of released data, a service-oriented adaptive anonymity algorithm is proposed. We establish a model of reaction dispersion degree to quantify the characteristics of attribute value distribution and introduce the concept of utility weight related to the contribution value of quasi-identifier attributes. The priority coefficient and the characterization coefficient of partition quality are defined to optimize selection strategies of dimension and splitting value in anonymity group partition process adaptively, which can reduce unnecessary information loss so as to further improve the utility of anonymized data. The rationality and validity of the algorithm are verified by theoretical analysis and multiple experiments.