Biblio
This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe that the three aspects addressed in this paper will form the basis for studying the Science of Cyber Security.
Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.
Integrating security testing into the workflow of software developers not only can save resources for separate security testing but also reduce the cost of fixing security vulnerabilities by detecting them early in the development cycle. We present an automatic testing approach to detect a common type of Cross Site Scripting (XSS) vulnerability caused by improper encoding of untrusted data. We automatically extract encoding functions used in a web application to sanitize untrusted inputs and then evaluate their effectiveness by automatically generating XSS attack strings. Our evaluations show that this technique can detect 0-day XSS vulnerabilities that cannot be found by static analysis tools. We will also show that our approach can efficiently cover a common type of XSS vulnerability. This approach can be generalized to test for input validation against other types injections such as command line injection.
At the core of its nature, security is a highly contextual and dynamic challenge. However, current security policy approaches are usually static, and slow to adapt to ever-changing requirements, let alone catching up with reality. In a 2012 Sophos survey, it was stated that a unique malware is created every half a second. This gives a glimpse of the unsustainable nature of a global problem, any improvement in terms of closing the "time window to adapt" would be a significant step forward. To exacerbate the situation, a simple change in threat and attack vector or even an implementation of the so-called "bring-your-own-device" paradigm will greatly change the frequency of changed security requirements and necessary solutions required for each new context. Current security policies also typically overlook the direct and indirect costs of implementation of policies. As a result, technical teams often fail to have the ability to justify the budget to the management, from a business risk viewpoint. This paper considers both the adaptive and cost-benefit aspects of security, and introduces a novel context-aware technique for designing and implementing adaptive, optimized security policies. Our approach leverages the capabilities of stochastic programming models to optimize security policy planning, and our preliminary results demonstrate a promising step towards proactive, context-aware security policies.
A major challenge of the existing attack detection approaches is the identification of relevant information to a particular situation, and the use of such information to perform multi-evidence intrusion detection. Addressing such a limitation requires integrating several aspects of context to better predict, avoid and respond to impending attacks. The quality and adequacy of contextual information is important to decrease uncertainty and correctly identify potential cyber-attacks. In this paper, a systematic methodology has been used to identify contextual dimensions that improve the effectiveness of detecting cyber-attacks. This methodology combines graph, probability, and information theories to create several context-based attack prediction models that analyze data at a high- and low-level. An extensive validation of our approach has been performed using a prototype system and several benchmark intrusion detection datasets yielding very promising results.
As the amount of mobile devices populating the Internet keeps growing at tremendous pace, context-aware services have gained a lot of traction thanks to the wide set of potential use cases they can be applied to. Environmental sensing applications, emergency services, and location-aware messaging are just a few examples of applications that are expected to increase in popularity in the next few years. The MobilityFirst future Internet architecture, a clean-slate Internet architecture design, provides the necessary abstractions for creating and managing context-aware services. Starting from these abstractions we design a context services framework, which is based on a set of three fundamental mechanisms: an easy way to specify context based on human understandable techniques, i.e. use of names, an architecture supported management mechanism that allows both to conveniently deploy the service and efficiently provide management capabilities, and a native delivery system that reduces the tax on the network components and on the overhead cost of deploying such applications. In this paper, we present an emergency alert system for vehicles assisting first responders that exploits users location awareness to support quick and reliable alert messages for interested vehicles. By deploying a demo of the system on a nationwide testbed, we aim to provide better understanding of the dynamics involved in our designed framework.
This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource's behaviour under control. An intrusion detection system observes distributed energy resource's behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data show that the contextual anomaly detection performs on average 55% better in the control detection and over 56% better in the malicious control detection over the point anomaly detection.
Keystroke dynamics analysis has been applied successfully to password or fixed short texts verification as a means to reduce their inherent security limitations, because their length and the fact of being typed often makes their characteristic timings fairly stable. On the other hand, free text analysis has been neglected until recent years due to the inherent difficulties of dealing with short term behavioral noise and long term effects over the typing rhythm. In this paper we examine finite context modeling of keystroke dynamics in free text and report promising results for user verification over an extensive data set collected from a real world environment outside the laboratory setting that we make publicly available.
The perception of lack of control over resources deployed in the cloud may represent one of the critical factors for an organization to decide to cloudify or not their own services. Furthermore, in spite of the idea of offering security-as-a-service, the development of secure cloud applications requires security skills that can slow down the adoption of the cloud for nonexpert users. In the recent years, the concept of Security Service Level Agreements (Security SLA) is assuming a key role in the provisioning of cloud resources. This paper presents the SPECS framework, which enables the development of secure cloud applications covered by a Security SLA. The SPECS framework offers APIs to manage the whole Security SLA life cycle and provides all the functionalities needed to automatize the enforcement of proper security mechanisms and to monitor userdefined security features. The development process of SPECS applications offering security-enhanced services is illustrated, presenting as a real-world case study the provisioning of a secure web server.
Cloud computing is a service-based computing resources sourcing model that is changing the way in which companies deploy and operate information and communication technologies (ICT). This model introduces several advantages compared with traditional environments along with typical outsourcing benefits reshaping the ICT services supply chain by creating a more dynamic ICT environment plus a broader variety of service offerings. This leads to higher risk of disruption and brings additional challenges for organisational resilience, defined herein as the ability of organisations to survive and also to thrive when exposed to disruptive incidents. This paper draws on supply chain theory and supply chain resilience concepts in order to identify a set of coordination mechanisms that positively impact ICT operational resilience processes within cloud supply chains and packages them into a conceptual model.
Most Depth Image Based Rendering (DIBR) techniques produce synthesized images which contain non-uniform geometric distortions affecting edges coherency. This type of distortions are challenging for common image quality metrics. Morphological filters maintain important geometric information such as edges across different resolution levels. There is inherent congruence between the morphological pyramid decomposition scheme and human visual perception. In this paper, multi-scale measure, morphological pyramid peak signal-to-noise ratio MP-PSNR, based on morphological pyramid decomposition is proposed for the evaluation of DIBR synthesized images. It is shown that MPPSNR achieves much higher correlation with human judgment compared to the state-of-the-art image quality measures in this context.
Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.
With the growth of the Internet, web applications are becoming very popular in the user communities. However, the presence of security vulnerabilities in the source code of these applications is raising cyber crime rate rapidly. It is required to detect and mitigate these vulnerabilities before their exploitation in the execution environment. Recently, Open Web Application Security Project (OWASP) and Common Vulnerabilities and Exposures (CWE) reported Cross-Site Scripting (XSS) as one of the most serious vulnerabilities in the web applications. Though many vulnerability detection approaches have been proposed in the past, existing detection approaches have the limitations in terms of false positive and false negative results. This paper proposes a context-sensitive approach based on static taint analysis and pattern matching techniques to detect and mitigate the XSS vulnerabilities in the source code of web applications. The proposed approach has been implemented in a prototype tool and evaluated on a public data set of 9408 samples. Experimental results show that proposed approach based tool outperforms over existing popular open source tools in the detection of XSS vulnerabilities.
South Africa's lead-users predilections to tinker and innovate mobile banking services is driven by various constructs. Advanced technologies have made mobile banking services easy to use, attractive and beneficial. While this is welcome news to many, there are concerns that when lead-users tinker with these services, information security risks are exacerbated. The aim of this article is to present an insightful understanding of the demand-side predilections of South Africa's lead-users in such contexts. We assimilate the theories of Usage Control, (UCON), the Theory of Technology Acceptance Model (TAM), and the Theory of Perceived Risk (TPP) to explain predilections over technology. We demonstrate that constructs derived from these theories can explain the general demand-side predilection to tinker with mobile banking services. A quantitative approach was used to test this. From a sample of South African banking lead-users operating in Gauteng province of South Africa, data was collected and analysed with the help of a software package. We found unexpectedly that, lead-users predilections to tinker with mobile banking services was inhibited by perceived risk. Moreover, male lead-users were more domineering in the tinkering process than female lead-users. The implication for this is discussed and explained in the main body of work.
Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.
Internet is facing many challenges that cannot be solved easily through ad hoc patches. To address these challenges, many research programs and projects have been initiated and many solutions are being proposed. However, before we have a new architecture that can motivate Internet service providers (ISPs) to deploy and evolve, we need to address two issues: 1) know the current status better by appropriately evaluating the existing Internet; and 2) find how various incentives and strategies will affect the deployment of the new architecture. For the first issue, we define a series of quantitative metrics that can potentially unify results from several measurement projects using different approaches and can be an intrinsic part of future Internet architecture (FIA) for monitoring and evaluation. Using these metrics, we systematically evaluate the current interdomain routing system and reveal many “autonomous-system-level” observations and key lessons for new Internet architectures. Particularly, the evaluation results reveal the imbalance underlying the interdomain routing system and how the deployment of FIAs can benefit from these findings. With these findings, for the second issue, appropriate deployment strategies of the future architecture changes can be formed with balanced incentives for both customers and ISPs. The results can be used to shape the short- and long-term goals for new architectures that are simple evolutions of the current Internet (so-called dirty-slate architectures) and to some extent to clean-slate architectures.
Most network traffic analysis applications are designed to discover malicious activity by only relying on high-level flow-based message properties. However, to detect security breaches that are specifically designed to target one network (e.g., Advanced Persistent Threats), deep packet inspection and anomaly detection are indispensible. In this paper, we focus on how we can support experts in discovering whether anomalies at message level imply a security risk at network level. In SNAPS (Semantic Network traffic Analysis through Projection and Selection), we provide a bottom-up pixel-oriented approach for network traffic analysis where the expert starts with low-level anomalies and iteratively gains insight in higher level events through the creation of multiple selections of interest in parallel. The tight integration between visualization and machine learning enables the expert to iteratively refine anomaly scores, making the approach suitable for both post-traffic analysis and online monitoring tasks. To illustrate the effectiveness of this approach, we present example explorations on two real-world data sets for the detection and understanding of potential Advanced Persistent Threats in progress.
Using heterogeneous clouds has been considered to improve performance of big-data analytics for healthcare platforms. However, the problem of the delay when transferring big-data over the network needs to be addressed. The purpose of this paper is to analyze and compare existing cloud computing environments (PaaS, IaaS) in order to implement middleware services. Understanding the differences and similarities between cloud technologies will help in the interconnection of healthcare platforms. The paper provides a general overview of the techniques and interfaces for cloud computing middleware services, and proposes a cloud architecture for healthcare. Cloud middleware enables heterogeneous devices to act as data sources and to integrate data from other healthcare platforms, but specific APIs need to be developed. Furthermore, security and management problems need to be addressed, given the heterogeneous nature of the communication and computing environment. The present paper fills a gap in the electronic healthcare register literature by providing an overview of cloud computing middleware services and standardized interfaces for the integration with medical devices.
Effective digital identity management system is a critical enabler of cloud computing, since it supports the provision of the required assurances to the transacting parties. Such assurances sometimes require the disclosure of sensitive personal information. Given the prevalence of various forms of identity abuses on the Internet, a re-examination of the factors underlying cloud services acquisition has become critical and imperative. In order to provide better assurances, parties to cloud transactions must have confidence in service providers' ability and integrity in protecting their interest and personal information. Thus a trusted cloud identity ecosystem could promote such user confidence and assurances. Using a qualitative research approach, this paper explains the role of trust in cloud service acquisition by organizations. The paper focuses on the processes of acquisition of cloud services by financial institutions in Ghana. The study forms part of comprehensive study on the monetization of personal Identity information.
Effective digital identity management system is a critical enabler of cloud computing, since it supports the provision of the required assurances to the transacting parties. Such assurances sometimes require the disclosure of sensitive personal information. Given the prevalence of various forms of identity abuses on the Internet, a re-examination of the factors underlying cloud services acquisition has become critical and imperative. In order to provide better assurances, parties to cloud transactions must have confidence in service providers' ability and integrity in protecting their interest and personal information. Thus a trusted cloud identity ecosystem could promote such user confidence and assurances. Using a qualitative research approach, this paper explains the role of trust in cloud service acquisition by organizations. The paper focuses on the processes of acquisition of cloud services by financial institutions in Ghana. The study forms part of comprehensive study on the monetization of personal Identity information.
The EIIM model for ER allows for creation and maintenance of persistent entity identity structures. It accomplishes this through a collection of batch configurations that allow updates and asserted fixes to be made to the Identity knowledgebase (IKB). The model also provides a batch IR configuration that provides no maintenance activity but instead allows access to the identity information. This batch IR configuration is limited in a few ways. It is driven by the same rules used for maintaining the IKB, has no inherent method to identity "close" matches, and can only identify and return the positive matches. Through the decoupling of this configuration and its movements into an interactive role under the umbrella of an Identity Management Service, a more robust access method can be provided for the use of identity information. This more robust access to the information improved the quality of the information along multiple Information Quality dimensions.
In a converging world, where borders between countries are surpassed in the digital environment, it is necessary to develop systems that effectively replace the recognition “vis-a-vis” with digital means of recognizing and identifying entities and people. In this work we summarize the current standardization efforts in the area of digital identity management. We identify a number of open challenges that need to be addressed in the near future to ensure the interoperability and usability of digital identity management services in an efficient and privacy maintaining international framework. These challenges for standardization include: the management of identifiers for digital identities at the global level; attribute management including attribute format, structure, and assurance; procedures and protocols to link attributes to digital identities. Attention is drawn to key elements that should be considered in addressing these issues through standardization.