Biblio

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 presents a study on detecting cyber attacks on industrial control systems (ICS) using convolutional neural networks. The study was performed on a Secure Water Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. We suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value. We applied the proposed method by using a variety of deep neural network architectures including different variants of convolutional and recurrent networks. The test dataset included 36 different cyber attacks. The proposed method successfully detected 31 attacks with three false positives thus improving on previous research based on this dataset. The results of the study show that 1D convolutional networks can be successfully used for anomaly detection in industrial control systems and outperform recurrent networks in this setting. The findings also suggest that 1D convolutional networks are effective at time series prediction tasks which are traditionally considered to be best solved using recurrent neural networks. This observation is a promising one, as 1D convolutional neural networks are simpler, smaller, and faster than the recurrent neural networks.

Browser extensions are small JavaScript, CSS and HTML programs that run inside the browser with special privileges. These programs, often written by third parties, operate on the pages that the browser is visiting, giving the user a programmatic way to configure the browser. The privacy implications that arise by allowing privileged third-party code to execute inside the users' browser are not well understood. In this paper, we develop a taint analysis framework for browser extensions and use it to perform a large scale study of extensions in regard to their privacy practices. We first present a hybrid approach to traditional taint analysis: by leveraging the fact that extension source code is available to the runtime JavaScript engine, we implement as well as enhance traditional taint analysis using information gathered from static data flow and control-flow analysis of the JavaScript source code. Based on this, we further modify the Chromium browser to support taint tracking for extensions. We analyzed 178,893 extensions crawled from the Chrome Web Store between September 2016 and March 2018, as well as a separate set of all available extensions (2,790 in total) for the Opera browser at the time of analysis. From these, our analysis flagged 3,868 (2.13%) extensions as potentially leaking privacy-sensitive information. The top 10 most popular Chrome extensions that we confirmed to be leaking privacy-sensitive information have more than 60 million users combined. We ran the analysis on a local Kubernetes cluster and were able to finish within a month, demonstrating the feasibility of our approach for large-scale analysis of browser extensions. At the same time, our results emphasize the threat browser extensions pose to user privacy, and the need for countermeasures to safeguard against misbehaving extensions that abuse their privileges.

Personally Identifiable Information (PII) is information that can be used on its own or with other information to distinguish or trace an individual's identity. To investigate an application for PII tracking, a reverse engineer has to put considerable effort to reverse engineer an application and discover what an application does with PII. To automate this process and save reverse engineers substantial time and effort, we propose PIITracker which is a new and novel tool that can track PII automatically and capture if any processes are sending PII over the network. This is made possible by 1) whole-system dynamic information flow tracking 2) monitoring specific function and system calls. We analyzed 15 popular chat applications and browsers using PIITracker, and determined that 12 of these applications collect some form of PII.

Despite recent regulations and growing user awareness, undesired browser tracking is increasing. In addition to cookies, browser fingerprinting is a stateless technique that exploits a device's configuration for tracking purposes. In particular, browser fingerprinting builds on attributes made available from Javascript and HTTP headers to create a unique and stable fingerprint. For example, browser plugins have been heavily exploited by state-of-the-art browser fingerprinters as a rich source of entropy. However, as browser vendors abandon plugins in favor of extensions, fingerprinters will adapt. We present FP-TESTER, an approach to automatically test the effectiveness of browser fingerprinting countermeasure extensions. We implement a testing toolkit to be used by developers to reduce browser fingerprintability. While countermeasures aim to hinder tracking by changing or blocking attributes, they may easily introduce subtle side-effects that make browsers more identifiable, rendering the extensions counterproductive. FP-TESTER reports on the side-effects introduced by the countermeasure, as well as how they impact tracking duration from a fingerprinter's point-of-view. To the best of our knowledge, FP-TESTER is the first tool to assist developers in fighting browser fingerprinting and reducing the exposure of end-users to such privacy leaks.

Browser fingerprinting has emerged as a technique to track users without their consent. Unlike cookies, fingerprinting is a stateless technique that does not store any information on devices, but instead exploits unique combinations of attributes handed over freely by browsers. The uniqueness of fingerprints allows them to be used for identification. However, browser fingerprints change over time and the effectiveness of tracking users over longer durations has not been properly addressed. In this paper, we show that browser fingerprints tend to change frequently-from every few hours to days-due to, for example, software updates or configuration changes. Yet, despite these frequent changes, we show that browser fingerprints can still be linked, thus enabling long-term tracking. FP-STALKER is an approach to link browser fingerprint evolutions. It compares fingerprints to determine if they originate from the same browser. We created two variants of FP-STALKER, a rule-based variant that is faster, and a hybrid variant that exploits machine learning to boost accuracy. To evaluate FP-STALKER, we conduct an empirical study using 98,598 fingerprints we collected from 1, 905 distinct browser instances. We compare our algorithm with the state of the art and show that, on average, we can track browsers for 54.48 days, and 26 % of browsers can be tracked for more than 100 days.

In this paper, we present two web-based attacks against local IoT devices that any malicious web page or third-party script can perform, even when the devices are behind NATs. In our attack scenario, a victim visits the attacker's website, which contains a malicious script that communicates with IoT devices on the local network that have open HTTP servers. We show how the malicious script can circumvent the same-origin policy by exploiting error messages on the HTML5 MediaError interface or by carrying out DNS rebinding attacks. We demonstrate that the attacker can gather sensitive information from the devices (e.g., unique device identifiers and precise geolocation), track and profile the owners to serve ads, or control the devices by playing arbitrary videos and rebooting. We propose potential countermeasures to our attacks that users, browsers, DNS providers, and IoT vendors can implement.

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.

Privacy issues can be difficult for end-users to understand and are therefore a key concern for information-sharing systems. This paper describes a deployment of fifteen Bluetooth-beacon-enabled 'creatures' spread across London's Queen Elizabeth Olympic Park, which initiate conversations on mobile phones in their vicinity via push notifications. Playing on the common assumption that neutral public settings promote anonymity, users' willingness to converse with personified chatbots is used as a proxy for understanding their inclination to share personal and potentially disclosing information. Each creature is linked to a conversational agent that asks for users' memories and their responses are then shared with other creatures in the network. This paper presents the design of an interactive device used to test users' awareness of how their information propagates to others.

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.

Collaborative smart services provide functionalities which exploit data collected from different sources to provide benefits to a community of users. Such data, however, might be privacy sensitive and their disclosure has to be avoided. In this paper, we present a distributed multi-tier framework intended for smart-environment management, based on usage control for policy evaluation and enforcement on devices belonging to different collaborating entities. The proposed framework exploits secure multi-party computation to evaluate policy conditions without disclosing actual value of evaluated attributes, to preserve privacy. As reference example, a smart-grid use case is presented.

In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can offer a cost-effective power management and supply alternative to national power grid connections. RCSMG architectures handle communications over distributed lossy networks to minimize operation costs. However, the unreliable nature of lossy networks makes privacy an important consideration. Existing anonymisation works on data perturbation work mainly by distortion with additive noise. Apply these solutions to RCSMGs is problematic, because deliberate noise additions must be distinguishable both from system and adversarial generated noise. In this paper, we present a brief survey of privacy risks in RCSMGs centered on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.

The increasing deployment of smart meters at individual households has significantly improved people's experience in electricity bill payments and energy savings. It is, however, still challenging to guarantee the accurate detection of attacked meters' behaviors as well as the effective preservation of users'privacy information. In addition, rare existing research studies jointly consider both these two aspects. In this paper, we propose a Privacy-Preserving energy Theft Detection scheme (PPTD) to address the energy theft behaviors and information privacy issues in smart grid. Specifically, we use a recursive filter based on state estimation to estimate the user's energy consumption, and detect the abnormal data. During data transmission, we use the lightweight NTRU algorithm to encrypt the user's data to achieve privacy preservation. Security analysis demonstrates that in the PPTD scheme, only authorized units can transmit/receive data, and data privacy are also preserved. The performance evaluation results illustrate that our PPTD scheme can significantly reduce the communication and computation costs, and effectively detect abnormal users.

In the smart grid, residents' electricity usage needs to be periodically measured and reported for the purpose of better energy management. At the same time, real-time collection of residents' electricity consumption may unfavorably incur privacy leakage, which has motivated the research on privacy-preserving aggregation of electricity readings. Most previous studies either rely on a trusted third party (TTP) or suffer from expensive computation. In this paper, we first reveal the privacy flaws of a very recent scheme pursing privacy preservation without relying on the TTP. By presenting concrete attacks, we show that this scheme has failed to meet the design goals. Then, for better privacy protection, we construct a new scheme called PMDA, which utilizes Shamir's secret sharing to allow smart meters to negotiate aggregation parameters in the absence of a TTP. Using only lightweight cryptography, PMDA efficiently supports multi-functional aggregation of the electricity readings, and simultaneously preserves residents' privacy. Theoretical analysis is provided with regard to PMDA's security and efficiency. Moreover, experimental data obtained from a prototype indicates that our proposal is efficient and feasible for practical deployment.

With the advancement of Technology, the existing electric grids are shifting towards smart grid. The smart grids are meant to be effective in power management, secure and safe in communication and more importantly, it is favourable to the environment. The smart grid is having huge architecture it includes various stakeholders that encounter challenges in the name of authorisation and authentication. The smart grid has another important issue to deal with that is securing the communication from varieties of cyber-attacks. In this paper, we first discussed about the challenges in the smart grid data communication and later we surveyed the existing cryptographic algorithm and presented comparative work on certain factors for existing working cryptographic algorithms This work gives insight conclusion to improve the working scheme for data security and Privacy preservation of customer who is one of the stack holders. Finally, with the comparative work, we suggest a direction of future work on improvement of working algorithms for secure and safe data communication in a smart grid.

Recently, IoT, 5G mobile, big data, and artificial intelligence are increasingly used in the real world. These technologies are based on convergenced in Cyber Physical System(Cps). Cps technology requires core technologies to ensure reliability, real-time, safety, autonomy, and security. CPS is the system that can connect between cyberspace and physical space. Cyberspace attacks are confused in the real world and have a lot of damage. The personal information that dealing in CPS has high confidentiality, so the policies and technique will needed to protect the attack in advance. If there is an attack on the CPS, not only personal information but also national confidential data can be leaked. In order to prevent this, the risk is measured using the Factor Analysis of Information Risk (FAIR) Model, which can measure risk by element for situational awareness in CPS environment. To reduce risk by preventing attacks in CPS, this paper measures risk after using the concept of Crime Prevention Through Environmental Design(CPTED).

The alarming rate of big data usage in the cloud makes data exposed easily. Cloud which consists of many servers linked to each other is used for data storage. Having owned by third parties, the security of the cloud needs to be looked at. Risks of storing data in cloud need to be checked further on the severity level. There should be a way to access the risks. Thus, the objective of this paper is to use SLR so that we can have extensive background of literatures on risk assessment for big data in cloud computing environment from the perspective of security, privacy and trust.

Combining conventional power networks and information communication technologies forms smart grid concept. Researches on the evolution of conventional power grid system into smart grid continue thanks to the development of communication and information technologies hopefully. Testing of smart grid systems is usually performed in simulation environments. However, achieving more effective real-world implementations, a smart grid application needs a real-world test environment, called testbed. Smart grid, which is the combination of conventional electricity line with information communication technologies, is vulnerable to cyber-attacks and this is a key challenge improving the smart grid. The vulnerabilities to cyber-attacks in smart grid arise from information communication technologies' nature inherently. Testbeds, which cyber-security researches and studies can be performed, are needed to find effective solutions against cyber-attacks capabilities in smart grid practices. In this paper, an evaluation of existing smart grid testbeds with the capability of cyber security is presented. First, background, domains, research areas and security issues in smart grid are introduced briefly. Then smart grid testbeds and features are explained. Also, existing security-oriented testbeds and cyber-attack testing capabilities of testbeds are evaluated. Finally, we conclude the study and give some recommendations for security-oriented testbed implementations.

This workshop aims to develop an agenda within the CHI community to address the emergence of blockchain, or distributed ledger technologies (DLTs). As blockchains emerge as a general purpose technology, with applications well beyond cryptocurrencies, DLTs present exciting challenges and opportunities for developing new ways for people and things to transact, collaborate, organize and identify themselves. Requiring interdisciplinary skills and thinking, the field of HCI is well placed to contribute to the research and development of this technology. This workshop will build a community for human-centred researchers and practitioners to present studies, critiques, design-led work, and visions of blockchain applications.

Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.

With the frequent use of Wi-Fi and hotspots that provide a wireless Internet environment, awareness and threats to wireless AP (Access Point) security are steadily increasing. Especially when using unauthorized APs in company, government and military facilities, there is a high possibility of being subjected to various viruses and hacking attacks. It is necessary to detect unauthorized Aps for protection of information. In this paper, we use RTT (Round Trip Time) value data set to detect authorized and unauthorized APs in wired / wireless integrated environment, analyze them using machine learning algorithms including SVM (Support Vector Machine), C4.5, KNN (K Nearest Neighbors) and MLP (Multilayer Perceptron). Overall, KNN shows the highest accuracy.

In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.

Data security has become a very serious parf of any organizational information system. More and more threats across the Internet has evolved and capable to deceive firewall as well as antivirus software. In addition, the number of attacks become larger and become more dificult to be processed by the firewall or antivirus software. To improve the security of the system is usually done by adding Intrusion Detection System(IDS), which divided into anomaly-based detection and signature-based detection. In this research to process a huge amount of data, Big Data technique is used. Anomaly-based detection is proposed using Learning Vector Quantization Algorithm to detect the attacks. Learning Vector Quantization is a neural network technique that learn the input itself and then give the appropriate output according to the input. Modifications were made to improve test accuracy by varying the test parameters that present in LVQ. Varying the learning rate, epoch and k-fold cross validation resulted in a more efficient output. The output is obtained by calculating the value of information retrieval from the confusion matrix table from each attack classes. Principal Component Analysis technique is used along with Learning Vector Quantization to improve system performance by reducing the data dimensionality. By using 18-Principal Component, dataset successfully reduced by 47.3%, with the best Recognition Rate of 96.52% and time efficiency improvement up to 43.16%.

Cyber threats and attacks have significantly increased in complexity and quantity throughout this past year. In this paper, the top fifteen cyber threats and trends are articulated in detail to provide awareness throughout the community and raising awareness. Specific attack vectors, mitigation techniques, kill chain and threat agents addressing Smart Digital Environments (SDE), including Internet of Things (IoT), are discussed. Due to the rising number of IoT and embedded firmware devices within ubiquitous computing environments such as smart homes, smart businesses and smart cities, the top fifteen cyber threats are being used in a comprehensive manner to take advantage of vulnerabilities and launch cyber operations using multiple attack vectors. What began as ubiquitous, or pervasive, computing is now matured to smart environments where the vulnerabilities and threats are widespread.

While advances in cyber-security defensive mechanisms have substantially prevented malware from penetrating into organizational Information Systems (IS) networks, organizational users have found themselves vulnerable to threats emanating from Advanced Persistent Threat (APT) vectors, mostly in the form of spear phishing. In this respect, the question of how an organizational user can differentiate between a genuine communication and a similar looking fraudulent communication in an email/APT threat vector remains a dilemma. Therefore, identifying and evaluating the APT vector attributes and assigning relative weights to them can assist the user to make a correct decision when confronted with a scenario that may be genuine or a malicious APT vector. In this respect, we propose an APT Decision Matrix model which can be used as a lens to build multiple APT threat vector scenarios to identify threat attributes and their weights, which can lead to systems compromise.