Biblio

Dynamic security assessment provides system operators with vital information for possible preventive or emergency control to prevent security problems. In some cases, power system topology change deteriorates intelligent system-based online stability assessment performance. In this paper, we propose a new online assessment scheme to improve classification performance reliability of dynamic transient stability assessment. In the new scheme, we use an intelligent system consisting an ensemble of neural networks based on extreme learning machine. A new feature selection algorithm combining filter type method RRelief-F and wrapper type method Sequential Floating Forward Selection is proposed. Boosting learning algorithm is used in intelligent system training process which leads to higher classification accuracy. Moreover, we propose a new classification rule using weighted outputs of predictors in the ensemble helps to achieve 100% transient stability prediction in our case study.

Dynamic security assessment provides system operators with vital information for possible preventive or emergency control to prevent security problems. In some cases, power system topology change deteriorates intelligent system-based online stability assessment performance. In this paper, we propose a new online assessment scheme to improve classification performance reliability of dynamic transient stability assessment. In the new scheme, we use an intelligent system consisting an ensemble of neural networks based on extreme learning machine. A new feature selection algorithm combining filter type method RRelief-F and wrapper type method Sequential Floating Forward Selection is proposed. Boosting learning algorithm is used in intelligent system training process which leads to higher classification accuracy. Moreover, we propose a new classification rule using weighted outputs of predictors in the ensemble helps to achieve 100% transient stability prediction in our case study.

Detecting software security vulnerabilities and distinguishing vulnerable from non-vulnerable code is anything but simple. Most of the time, vulnerabilities remain undisclosed until they are exposed, for instance, by an attack during the software operational phase. Software metrics are widely-used indicators of software quality, but the question is whether they can be used to distinguish vulnerable software units from the non-vulnerable ones during development. In this paper, we perform an exploratory study on software metrics, their interdependency, and their relation with security vulnerabilities. We aim at understanding: i) the correlation between software architectural characteristics, represented in the form of software metrics, and the number of vulnerabilities; and ii) which are the most informative and discriminative metrics that allow identifying vulnerable units of code. To achieve these goals, we use, respectively, correlation coefficients and heuristic search techniques. Our analysis is carried out on a dataset that includes software metrics and reported security vulnerabilities, exposed by security attacks, for all functions, classes, and files of five widely used projects. Results show: i) a strong correlation between several project-level metrics and the number of vulnerabilities, ii) the possibility of using a group of metrics, at both file and function levels, to distinguish vulnerable and non-vulnerable code with a high level of accuracy.

User attribution process based on human inherent dynamics and preference is one area of research that is capable of elucidating and capturing human dynamics on the Internet. Prior works on user attribution concentrated on behavioral biometrics, 1-to-1 user identification process without consideration for individual preference and human inherent temporal tendencies, which is capable of providing a discriminatory baseline for online users, as well as providing a higher level classification framework for novel user attribution. To address these limitations, the study developed a temporal model, which comprises the human Polyphasia tendency based on Polychronic-Monochronic tendency scale measurement instrument and the extraction of unique human-centric features from server-side network traffic of 48 active users. Several machine-learning algorithms were applied to observe distinct pattern among the classes of the Polyphasia tendency, through which a logistic model tree was observed to provide higher classification accuracy for a 1-to-N user attribution process. The study further developed a high-level attribution model for higher-level user attribution process. The result from this study is relevant in online profiling process, forensic identification and profiling process, e-learning profiling process as well as in social network profiling process.

As the development of technology increases, the security risk also increases. This has affected most organizations, irrespective of size, as they depend on the increasingly pervasive technology to perform their daily tasks. However, the dependency on technology has introduced diverse security vulnerabilities in organizations which requires a reliable preparedness for probable forensic investigation of the unauthorized incident. Keystroke dynamics is one of the cost-effective methods for collecting potential digital evidence. This paper presents a keystroke pattern analysis technique suitable for the collection of complementary potential digital evidence for forensic readiness. The proposition introduced a technique that relies on the extraction of reliable behavioral signature from user activity. Experimental validation of the proposition demonstrates the effectiveness of proposition using a multi-scheme classifier. The overall goal is to have forensically sound and admissible keystroke evidence that could be presented during the forensic investigation to minimize the costs and time of the investigation.

Whilst the fundamental composition of digital forensic readiness have been expounded by myriad literature, the integration of behavioral modalities have not been considered. Behavioral modalities such as keystroke and mouse dynamics are key components of human behavior that have been widely used in complementing security in an organization. However, these modalities present better forensic properties, thus more relevant in investigation/incident response, than its deployment in security. This study, therefore, proposes a forensic framework which encompasses a step-by-step guide on how to integrate behavioral biometrics into digital forensic readiness process. The proposed framework, behavioral biometrics-based digital forensics readiness framework (BBDFRF) comprised four phases which include data acquisition, preservation, user-authentication, and user pattern attribution phase. The proposed BBDFRF is evaluated in line with the ISO/IEC 27043 standard for proactive forensics, to address the gap on the integration of the behavioral biometrics into proactive forensics. BBDFRF thus extends the body of literature on the forensic capability of behavioral biometrics. The implementation of this framework can be used to also strengthen the security mechanism of an organization, particularly on continuous authentication.

Extensible language frameworks aim to allow independently-developed language extensions to be easily added to a host programming language. It should not require being a compiler expert, and the resulting compiler should "just work" as expected. Previous work has shown how specifications for parsing (based on context free grammars) and for semantic analysis (based on attribute grammars) can be automatically and reliably composed, ensuring that the resulting compiler does not terminate abnormally. However, this work does not ensure that a property proven to hold for a language (or extended language) still holds when another extension is added, a problem we call interference. We present a solution to this problem using of a logical notion of coherence. We show that a useful class of language extensions, implemented as attribute grammars, preserve all coherent properties. If we also restrict extensions to only making use of coherent properties in establishing their correctness, then the correctness properties of each extension will hold when composed with other extensions. As a result, there can be no interference: each extension behaves as specified.

The concept of Extension Headers, newly introduced with IPv6, is elusive and enables new types of threats in the Internet. Simply dropping all traffic containing any Extension Header - a current practice by operators-seemingly is an effective solution, but at the cost of possibly dropping legitimate traffic as well. To determine whether threats indeed occur, and evaluate the actual nature of the traffic, measurement solutions need to be adapted. By implementing these specific parsing capabilities in flow exporters and performing measurements on two different production networks, we show it is feasible to quantify the metrics directly related to these threats, and thus allow for monitoring and detection. Analysing the traffic that is hidden behind Extension Headers, we find mostly benign traffic that directly affects end-user QoE: simply dropping all traffic containing Extension Headers is thus a bad practice with more consequences than operators might be aware of.

The interconnectivity of 6LoWPAN networks with the Internet raises serious security concerns, as constrained 6LoWPAN devices are accessible anywhere from the untrusted global Internet. Also, 6LoWPAN devices are mostly deployed in unattended environments, hence easy to capture and clone. Despite that state of the art crypto solutions provide information security, IPv6 enabled smart objects are vulnerable to attacks from outside and inside 6LoWPAN networks that are aimed to disrupt networks. This paper attempts to identify intrusions aimed to disrupt the Routing Protocol for Low-Power and Lossy Networks (RPL).In order to improve the security within 6LoWPAN networks, we extend SVELTE, an intrusion detection system for the Internet of Things, with an intrusion detection module that uses the ETX (Expected Transmissions) metric. In RPL, ETX is a link reliability metric and monitoring the ETX value can prevent an intruder from actively engaging 6LoWPAN nodes in malicious activities. We also propose geographic hints to identify malicious nodes that conduct attacks against ETX-based networks. We implement these extensions in the Contiki OS and evaluate them using the Cooja simulator.

The rapid deployment of IoT systems on the public Internet is not without concerns for the security and privacy of consumers. Security in IoT systems is often poorly engineered and engineering for privacy does notseemtobea concern for vendors at all. Thecombination of poor security hygiene and access to valuable knowledge renders IoT systems a much-sought target for attacks. IoT systems are not only Internet-accessible but also play the role of servers according to the established client-server communication model and are thus configured with static and/or easily predictable IPv6 addresses, rendering them an easy target for attacks. We present 6HOP, a novel addressing scheme for IoT devices. Our proposal is lightweight in operation, requires minimal administration overhead, and defends against reconnaissance attacks, address based correlation as well as denial-of-service attacks. 6HOP therefore exploits the ample address space available in IPv6 networks and provides effective protection this way.

Mobile Cross-Platform Tools (CPTs) provide an alternative to native application development that allows mobile app developers to drastically reduce the development time and cost when targeting multiple platforms. They allow sharing a significant part of the application codebase between the implementations for the targeted platforms (e.g. Android, iOS, Windows Phone). Although CPTs provide significant benefits for developers, there can introduce several disadvantages. The CPT software layers and translation steps can impact the security of the produced applications. One of the most well-known and often-used CPTs is Cordova, formerly known as PhoneGap. Cordova has, over the years, taken several steps to reduce the attack surface and introduced several mechanisms that allow developers to increase the security of Cordova applications. This paper gives a statistical overview of the adoption of Cordova security best practices and mechanisms in Cordova applications downloaded from the Google Play Store. For the analysis, over a thousand Cordova application were downloaded. The research shows that the poor adoption of these mechanisms leads to a significant number of insecure Cordova applications.

The security of almost any real-world distributed system today depends on the participants having some "reasonably accurate" sense of current real time. Indeed, to name one example, the very authenticity of practically any communication on the Internet today hinges on the ability of the parties to accurately detect revocation of certificates, or expiration of passwords or shared keys.,,However, as recent attacks show, the standard protocols for determining time are subvertible, resulting in wide-spread security loss. Worse yet, we do not have security notions for network time protocols that (a) can be rigorously asserted, and (b) rigorously guarantee security of applications that require a sense of real time.,,We propose such notions, within the universally composable (UC) security framework. That is, we formulate ideal functionalities that capture a number of prevalent forms of time measurement within existing systems. We show how they can be realized by real-world protocols, and how they can be used to assert security of time-reliant applications - specifically, certificates with revocation and expiration times. This allows for relatively clear and modular treatment of the use of time consensus in security-sensitive systems.,,Our modeling and analysis are done within the existing UC framework, in spite of its asynchronous, event-driven nature. This allows incorporating the use of real time within the existing body of analytical work done in this framework. In particular it allows for rigorous incorporation of real time within cryptographic tools and primitives.

Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.

Developers of applications relying on cryptographic libraries can easily make mistakes in their use. Popular dynamic languages such as JavaScript make testing or verifying such applications particularly challenging. In this paper, we present our ongoing work toward a methodology for automatically checking security properties in JavaScript code. Our main idea is to attach security annotations to values that encode properties of interest. We illustrate our idea using examples and, as an initial step in our line of work, we present a formalization of security annotations in a statically typed lambda calculus. As next steps, we will translate our annotations to a dynamically typed formalization of JavaScript such as $łambda$JS and implement a runtime checked type extension using code instrumentation for full JavaScript.

We analyze the security practices of three smart toys that communicate with children through voice commands. We show the general communication architecture, and some general security and privacy practices by each of the devices. Then we focus on the analysis of one particular toy, and show how attackers can decrypt communications to and from a target device, and perhaps more worryingly, the attackers can also inject audio into the toy so the children listens to any arbitrary audio file the attacker sends to the toy. This last attack raises new safety concerns that manufacturers of smart toys should prevent.

This research in progress paper describes the role of cyber security measures undertaken in an ICT system for integrating electric storage technologies into the grid. To do so, it defines security requirements for a communications gateway and gives detailed information and hands-on configuration advice on node and communication line security, data storage, coping with backend M2M communications protocols and examines privacy issues. The presented research paves the road for developing secure smart energy communications devices that allow enhancing energy efficiency. The described measures are implemented in an actual gateway device within the HORIZON 2020 project STORY, which aims at developing new ways to use storage and demonstrating these on six different demonstration sites.

Mobile attack approaches can be categorized as Application Based Attacks and Frequency Based Attacks. Application based attacks are reviewed extensively in the literature. However, frequency based attacks to mobile phones are not experimented in detail. In this work, we have experimentally succeeded to attack an Android smartphone using a simple software based radio circuit. We have developed a software “Primary Mobile Hack Builder” to control Android operated cellphone as a distance. The SMS information and pictures in the cellphone can be obtained using this device. On the other hand, after launching a software into targeting cellphone, the camera of the cellphone can be controlled for taking pictures and downloading them into our computers. It was also possible to eavesdropping the conversation.

Specifics of an alias-free digitizer application for compressed digitizing and recording of wideband signals are considered. Signal sampling in this case is performed on the basis of picosecond resolution event timing, the digitizer actually is a subsystem of Event Timer A033-ET and specific events that are detected and then timed are the signal and reference sine-wave crossings. The used approach to development of this subsystem is described and some results of experimental studies are given.

The article proposes an enhanced behavior model using graphs of state transitions. The properties and advantages of the proposed model are discussed, UML-based Cooperative Interaction of Automata Objects (CIAO) language is described, attribute approach on its parsing mechanism is introduced. The proposed model for describing behavior is aimed at achieving higher reliability and productivity indicators when designing the secure architecture and implementing reactive and distributed systems in comparison with traditional methods. A side-by-side goal is to create a convenient publication language for describing parallel algorithms and distributed reactive systems. The offered model has advantages under certain conditions in comparison with other models of behavior description in the field of the description of asynchronous distributed reacting systems.

Often considered as the brain of an industrial process, Industrial control systems are presented as the vital part of today's critical infrastructure due to their crucial role in process control and monitoring. Any failure or error in the system will have a considerable damage. Their openness to the internet world raises the risk related to cyber-attacks. Therefore, it's necessary to consider cyber security challenges while designing an ICS in order to provide security services such as authentication, integrity, access control and secure communication channels. To implement such services, it's necessary to provide an efficient key management system (KMS) as an infrastructure for all cryptographic operations, while preserving the functional characteristics of ICS. In this paper we will analyze existing KMS and their suitability for ICS, then we propose a new KMS based on Identity Based Cryptography (IBC) as a better alternative to traditional KMS. In our proposal, we consider solving two security problems in IBC which brings it up to be more suitable for ICS.

Transmission lines' monitoring systems produce a large amount of data that hinders faults diagnosis. For this reason, approaches that can acquire and automatically interpret the information coming from lines' monitoring are needed. Furthermore, human errors stemming from operator dependent real-time decision need to be reduced. In this paper a multiple faults diagnosis method to determine transmission lines' operating conditions is proposed. Different scenarios, including insulator chains contamination with different types and concentrations of pollutants were modeled by equivalents circuits. Their performance were characterized by leakage current (LC) measurements and related to specific fault modes. Features extraction's algorithm relying on the difference between normal and faulty conditions were used to define qualitative trends for the diagnosis of various fault modes.

Cross-Site Scripting (XSS) is an unremitting problem for the Web. Since its initial public documentation in 2000 until now, XSS has been continuously on top of the vulnerability statistics. Even though there has been a considerable amount of research and developer education to address XSS on the source code level, the overall number of discovered XSS problems remains high. Because of this, various approaches to mitigate XSS have been proposed as a second line of defense, with HTML sanitizers, Web Application Firewalls, browser-based XSS filters, and the Content Security Policy being some prominent examples. Most of these mechanisms focus on script tags and event handlers, either by removing them from user-provided content or by preventing their script code from executing. In this paper, we demonstrate that this approach is no longer sufficient for modern applications: We describe a novel Web attack that can circumvent all of theses currently existing XSS mitigation techniques. In this attack, the attacker abuses so called script gadgets (legitimate JavaScript fragments within an application's legitimate code base) to execute JavaScript. In most cases, these gadgets utilize DOM selectors to interact with elements in the Web document. Through an initial injection point, the attacker can inject benign-looking HTML elements which are ignored by these mitigation techniques but match the selector of the gadget. This way, the attacker can hijack the input of a gadget and cause processing of his input, which in turn leads to code execution of attacker-controlled values. We demonstrate that these gadgets are omnipresent in almost all modern JavaScript frameworks and present an empirical study showing the prevalence of script gadgets in productive code. As a result, we assume most mitigation techniques in web applications written today can be bypassed.

As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic. This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (\textbackslashtextless;100) BGP prefixes to isolate 50% of the mining power-even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages. We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data. The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately.

In this paper, we present a differential privacy preserving approach, which extracts personality-based knowledge to serve privacy guarantee data analysis on personal sensitive data. Based on the approach, we further implement an end-to-end privacy guarantee system, KaPPA, to provide researchers iterative data analysis on sensitive data. The key challenge for differential privacy is determining a reasonable amount of privacy budget to balance privacy preserving and data utility. Most of the previous work applies unified privacy budget to all individual data, which leads to insufficient privacy protection for some individuals while over-protecting others. In KaPPA, the proposed personality-based privacy preserving approach automatically calculates privacy budget for each individual. Our experimental evaluations show a significant trade-off of sufficient privacy protection and data utility.

Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user's security and privacy and consequently the operator's reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly.