Biblio

IT technology is a vital part of our everyday life and society. Additionally, as it is present in strategic domains like the military, healthcare or critical infrastructure, the aspect of protection, i.e. cybersecurity is of utmost importance. In recent years, the demand for cybersecurity experts is exponentially rising. Additionally, the field of cybersecurity is very much interdisciplinary and therefore requires a broad set of skills. Renowned organisations as ACM or IEEE have recognized the importance of cybersecurity experts and proposed guidelines for higher education training of such professionals. This paper presents an overview of a cybersecurity education model from the Information Systems and Information Technology perspective together with a good example and experience of the University of Maribor. The presented education model is shaped according to the guidelines by the Joint Task Force on Cybersecurity Education and the expectations of the Slovene industry regarding the knowledge and skills their future employees should possess.

This research conducted a security evaluation website with Penetration Testing terms. This Penetration testing is performed using the Man-In-The-Middle Attack method. This method is still widely used by hackers who are not responsible for performing Sniffing, which used for tapping from a targeted computer that aims to search for sensitive data. This research uses some penetration testing techniques, namely SQL Injection, XSS (Cross-site Scripting), and Brute Force Attack. Penetration testing in this study was conducted to determine the security hole (vulnerability), so the company will know about their weakness in their system. The result is 85% success for the penetration testing that finds the vulnerability on the website.

With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.

Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a standard data model and related data manipulation language. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.

As data security has become the major concern in modern storage systems with low-cost multi-level-cell (MLC) flash memories, it is not trivial to realize data sanitization in such a system. Even though some existing works employ the encryption or the built-in erase to achieve this requirement, they still suffer the risk of being deciphered or the issue of performance degradation. In contrast to the existing work, a fast sanitization scheme is proposed to provide the highest degree of security for data sanitization; that is, every old version of data could be immediately sanitized with zero live-data-copy overhead once the new version of data is created/written. In particular, this scheme further considers the reliability issue of MLC flash memories; the proposed scheme includes a one-shot sanitization design to minimize the disturbance during data sanitization. The feasibility and the capability of the proposed scheme were evaluated through extensive experiments based on real flash chips. The results demonstrate that this scheme can achieve the data sanitization with zero live-data-copy, where performance overhead is less than 1%.

Cloud storage service makes it very convenient for people to access and share data. At the same time, the confidentiality and privacy of user data is also facing great challenges. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme is widely considered to be the most suitable security access control technology for cloud storage environment. Aiming at the problem of privacy leakage caused by single-cloud CP-ABE which is commonly adopted in the current schemes, this paper proposes a privacy-preserving CP-ABE access control scheme using multi-cloud architecture. By improving the traditional CP-ABE algorithm and introducing a proxy to cut the user's private key, it can ensure that only a part of the user attribute set can be obtained by a single cloud, which effectively protects the privacy of user attributes. Meanwhile, the intermediate logical structure of the access policy tree is stored in proxy, and only the leaf node information is stored in the ciphertext, which effectively protects the privacy of the access policy. Security analysis shows that our scheme is effective against replay and man-in-the-middle attacks, as well as user collusion attack. Experimental results also demonstrates that the multi-cloud CP-ABE does not significantly increase the overhead of storage and encryption compared to the single cloud scheme, but the access control overhead decreases as the number of clouds increases. When the access policy is expressed with a AND gate structure, the decryption overhead is obviously less than that of a single cloud environment.

With the advancement in technology, inclusion of Information and Communication Technology (ICT) in the conventional Electrical Power Grid has become evident. The combination of communication system with physical system makes it cyber-physical system (CPS). Though the advantages of this improvement in technology are numerous, there exist certain issues with the system. Security and privacy concerns of a CPS are a major field and research and the insight of which is content of this paper.

Wide bandgap power semiconductors are key enablers for increasing the power density of switch-mode power supplies. However, they require new gate drive technologies. This paper examines and characterizes a fabricated gate-driver in a class-E resonant inverter. The gate-driver's total area of 1.2mm2 includes two high-voltage transistors for gate-driving, integrated complementary metal-oxide-semiconductor (CMOS) gate-drivers, high-speed floating level-shifter and reset circuitry. A prototype printed circuit board (PCB) was designed to assess the implications of an electrostatic discharge (ESD) diode, its parasitic capacitance and package bondwire connections. The parasitic capacitance was estimated using its discharge time from an initial voltage and the capacitance is 56.7 pF. Both bondwires and the diode's parasitic capacitance is neglegible. The gate-driver's functional behaviour is validated using a parallel LC resonant tank resembling a self-oscillating gate-drive. Measurements and simulations show the ESD diode clamps the output voltage to a minimum of -2V.

Cloud computing is an emerging technology that can provide organizations, enterprises and governments with cheaper, more convenient and larger scale computing resources. However, cloud computing will bring potential risks and threats, especially on security and privacy. We make a survey on potential threats and risks and existing solutions on cloud security and privacy. We also put forward some problems to be addressed to provide a secure cloud computing environment.

As the Internet of Things (IoT) era raise, billions of additional connected devices in new locations and applications will create new challenges. Security and privacy are among the major challenges in IoT as any breaches and misuse in those aspects will have the adverse impact on users. Among many factors that determine the security of any system, human factor is the most important aspect to be considered; as it is renowned that human is the weakest link in the information security cycle. Experts express the need to increase cyber resilience culture and a focus on the human factors involved in cybersecurity to counter cyber risks. The aim of this study is to propose a conceptual model to improve cyber resilience in IoT users that is adapted from a model in public health sector. Cyber resilience is improved through promoting security behavior by gathering the existing knowledge and gain understanding about every contributing aspects. The proposed approach is expected to be used as foundation for government, especially in Indonesia, to derive strategies in improving cyber resilience of IoT users.

This paper proposes a content-centric privacy (CCP) model that enables a privacy-preserving monitoring services in surveillance systems without cloud dependency. We design a simple yet powerful method that could not be obtained from a cloud-like system. The CCP model includes two key ideas: (1) the separation of the private data (i.e., target object images) from the public data (i.e., background images), and (2) the service authentication with the classification model. Deploying the CCP model over ICN enables the privacy central around the content itself rather than relying on a cloud system. Our preliminary analysis shows that the ICN-based CCP model can preserve privacy with respect to the W3 -privacy in which the private information of target object are decoupled from the queries and cameras.

Today an increasing number of consumer devices such as head phones, wearables, light bulbs and even baseball bats, are Bluetooth-enabled thanks to the widespread support of the technology by phone manufacturers and mobile operating system vendors. The ability for any device to seamlessly connect and exchange information with smartphones via Bluetooth Low Energy (BLE) protocol promises unlimited room for innovation. However, it also brings about new privacy challenges. We show that the BLE protocol together with the Bluetooth permission model implemented in the Android and iOS operating systems can be used for cross-app tracking unbeknownst to the individuals. Specifically, through experiments and analyses based on real-world smartphone data we show that by listening to advertising packets broadcasted by nearby BLE-enabled devices and recording information contained in them, app developers can derive fairly unique "fingerprints" for their users, which can be used for cross-app tracking, i.e., linking pseudonymous users of different apps to each other. We demonstrate that privacy protections put in place by the Bluetooth Special Interest Group, Google, and Apple are not sufficient to prevent such fingerprinting or to make cross-app tracking difficult to execute. Our main contribution is to demonstrate the feasibility of cross-app tracking using nearby BLE and raise awareness that changes are needed in order to prevent it from becoming widespread. We also propose mitigation strategies to decrease the feasibility of tracking using nearby BLE devices while preserving the utility of the BLE technology.

The privacy leakage resulting from location-based service (LBS) has become a critical issue. To preserve user privacy, many previous studies have investigated to prevent LBS servers from user privacy theft. However, they only consider whether the peers are innocent or malicious but ignore the relationship between the peers, whereas such a relationship between each pairwise of users affects the privacy leakage tremendously. For instance, a user has less concern of privacy leakage from a social friend than a stranger. In this paper, we study cyber-physical-social (CPS) aware method to address the privacy preserving in the case that not only LBS servers but also every other participant in the network has the probability to be malicious. Furthermore, by exploring the physical coupling and social ties among users, we construct CPS-aware privacy utility maximization (CPUM) game. We then study the potential Nash equilibrium of the game and show the existence of Nash equilibrium of CPUM game. Finally, we design a CPS-aware algorithm to find the Nash equilibrium for the maximization of privacy utility. Extensive evaluation results show that the proposed approach reduces privacy leakage by 50% in the case that malicious servers and users exist in the network.

In recent years, the big-data approach has become important within various business operations and sales judgment tactics. Contrarily, numerous privacy problems limit the progress of their analysis technologies. To mitigate such problems, this paper proposes several privacy-preserving methods, i.e., anonymization, extreme value record elimination, fully encrypted analysis, and so on. However, privacy-cracking fears still remain that prevent the open use of big-data by other, external organizations. We propose a big-data reconstruction method that does not intrinsically use privacy data. The method uses only the statistical features of big-data, i.e., its attribute histograms and their correlation coefficients. To verify whether valuable information can be extracted using this method, we evaluate the data by using Self Organizing Map (SOM) as one of the big-data analysis tools. The results show that the same pieces of information are extracted from our data and the big-data.

In recent years, service providers, such as mobile operators providing wireless services, collected location data in enormous extent with the increase of the usages of mobile phones. Vertical businesses, such as banks, may want to use this location information for their own scenarios. However, service providers cannot directly provide these private data to the vertical businesses because of the privacy and legal issues. In this demo, we show how privacy preserving solutions can be utilized using such location-based queries without revealing each organization's sensitive data. In our demonstration, we used partially homomorphic cryptosystem in our protocols and showed practicality and feasibility of our proposed solution.

The internet-of-things (IoT) consists of embedded devices and their networks of communication as they form decentralized frameworks of ubiquitous computing services. Within such decentralized systems the potential for malicious actors to impact the system is significant, with far-reaching consequences. Hence this work addresses the challenge of providing IoT systems engineers with a framework to elicit privacy and security design considerations, specifically for indoor adaptive smart environments. It introduces a new ambient intelligence indoor adaptive environment framework (CORE) which leverages multiple forms of data, and aims to elicit the privacy and security needs of this representative system. This contributes both a new adaptive IoT framework, but also an approach to systematically derive privacy and security design requirements via a combined and modified OCTAVE-Allegro and Privacy-by-Design methodology. This process also informs the future developments and evaluations of the CORE system, toward engineering more secure and private IoT systems.

Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.

Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.

Bluetooth is a popular wireless communication technology that is available on most mobile devices. Although Bluetooth includes security and privacy preserving mechanisms, we show that a Bluetooth harmless inherent request-response mechanism can taint users privacy. More specifically, we introduce a timing attack that can be triggered by a remote attacker in order to infer information about a Bluetooth device state. By observing the L2CAP layer ping mechanism timing variations, it is possible to detect device state changes, for instance when the device goes in or out of the locked state. Our experimental results show that change point detection analysis of the timing allows to detect device state changes with a high accuracy. Finally, we discuss applications and countermeasures.

Low-level programming languages with weak/static type systems, such as C and C++, are vulnerable to errors relating to the misuse of memory at runtime, such as (sub-)object bounds overflows, (re)use-after-free, and type confusion. Such errors account for many security and other undefined behavior bugs for programs written in these languages. In this paper, we introduce the notion of dynamically typed C/C++, which aims to detect such errors by dynamically checking the "effective type" of each object before use at runtime. We also present an implementation of dynamically typed C/C++ in the form of the Effective Type Sanitizer (EffectiveSan). EffectiveSan enforces type and memory safety using a combination of low-fat pointers, type meta data and type/bounds check instrumentation. We evaluate EffectiveSan against the SPEC2006 benchmark suite and the Firefox web browser, and detect several new type and memory errors. We also show that EffectiveSan achieves high compatibility and reasonable overheads for the given error coverage. Finally, we highlight that EffectiveSan is one of only a few tools that can detect sub-object bounds errors, and uses a novel approach (dynamic type checking) to do so.

Sky surveys represent a fundamental data source in astronomy. Today, these surveys are moving into a petascale regime produced by modern telescopes. Due to the exponential growth of astronomical data, there is a pressing need to provide efficient astronomical query processing. Our goal is to bridge the gap between existing distributed systems and high-level languages for astronomers. In this paper, we present efficient techniques for query processing of astronomical data using ASTROIDE. Our framework helps astronomers to take advantage of the richness of the astronomical data. The proposed model supports complex astronomical operators expressed using ADQL (Astronomical Data Query Language), an extension of SQL commonly used by astronomers. ASTROIDE proposes spatial indexing and partitioning techniques to better filter the data access. It also implements a query optimizer that injects spatial-aware optimization rules and strategies. Experimental evaluation based on real datasets demonstrates that the present framework is scalable and efficient.

Location Privacy breach in Wireless Sensor Networks (WSNs) cannot be controlled by encryption techniques as all the communications are signal based. Signal strength can be analyzed to reveal many routing information. Adversary takes advantage of this and tracks the incoming packet to know the direction of the packet. With the information of location of origin of packets, the Source is also exposed which is generating packets on sensing any object. Thus, the location of subject is exposed. For protecting such privacy breaches, routing schemes are used which create anonymization or diverts the adversary. In this paper, we are using `Dummy' packets that will be inserted into real traffic to confuse the adversary. The dummy packets are such inserted that they encircle the Sink or Base Station. These Dummy packets are send with a value of TTL (Time To Live) field such that they travel only a few hops. Since adversary starts backtracking from the Sink, it will be trapped in the dummy traffic. In our protocol, we are confusing adversary without introducing any delay in packet delivery. Adversary uses two common methods for knowing the source i.e. Traffic Analysis and Back-tracing. Mathematically and experimentally, our proposal is sound for both type of methods. Overhead is also balanced as packets will not live long.

This paper presents a novel research model of identity and the use of this model to answer some interesting research questions. Information travels in the cyber world, not only bringing us convenience and prosperity but also jeopardy. Protecting this information has been a commonly discussed issue in recent years. One type of this information is Personally Identifiable Information (PII), often used to perform personal authentication. People often give PIIs to organizations, e.g., when applying for a new job or filling out a new application on a website. While the use of such PII might be necessary for authentication, giving PII increases the risk of its exposure to criminals. We introduce two innovative approaches based on our model of identity to help evaluate and find an optimal set of PIIs that satisfy authentication purposes but minimize risk of exposure. Our model paves the way for more informed selection of PIIs by organizations that collect them as well as by users who offer PIIs to these organizations.

This paper describes the achievements of project EUBra-BIGSEA, which has delivered programming models and data analytics tools for the development of distributed Big Data applications. As framework components, multiple data models are supported (e.g. data streams, multidimensional data, etc.) and efficient mechanisms to ensure privacy and security, on top of a QoS-aware layer for the smart and rapid provisioning of resources in a cloud-based environment.

This paper describes how a privacy policy framework can be extended with timing information to not only decide if requests for data are allowed at a given point in time, but also to decide for how long such permission is granted. Augmenting policy decisions with expiration information eliminates the need to reason about access permissions prior to every individual data access operation. This facilitates the application of privacy policy frameworks to protect multimedia streaming data where repeated re-computations of policy decisions are not a viable option. We show how timing information can be integrated into an existing declarative privacy policy framework. In particular, we discuss how to obtain valid expiration information in the presence of complex sets of policies with potentially interacting policies and varying timing information.