Biblio

The reverse k-nearest neighbours search is a fundamental primitive in multi-dimensional (i.e. multi-attribute) databases with applications in location-based services, online recommendations, statistical classification, pat-tern recognition, graph algorithms, computer games development, and so on. Despite the relevance and popularity of the query, no solution has yet been put forward that supports it in encrypted databases while protecting at the same time the privacy of both the data and the queries. With the outsourcing of massive datasets in the cloud, it has become urgent to find ways of ensuring the fast and secure processing of this query in untrustworthy cloud computing. This paper presents searchable encryption schemes which can efficiently and securely enable the processing of the reverse k-nearest neighbours query over encrypted multi-dimensional data, including index-based search schemes which can carry out fast query response that preserves data confidentiality and query privacy. The proposed schemes resist practical attacks operating on the basis of powerful background knowledge and their efficiency is confirmed by a theoretical analysis and extensive simulation experiments.

Vendor cybersecurity risk assessment is of critical importance to smart city infrastructure and sustainability of the autonomous mobility ecosystem. Lack of engagement in cybersecurity policies and process implementation by the tier companies providing hardware or services to OEMs within this ecosystem poses a significant risk to not only the individual companies but to the ecosystem overall. The proposed quantitative method of estimating cybersecurity risk allows vendors to have visibility to the financial risk associated with potential threats and to consequently allocate adequate resources to cybersecurity. It facilitates faster implementation of defense measures and provides a useful tool in the vendor selection process. The paper focuses on cybersecurity risk assessment as a critical part of the overall company mission to create a sustainable structure for maintaining cybersecurity health. Compound cybersecurity risk and impact on company operations as outputs of this quantitative analysis present a unique opportunity to strategically plan and make informed decisions towards acquiring a reputable position in a sustainable ecosystem. This method provides attack trees and assigns a risk factor to each vendor thus offering a competitive advantage and an insight into the supply chain risk map. This is an innovative way to look at vendor cybersecurity posture. Through a selection of unique industry specific parameters and a modular approach, this risk assessment model can be employed as a tool to navigate the supply base and prevent significant financial cost. It generates synergies within the connected vehicle ecosystem leading to a safe and sustainable economy.

Most static analyzers are monolithic applications that define their own ways to analyze source code and present the results. Therefore aggregating multiple static analyzers into a single tool or integrating a new analyzer into existing tools requires a significant amount of effort. Over the last few years, we cultivated Renraku — a static analysis model that acts as a mediator between the static analyzers and the tools that present the reports. When used by both analysis and tool developers, this single quality model can reduce the cost to both introduce a new type of analysis to existing tools and create a tool that relies on existing analyzers.

A program subject to a Return-Oriented Programming (ROP) attack usually presents an execution trace with a high frequency of indirect branches. From this observation, several researchers have proposed to monitor the density of these instructions to detect ROP attacks. These techniques use universal thresholds: the density of indirect branches that characterizes an attack is the same for every application. This paper shows that universal thresholds are easy to circumvent. As an alternative, we introduce an inter-procedural semi-context-sensitive static code analysis that estimates the maximum density of indirect branches possible for a program. This analysis determines detection thresholds for each application; thus, making it more difficult for attackers to compromise programs via ROP. We have used an implementation of our technique in LLVM to find specific thresholds for the programs in SPEC CPU2006. By comparing these thresholds against actual execution traces of corresponding programs, we demonstrate the accuracy of our approach. Furthermore, our algorithm is practical: it finds an approximate solution to a theoretically undecidable problem, and handles programs with up to 700 thousand assembly instructions in 25 minutes.

This paper presents a multilayer protection approach to guard programs against Return-Oriented Programming (ROP) attacks. Upper layers validate most of a program's control flow at a low computational cost; thus, not compromising runtime. Lower layers provide strong enforcement guarantees to handle more suspicious flows; thus, enhancing security. Our multilayer system combines techniques already described in the literature with verifications that we introduce in this paper. We argue that modern versions of x86 processors already provide the microarchitectural units necessary to implement our technique. We demonstrate the effectiveness of our multilayer protection on a extensive suite of benchmarks, which includes: SPEC CPU2006; the three most popular web browsers; 209 benchmarks distributed with LLVM and four well-known systems shown to be vulnerable to ROP exploits. Our experiments indicate that we can protect programs with almost no overhead in practice, allying the good performance of lightweight security techniques with the high dependability of heavyweight approaches.

Industrial Control Systems (ICS) have evolved in the last decade, shifting from proprietary software/hardware to contemporary embedded architectures paired with open-source operating systems. In contrast to the IT world, where continuous updates and patches are expected, decommissioning always-on ICS for security assessment can incur prohibitive costs to their owner. Thus, a solution for routinely assessing the cybersecurity posture of diverse ICS without affecting their operation is essential. Therefore, in this paper we introduce IFFSET, a platform that leverages full system emulation of Linux-based ICS firmware and utilizes fuzzing for security evaluation. Our platform extracts the file system and kernel information from a live ICS device, building an image which is emulated on a desktop system through QEMU. We employ fuzzing as a security assessment tool to analyze ICS specific libraries and find potential security threatening conditions. We test our platform with commercial PLCs, showcasing potential threats with no interruption to the control process.

Industrial Internet-of-Things has been touted as the next revolution in the industrial domain, offering interconnectivity, independence, real-time operation, and self-optimization. Integration of smart systems, however, bridges the gap between information and operation technology, creating new avenues for attacks from the cyber domain. The dismantling of this air-gap, in conjunction with the devices' long lifespan -in the range of 20-30 years-, motivates us to bring the attention of the community to emerging advanced persistent threats. We demonstrate a threat that bridges the air-gap by leaking data from memory to analog peripherals through Direct Memory Access (DMA), delivered as a firmware modification through the supply chain. The attack automatically adapts to a target device by leveraging the Device Tree and resides solely in the peripherals, completely transparent to the main CPU, by judiciously short-circuiting specific components. We implement this attack on a commercial Programmable Logic Controller, leaking information over the available LEDs. We evaluate the presented attack vector in terms of stealthiness, and demonstrate no observable overhead on both CPU performance and DMA transfer speed. Since traditional anomaly detection techniques would fail to detect this firmware trojan, this work highlights the need for industrial control system-appropriate techniques that can be applied promptly to installed devices.

Private communication over the Internet remains a challenging problem. Even if messages are encrypted, it is hard to deliver them without revealing metadata about which pairs of users are communicating. Scalable anonymity systems, such as Tor, are susceptible to traffic analysis attacks that leak metadata. In contrast, the largest-scale systems with metadata privacy require passing all messages through a small number of providers, requiring a high operational cost for each provider and limiting their deployability in practice. This paper presents Stadium, a point-to-point messaging system that provides metadata and data privacy while scaling its work efficiently across hundreds of low-cost providers operated by different organizations. Much like Vuvuzela, the current largest-scale metadata-private system, Stadium achieves its provable guarantees through differential privacy and the addition of noisy cover traffic. The key challenge in Stadium is limiting the information revealed from the many observable traffic links of a highly distributed system, without requiring an overwhelming amount of noise. To solve this challenge, Stadium introduces techniques for distributed noise generation and differentially private routing as well as a verifiable parallel mixnet design where the servers collaboratively check that others follow the protocol. We show that Stadium can scale to support 4x more users than Vuvuzela using servers that cost an order of magnitude less to operate than Vuvuzela nodes.

The information-theoretic approach to security entails harnessing the correlated randomness available in nature to establish security. It uses tools from information theory and coding and yields provable security, even against an adversary with unbounded computational power. However, the feasibility of this approach in practice depends on the development of efficiently implementable schemes. In this paper, we review a special class of practical schemes for information-theoretic security that are based on 2-universal hash families. Specific cases of secret key agreement and wiretap coding are considered, and general themes are identified. The scheme presented for wiretap coding is modular and can be implemented easily by including an extra preprocessing layer over the existing transmission codes.

Preparing recommendations for unknown users or such that correctly respond to the short-term needs of a particular user is one of the fundamental problems for e-commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session-Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN). Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real-life system with ephemeral items (identified only by the set of their attributes) for the task of top-n best recommendations.

We showcase the capabilities of V2Verifier, a new open-source software-defined radio (SDR) testbed for vehicle-to-vehicle (V2V) communications security, to expose the strengths and vulnerabilities of current V2V security systems based on the IEEE 1609.2 standard. V2Verifier supports both major V2V technologies and facilitates a broad range of experimentation with upper- and lower-layer attacks using a combination of SDRs and commercial V2V on-board units (OBUs). We demonstrate two separate attacks (jamming and replay) against Dedicated Short Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X) technologies, experimentally quantifying the threat posed by these types of attacks. We also use V2Verifier's open-source implementation to show how the 1609.2 standard can effectively mitigate certain types of attacks (e.g., message replay), facilitating further research into the security of V2V.

The paper considers the general structure of Pseudo-random binary sequence generator based on the numerical solution of chaotic differential equations. The proposed generator architecture divides the generation process in two stages: numerical simulation of the chaotic system and converting the resulting sequence to a binary form. The new method of calculation of normalization factor is applied to the conversion of state variables values to the binary sequence. Numerical solution of chaotic ODEs is implemented using semi-implicit symmetric composition D-method. Experimental study considers Thomas and Rössler attractors as test chaotic systems. Properties verification for the output sequences of generators is carried out using correlation analysis methods and NIST statistical test suite. It is shown that output sequences of investigated generators have statistical and correlation characteristics that are specific for the random sequences. The obtained results can be used in cryptography applications as well as in secure communication systems design.

As power grid control systems become increasingly automated and distributed, security has become a significant design concern. Systems increasingly expose new avenues, at a variety of levels, for attackers to exploit and enable widespread disruptions and/or surveillance. Much prior work has explored the implications of attack models focused on false data injection at the front-end of the control system (i.e. during state estimation) [1]. Instead, in this paper we focus on characterizing the inherent cyber-attack vulnerabilities with power flow. Power flow (and power flow constraints) are at the core of many applications critical to operation of power grids (e.g. state estimation, economic dispatch, contingency analysis, etc.). We propose two algorithmic approaches for characterizing the vulnerability of buses within power grids to cyber-attacks. Specifically, we focus on measuring the instability of power flow to attacks which manifest as either voltage or power related errors. Our results show that attacks manifesting as voltage errors are an order of magnitude more likely to cause instability than attacks manifesting as power related errors (and 5x more likely for state estimation as compared to power flow).

Natural disasters and cyber intrusions threaten the normal operation of the critical electric grid infrastructure. There is still no widely accepted methodology to quantify the resilience in power systems. In this work, power system resiliency refers to the ability of the system to keep provide energy to the critical load even with adverse events. A significant amount of work has been done to quantify the resilience for distribution systems. Even though critical loads are located in distribution system, transmission system play a critical role in supplying energy to distribution feeder in addition to the Distributed Energy Resources (DERs). This work focuses on developing a framework to quantify the resiliency of cyber-physical transmission systems. Quantifying the resiliency of the transmission network, is important to determine and devise suitable control mechanisms to minimize the effects of undesirable events in the power grid. The proposed metric is based on both system infrastructure and with changing operating conditions. A graphical analysis along with measure of critical parameters of the network is performed to quantify the redundancy and vulnerabilities in the physical network of the system. A similar approach is used to quantify the cyber-resiliency. The results indicate the capability of the proposed framework to quantify cyber-physical resilience of the transmission systems.

As system of systems (SoS) models become increasingly complex and interconnected a new approach is needed to capture the effects of humans within the SoS. Many real-life events have shown the detrimental outcomes of failing to account for humans in the loop. This research introduces a novel and cross-disciplinary methodology for modeling humans interacting with technologies to perform tasks within an SoS specifically within a layered physical security system use case. Metrics and formulations developed for this new way of looking at SoS termed sociotechnical SoS allow for the quantification of the interplay of effectiveness and efficiency seen in detection theory to measure the ability of a physical security system to detect and respond to threats. This methodology has been applied to a notional representation of a small military Forward Operating Base (FOB) as a proof-of-concept.

Structured Query Language Injection (SQLi) is a client-side application vulnerability that allows attackers to inject malicious SQL queries with harmful intents, including stealing sensitive information, bypassing authentication, and even executing illegal operations to cause more catastrophic damage to users on the web application. According to OWASP, the top 10 harmful attacks against web applications are SQL Injection attacks. Moreover, based on data reports from the UK's National Fraud Authority, SQL Injection is responsible for 97% of data exposures. Therefore, in order to prevent the SQL Injection attack, detection SQLi system is essential. The contribution of this research is securing web applications by developing a browser extension for Google Chrome using Long Short-Term Memory (LSTM), which is a unique kind of RNN algorithm capable of learning long-term dependencies like SQL Injection attacks. The results of the model will be deployed in static analysis in a browser extension, and the LSTM algorithm will learn to identify the URL that has to be injected into Damn Vulnerable Web Application (DVWA) as a sample-tested web application. Experimental results show that the proposed SQLi detection model based on the LSTM algorithm achieves an accuracy rate of 99.97%, which means that a reliable client-side can effectively detect whether the URL being accessed contains a SQLi attack or not.

Authorization policy authoring has required tools from the start. With access policy governance now an executive-level responsibility, it is imperative that such a tool expose the policy to business users' with little or no IT intervention-as natural language. NIST SP 800-162 [1] first prescribes natural language policies (NLPs) as the preferred expression of policy and then implicitly calls for automated translation of NLP to machine-executable code. This paper therefore proposes an interoperable model for the NLP's human expression. It furthermore documents the research and development of a tool set for end-to-end authoring and translation. This R&D journey-focusing constantly on end users' has debunked certain myths, has responded to steadily increasing market sophistication, has applied formal disciplines (e.g. ontologies, grammars and compiler design) and has motivated an informal demonstration of autonomic code generation. The lessons learned should be of practical value to the entire ABAC community. The research in progress' increasingly complex policies, proactive rule analytics, and expanded NLP authoring language support will require collaboration with an ever-expanding technical community from industry and academia.

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.

Mobile Ad-Hoc Networks are dynamic and wireless self-organization networks that many mobile nodes connect to each other weakly. To compare with traditional networks, they suffer failures that prevent the system from working properly. Nevertheless, we have to cope with many security issues such as unauthorized attempts, security threats and reliability. Using mobile agents in having low level fault tolerance ad-hoc networks provides fault masking that the users never notice. Mobile agent migration among nodes, choosing an alternative paths autonomous and, having high level fault tolerance provide networks that have low bandwidth and high failure ratio, more reliable. In this paper we declare that mobile agents fault tolerance peculiarity and existing fault tolerance method based on mobile agents. Also in ad-hoc networks that need security precautions behind fault tolerance, we express the new model: Secure Mobil Agent Based Fault Tolerance Model.

Application whitelisting software allows only examined and trusted applications to run on user's machine. Since many malicious files don't require administrative privileges in order for them to be executed, whitelisting can be the only way to block the execution of unauthorized applications in enterprise environment and thus prevent infection or data breach. In order to assess the current state of such solutions, the access to three whitelisting solution licenses was obtained with the purpose to test their effectiveness against different modern types of ransomware found in the wild. To conduct this study a virtual environment was used with Windows Server and Enterprise editions installed. The objective of this paper is not to evaluate each vendor or make recommendations of purchasing specific software but rather to assess the ability of application control solutions to block execution of ransomware files, as well as assess the potential for future research. The results of the research show the promise and effectiveness of whitelisting solutions.

The graphical pattern unlock scheme which requires users to connect a minimum of 4 nodes on 3X3 grid is one of the most popular authentication mechanism on mobile devices. However prior research suggests that users' pattern choices are highly biased and hence vulnerable to guessing attacks. Moreover, 3X3 pattern choices are devoid of features such as longer stroke lengths, direction changes and intersections that are considered to be important in preventing shoulder-surfing attacks. We attribute these insecure practices to the geometry of the grid and its complicated drawing rules which prevent users from realising the full potential of graphical passwords. In this paper, we propose and explore an alternate circular layout referred to as Pass-O which unlike grid layout allows connection between any two nodes, thus simplifying the pattern drawing rules. Consequently, Pass-O produces a theoretical search space of 9,85,824, almost 2.5 times greater than 3X3 grid layout. We compare the security of 3X3 and Pass-O patterns theoretically as well as empirically. Theoretically, Pass-O patterns are uniform and have greater visual complexity due to large number of intersections. To perform empirical analysis, we conduct a large-scale web-based user study and collect more than 1,23,000 patterns from 21,053 users. After examining user-chosen 3X3 and Pass-O patterns across different metrics such as pattern length, stroke length, start point, end point, repetitions, number of direction changes and intersections, we find that Pass-O patterns are much more secure than 3X3 patterns.

Software Defined Networking (SDN) is disruptive networking technology which adopts a centralised framework to facilitate fine-grained network management. However security in SDN is still in its infancy and there is need for significant work to deal with different attacks in SDN. In this paper we discuss some of the possible attacks on SDN switches and propose techniques for detecting the attacks on switches. We have developed a Switch Security Application (SSA)for SDN Controller which makes use of trusted computing technology and some additional components for detecting attacks on the switches. In particular TPM attestation is used to ensure that switches are in trusted state during boot time before configuring the flow rules on the switches. The additional components are used for storing and validating messages related to the flow rule configuration of the switches. The stored information is used for generating a trusted report on the expected flow rules in the switches and using this information for validating the flow rules that are actually enforced in the switches. If there is any variation to flow rules that are enforced in the switches compared to the expected flow rules by the SSA, then, the switch is considered to be under attack and an alert is raised to the SDN Administrator. The administrator can isolate the switch from network or make use of trusted report for restoring the flow rules in the switches. We will also present a prototype implementation of our technique.

In this paper we present techniques for enhancing the security of south bound infrastructure in SDN which includes OpenFlow switches and end hosts. In particular, the proposed security techniques have three main goals: (i) validation and secure configuration of flow rules in the OpenFlow switches by trusted SDN controller in the domain; (ii) securing the flows from the end hosts; and (iii) detecting attacks on the switches by malicious entities in the SDN domain. We have implemented the proposed security techniques as an application for ONOS SDN controller. We have also validated our application by detecting various OpenFlow switch specific attacks such as malicious flow rule insertions and modifications in the switches over a mininet emulated network.

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.

A novel attack model is proposed against the existing wireless link-based source identification, which classifies packet sources according to the physical-layer link signatures. A link signature is believed to be a more reliable indicator than an IP or MAC address for identifying packet source, as it is generally harder to modify/forge. It is therefore expected to be a future authentication against impersonation and DoS attacks. However, if an attacker is equipped with the same capability/hardware as the authenticator to process physical-layer signals, a link signature can be easily manipulated by any nearby wireless device during the training phase. Based on this finding, we propose an attack model, called the analog man-in-the-middle (AMITM) attack, which utilizes the latest full-duplex relay technology to inject semi-controlled link signatures into authorized packets and reproduce the injected signature in the fabricated packets. Our experimental evaluation shows that with a proper parameter setting, 90% of fabricated packets are classified as those sent from an authorized transmitter. A countermeasure against this new attack is also proposed for the authenticator to inject link-signature noise by the same attack methodology.