Biblio

Experimentation focused on assessing the value of complex visualisation approaches when compared with alternative methods for data analysis is challenging. The interaction between participant prior knowledge and experience, a diverse range of experimental or real-world data sets and a dynamic interaction with the display system presents challenges when seeking timely, affordable and statistically relevant experimentation results. This paper outlines a hybrid approach proposed for experimentation with complex interactive data analysis tools, specifically for computer network traffic analysis. The approach involves a structured survey completed after free engagement with the software platform by expert participants. The survey captures objective and subjective data points relating to the experience with the goal of making an assessment of software performance which is supported by statistically significant experimental results. This work is particularly applicable to field of network analysis for cyber security and also military cyber operations and intelligence data analysis.

Analyzing multi-dimensional geospatial data is difficult and immersive analytics systems are used to visualize geospatial data and models. There is little previous work evaluating when immersive and non-immersive visualizations are the most suitable for data analysis and more research is needed.

This study proposes a machine learning method based on stream homomorphic encryption computing for improving security and reducing computational time. A case study of mobile positioning based on k nearest neighbors ( kNN) is selected to evaluate the proposed method. The results showed the proposed method can save computational resources than others.

Detection of multi-stage threats such as Advanced Persistent Threats (APT) is extremely challenging due to their deceptive approaches. Sequential events of threats might look benign when performed individually or from different addresses. We propose a new unsupervised framework to identify patterns and correlations of malicious behaviours by analysing heterogeneous log-files. The framework consists of two main phases of data analysis to extract inner-behaviours of log-files and then the patterns of those behaviours over analysed files. To evaluate the framework we have produced a (publicly available) labelled version of the SotM43 dataset. Our results demonstrate that the framework can (i) efficiently cluster inner-behaviours of log-files with high accuracy and (ii) extract patterns of malicious behaviour and correlations between those patterns from real-world data.

Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy and their trade-offs. The distinguishing feature of DPella is a novel component which statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements.

As the number of data in various industries and government sectors is growing exponentially, the `7V' concept of big data aims to create a new value by indiscriminately collecting and analyzing information from various fields. At the same time as the ecosystem of the ICT industry arrives, big data utilization is treatened by the privacy attacks such as infringement due to the large amount of data. To manage and sustain the controllable privacy level, there need some recommended de-identification techniques. This paper exploits those de-identification processes and three types of commonly used privacy models. Furthermore, this paper presents use cases which can be adopted those kinds of technologies and future development directions.

Based on the analysis of the difficulties and pain points of privacy protection in the opening and sharing of government data, this paper proposes a new method for intelligent discovery and protection of structured and unstructured privacy data. Based on the improvement of the existing government data masking process, this method introduces the technologies of NLP and machine learning, studies the intelligent discovery of sensitive data, the automatic recommendation of masking algorithm and the full automatic execution following the improved masking process. In addition, the dynamic masking and static masking prototype with text and database as data source are designed and implemented with agent-based intelligent masking middleware. The results show that the recognition range and protection efficiency of government privacy data, especially government unstructured text have been significantly improved.

OLAP is an authoritative analytical tool in the emerging big data analytics context, with particular regards to the target distributed environments (e.g., Clouds). Here, privacy-preserving OLAP-based big data analytics is a critical topic, with several amenities in the context of innovative big data application scenarios like smart cities, social networks, bio-informatics, and so forth. The goal is that of providing privacy preservation during OLAP analysis tasks, with particular emphasis on the privacy of OLAP aggregates. Following this line of research, in this paper we provide a deep contribution on experimenting and assessing a state-of-the-art distributed privacy-preserving OLAP framework, named as SPPOLAP, whose main benefit is that of introducing a completely-novel privacy notion for OLAP data cubes.

In this New Age where information has an indispensable value for companies and data mining technologies are growing in the area of Information Technology, privacy remains a sensitive issue in the approach to the exploitation of the large volume of data generated and processed by companies. The way data is collected, handled and destined is not yet clearly defined and has been the subject of constant debate by several areas of activity. This literature review gives an overview of privacy in the era of Business Analytics and Big Data in different timelines, the opportunities and challenges faced, aiming to broaden discussions on a subject that deserves extreme attention and aims to show that, despite measures for data protection have been created, there is still a need to discuss the subject among the different parties involved in the process to achieve a positive ideal for both users and companies.

In recent years, with the progress of data analysis methods utilizing artificial intelligence (AI) technology, concepts of smart cities collecting data from IoT devices and creating values by analyzing it have been proposed. However, making sure that the data is not tampered with is of the utmost importance. One way to do this is to utilize blockchain technology to record and trace the history of the data. Park and Kim proposed ensuring the trustworthiness of the data by utilizing an IoT device with a trusted execution environment (TEE). Also, Guan et al. proposed authenticating an IoT device and mediating data using a TEE. For the authentication, they use the physically unclonable function of the IoT device. Usually, IoT devices suffer from the lack of resources necessary for creating transactions for the blockchain ledger. In this paper, we present a secure protocol in which a TEE acts as a proxy to the IoT devices and creates the necessary transactions for the blockchain. We use an authenticated encryption method on the data transmission between the IoT device and TEE to authenticate the device and ensure the integrity and confidentiality of the data generated by the IoT devices.

Real-time discovery of all different types of unique attributes within unstructured data is a challenging problem to solve when dealing with multiple petabytes of unstructured data volume everyday. Popular discovery solutions such as the creation of offline jobs to uniquely identify attributes or running aggregation queries on raw data sets limits real time discovery use-cases and often results into poor resource utilization. The discovery information must be treated as a parallel problem to just storing raw data sets efficiently onto back-end big data systems. Solving the discovery problem by creating a parallel discovery data store infrastructure has multiple benefits as it allows such to channel the actual search queries against the raw data set in much more funneled manner instead of being widespread across the entire data sets. Such focused search queries and data separation are far more performant and requires less compute and memory footprint.

The growing adoption of IoT devices in our daily life engendered a need for secure systems to safely store and analyze sensitive data as well as the real-time data processing system to be as fast as possible. The cloud services used to store and process sensitive data are often come out to be vulnerable to outside threats. Furthermore, to analyze streaming IoT data swiftly, they are in need of a fast and efficient system. The Paper will envision the aspects of complexity dealing with real time data from various devices in parallel, building solution to ingest data from different IOT devices, forming a secure platform to process data in a short time, and using various techniques of IOT edge computing to provide meaningful intuitive results to users. The paper envisions two modules of building a real time data analytics system. In the first module, we propose to maintain confidentiality and integrity of IoT data, which is of paramount importance, and manage large-scale data analytics with real-time data collection from various IoT devices in parallel. We envision a framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, end-to-end data encryption mechanism, and strong access control policies. Moreover, we design a generic framework to simplify the process of collecting and storing heterogeneous data coming from diverse IoT devices. In the second module, we envision a drone-based data processing system in real-time using edge computing and on-device computing. As, we know the use of drones is growing rapidly across many application domains including real-time monitoring, remote sensing, search and rescue, delivery of goods, security and surveillance, civil infrastructure inspection etc. This paper demonstrates the potential drone applications and their challenges discussing current research trends and provide future insights for potential use cases using edge and on-device computing.

The paper considers data security issues in vehicle-to-infrastructure communications, where vehicles stream data to a road side unit. We assume aggregated data in road side units can be stored or used for data analytics. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicle layer, where a group leader is assigned to communicate with group devices and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality of sensory data.

The security, safety and reliability of rail systems are of the utmost importance. In order to better detect and prevent anomalies, it is necessary to accurately study and analyze the network traffic and abnormal behaviors, as well as to detect and alert any anomalies if happened. This paper focuses on data analysis for anomaly detection with Wireshark and packet analysis system. An alert function is also developed to provide an alert when abnormality happens. Rail network traffic data have been captured and analyzed so that their network features are obtained and used to detect the abnormality. To improve efficiency, a packet analysis system is introduced to receive the network flow and analyze data automatically. The provision of two detection methods, i.e., the Wireshark detection and the packet analysis system together with the alert function will facilitate the timely detection of abnormality and triggering of alert in the rail network.

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.

A security breach often makes companies react by changing their attitude and approach to security within the organization. This paper presents an in-depth case study of post-breach security changes made by a company and the consequences of those changes. We employ the principles of participatory action research and humble inquiry to conduct a long-term study with employee interviews while embedded in the organization's security division. Despite an extremely high level of financial investment in security, and consistent attention and involvement from the board, the interviews indicate a significant level of friction between employees and security. In the main themes that emerged from our data analysis, a number of factors shed light on the friction: fear of another breach leading to zero risk appetite, impossible security controls making non-compliance a norm, security theatre underminining the purpose of security policies, employees often trading-off security with productivity, and as such being treated as children in detention rather than employees trying to finish their paid jobs. This paper shows that post-breach security changes can be complex and sometimes risky due to emotions often being involved. Without an approach considerate of how humans and security interact, even with high financial investment, attempts to change an organization's security behaviour may be ineffective.

Smart cities comprise multiple critical infrastructures, two of which are the power grid and communication networks, backed by centralized data analytics and storage. To effectively model the interdependencies between these infrastructures and enable a greater understanding of how communities respond to and impact them, large amounts of varied, real-world data on residential and commercial consumer energy consumption, load patterns, and associated human behavioral impacts are required. The dissemination of such data to the research communities is, however, largely restricted because of security and privacy concerns. This paper creates an opportunity for the development and dissemination of synthetic energy consumption data which is inherently anonymous but holds similarities to the properties of real data. This paper explores a framework using mixture density network (MDN) model integrated with a multi-layered Long Short-Term Memory (LSTM) network which shows promise in this area of research. The model is trained using an initial sample recorded from residential smart meters in the state of Florida, and is used to generate fully synthetic energy consumption data. The synthesized data will be made publicly available for interested users.

Operating data analysis means to analyze the operating system logs, user operation logs, various types of alarms and security relevant configurations, etc. The purpose is to find whether there is an attack event, suspicious behaviors or improper configurations. It is an important part of risk assessment for enterprise intranet. However, due to the lack of information security knowledge or relevant experience, many people do not know how to properly implement it. In this article, we provided guidance on conducting operating data analysis and how to determine the security risk with the analysis results.

In Compositional data, the relative proportions of the components contain important relevant information. In such case, Euclidian distance fails to capture variation when considered within data science models and approaches such as partial least squares discriminant analysis (PLS-DA). Indeed, the Euclidean distance assumes implicitly that the data is normally distributed which is not the case of compositional vectors. Aitchison transformation has been considered as a standard in compositional data analysis. In this paper, we consider two other transformation methods, Isometric log ratio (ILR) transformation and data-based power (alpha) transformation, before feeding the data to PLS-DA algorithm for classification [1]. In order to investigate the merits of both methods, we apply them in two challenging information system security applications namely spam filtering and intrusion detection.

Minimum-Storage Regenerating (MSR) codes have emerged as a viable alternative to Reed-Solomon (RS) codes as they minimize the repair bandwidth while they are still optimal in terms of reliability and storage overhead. Although several MSR constructions exist, so far they have not been practically implemented mainly due to the big number of I/O operations. In this paper, we analyze high-rate MDS codes that are simultaneously optimized in terms of storage, reliability, I/O operations, and repair-bandwidth for single and multiple failures of the systematic nodes. The codes were recently introduced in [1] without any specific name. Due to the resemblance between the hashtag sign \# and the procedure of the code construction, we call them in this paper HashTag Erasure Codes (HTECs). HTECs provide the lowest data-read and data-transfer, and thus the lowest repair time for an arbitrary sub-packetization level α, where α ≤ r⌈k/r⌉, among all existing MDS codes for distributed storage including MSR codes. The repair process is linear and highly parallel. Additionally, we show that HTECs are the first high-rate MDS codes that reduce the repair bandwidth for more than one failure. Practical implementations of HTECs in Hadoop release 3.0.0-alpha2 demonstrate their great potentials.

The e-government concept and healthcare have usually been studied separately. Even when and where both e-government and healthcare systems were combined in a study, the roles of e-government in healthcare have not been examined. As a result., the complementarity of the systems poses potential challenges. The interpretive approach was applied in this study. Existing materials in the areas of healthcare and e-government were used as data from a qualitative method viewpoint. Dimension of change from the perspective of the structuration theory was employed to guide the data analysis. From the analysis., six factors were found to be the main roles of e-government in the implementation and application of e-health in the delivering of healthcare services. An understanding of the roles of e-government promotes complementarity., which enhances the healthcare service delivery to the community.

Smart technologies at hand have facilitated generation and collection of huge volumes of data, on daily basis. It involves highly sensitive and diverse data like personal, organisational, environment, energy, transport and economic data. Data Analytics provide solution for various issues being faced by smart cities like crisis response, disaster resilience, emergence management, smart traffic management system etc.; it requires distribution of sensitive data among various entities within or outside the smart city,. Sharing of sensitive data creates a need for efficient usage of smart city data to provide smart applications and utility to the end users in a trustworthy and safe mode. This shared sensitive data if get leaked as a consequence can cause damage and severe risk to the city's resources. Fortification of critical data from unofficial disclosure is biggest issue for success of any project. Data Leakage Detection provides a set of tools and technology that can efficiently resolves the concerns related to smart city critical data. The paper, showcase an approach to detect the leakage which is caused intentionally or unintentionally. The model represents allotment of data objects between diverse agents using Bigraph. The objective is to make critical data secure by revealing the guilty agent who caused the data leakage.

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

The number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.

Finite-state machine (FSM) is widely used as control unit in most digital designs. Many intellectual property protection and obfuscation techniques leverage on the exponential number of possible states and state transitions of large FSM to secure a physical design with the reason that it is challenging to retrieve the FSM design from its downstream design or physical implementation without knowledge of the design. In this paper, we postulate that this assumption may not be sustainable with big data analytics. We demonstrate by applying a data mining technique to analyze sufficiently large amount of data collected from a full scan design to identify its FSM state registers. An impact metric is introduced to discriminate FSM state registers from other registers. A decision tree algorithm is constructed from the scan data for the regression analysis of the dependency of other registers on a chosen register to deduce its impact. The registers with the greater impact are more likely to be the FSM state registers. The proposed scheme is applied on several complex designs from OpenCores. The experiment results show the feasibility of our scheme in correctly identifying most FSM state registers with a high hit rate for a large majority of the designs.