Biblio

Deception has been widely considered in literature as an effective means of enhancing security protection when the defender holds some private information about the ongoing rivalry unknown to the attacker. However, most of the existing works on deception assume static environments and thus consider only myopic deception, while practical security games between the defender and the attacker may happen in dynamic scenarios. To better exploit the defender's private information in dynamic environments and improve security performance, a stochastic deception game (SDG) framework is developed in this work to enable the defender to conduct foresighted deception. To solve the proposed SDG, a new iterative algorithm that is provably convergent is developed. A corresponding learning algorithm is developed as well to facilitate the defender in conducting foresighted deception in unknown dynamic environments. Numerical results show that the proposed foresighted deception can offer a substantial performance improvement as compared to the conventional myopic deception.

By using generalized regression neural network clustering analysis, effective clustering of five kinds of network intrusion behavior modes is carried out. First of all, intrusion data is divided into five categories by making use of fuzzy C means clustering algorithm. Then, the samples that are closet to the center of each class in the clustering results are taken as the clustering training samples of generalized neural network for the data training, and the results output by the training are the individual owned invasion category. The experimental results showed that the new algorithm has higher classification accuracy of network intrusion ways, which can provide more reliable data support for the prevention of the network intrusion.

Wearable and mobile medical devices provide efficient, comfortable, and economic health monitoring, having a wide range of applications from daily to clinical scenarios. Health data security becomes a critically important issue. Electrocardiogram (ECG) has proven to be a potential biometric in human recognition over the past decade. Unlike conventional authentication methods using passwords, fingerprints, face, etc., ECG signal can not be simply intercepted, duplicated, and enables continuous identification. However, in many of the studies, algorithms developed are not suitable for practical application, which usually require long ECG data for authentication. In this work, we introduce a two-phase authentication using artificial neural network (NN) models. This algorithm enables fast authentication within only 3 seconds, meanwhile achieves reasonable performance in recognition. We test the proposed method in a controlled laboratory experiment with 50 subjects. Finger ECG signals are collected using a mobile device at different times and physical statues. At the first stage, a ``General'' NN model is constructed based on data from the cohort and used for preliminary screening, while at the second stage ``Personal'' NN models constructed from single individual's data are applied as fine-grained identification. The algorithm is tested on the whole data set, and on different sizes of subsets (5, 10, 20, 30, and 40). Results proved that the proposed method is feasible and reliable for individual authentication, having obtained average False Acceptance Rate (FAR) and False Rejection Rate (FRR) below 10% for the whole data set.

Although connecting a microgrid to modern power systems can alleviate issues arising from a large penetration of distributed generation, it can also cause severe voltage instability problems. This paper presents an online method to analyze voltage security in a microgrid using convolutional neural networks. To transform the traditional voltage stability problem into a classification problem, three steps are considered: 1) creating data sets using offline simulation results; 2) training the model with dimensional reduction and convolutional neural networks; 3) testing the online data set and evaluating performance. A case study in the modified IEEE 14-bus system shows the accuracy of the proposed analysis method increases by 6% compared to back-propagation neural network and has better performance than decision tree and support vector machine. The proposed algorithm has great potential in future applications.

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.

NoSQL databases have gained a lot of popularity over the last few years. They are now used in many new system implementations that work with vast amounts of data. This data will typically also include sensitive information that needs to be secured. NoSQL databases are also underlying a number of cloud implementations which are increasingly being used to store sensitive information by various organisations. This has made NoSQL databases a new target for hackers and other state sponsored actors. Forensic examinations of compromised systems will need to be conducted to determine what exactly transpired and who was responsible. This paper examines specifically if NoSQL databases have security features that leave relevant traces so that accurate forensic attribution can be conducted. The seeming lack of default security measures such as access control and logging has prompted this examination. A survey into the top ranked NoSQL databases was conducted to establish what authentication and authorisation features are available. Additionally the provided logging mechanisms were also examined since access control without any auditing would not aid forensic attribution tremendously. Some of the surveyed NoSQL databases do not provide adequate access control mechanisms and logging features that leave relevant traces to allow forensic attribution to be done using those. The other surveyed NoSQL databases did provide adequate mechanisms and logging traces for forensic attribution, but they are not enabled or configured by default. This means that in many cases they might not be available, leading to insufficient information to perform accurate forensic attribution even on those databases.

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.

Access control offers mechanisms to control and limit the actions or operations that are performed by a user on a set of resources in a system. Many access control models exist that are able to support this basic requirement. One of the properties examined in the context of these models is their ability to successfully restrict access to resources. Nevertheless, considering only restriction of access may not be enough in some environments, as in critical infrastructures. The protection of systems in this type of environment requires a new line of enquiry. It is essential to ensure that appropriate access is always possible, even when users and resources are subjected to challenges of various sorts. Resilience in access control is conceived as the ability of a system not to restrict but rather to ensure access to resources. In order to demonstrate the application of resilience in access control, we formally define an attribute based access control model (ABAC) based on guidelines provided by the National Institute of Standards and Technology (NIST). We examine how ABAC-based resilience policies can be specified in temporal logic and how these can be formally verified. The verification of resilience is done using an automated model checking technique, which eventually may lead to reducing the overall complexity required for the verification of resilience policies and serve as a valuable tool for administrators.

In this talk, I will discuss my lab's work in the emerging field of adversarial stylometry and machine learning. Machine learning algorithms are increasingly being used in security and privacy domains, in areas that go beyond intrusion or spam detection. For example, in digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions. We have applied stylometry to difficult domains such as underground hacker forums, open source projects (code), and tweets. I will discuss our Doppelgnger Finder algorithm, which enables us to group Sybil accounts on underground forums and detect blogs from Twitter feeds and reddit comments. In addition, I will discuss our work attributing unknown source code and binaries.

Attribute-Based Access Control (ABAC) has received significant attention in recent years, although the concept has been around for over two decades now. Many ABAC models, with different variations, have been proposed and formalized. Besides basic ABAC models, there are models designed with additional capabilities such as group attributes, group and attribute hierarchies and so on. Hierarchical relationship among groups and attributes enhances access control flexibility and facilitates attribute management and administration. However, implementation and demonstration of ABAC models in real-world applications is still lacking. In this paper, we present a restricted HGABAC (rHGABAC) model with user and object groups and group hierarchy. We then introduce attribute hierarchies in this model. We also present an authorization architecture for implementing rHGABAC utilizing the NIST Policy Machine (PM). PM allows to define attribute-based access control policies, however, the attributes in PM are different in nature than attributes in typical ABAC models as name-value pairs. We identify a policy configuration mechanism for our proposed model employing PM capabilities, and demonstrate use cases and their configuration and implementation in PM using our authorization architecture.

In this paper, we introduce the concept of transforming attribute-value assignments from one set to another set. We specify two types of transformations–-attribute reduction and attribute expansion. We distinguish policy attributes from non-policy attributes in that policy attributes are used in authorization policies whereas the latter are not. Attribute reduction is a process of contracting a large set of assignments of non-policy attributes into a possibly smaller set of policy attribute-value assignments. This process is useful for abstracting attributes that are too specific for particular types of objects or users, designing modular authorization policies, and modeling hierarchical policies. On the other hand, attribute expansion is a process of performing a large set of attribute-value assignments to users or objects from a possibly smaller set of assignments. We define a language for specifying mapping for the transformation process. We also identify and discuss various issues that stem from the transformation process.

Some online communities are better than others in standardizing and automating the attribution process. This study examines how automated attribution can alleviate attribution apprehension and thus facilitate creative integration in open communities. Attribution apprehension, i.e., a user's anxiety over proper attribution of reused artifacts, adversely impacts the tendencies to engage in the integration process. Because open communities thrive on the basis of fairness, automated attribution features are essential in fostering creative integration. This study draws upon task-technology fit to craft a theoretical framework for explaining this phenomenon, reviews current tools for automated attribution in different communities and describes findings of a pilot survey on how those tools can encourage creative integration.

Continuously improving security on an information system requires unique combination of human aspect, policies, and technology. This acts as leverage for designing an access control management approach which avails only relevant parts of a system according to an end-users' scope of work. This paper introduces a framework for information security fundamentals at organizational and theoretical levels, to identify critical success factors that are vital in assessing an organization's security maturity through a model referred to as “information security digital divide maturity framework”. The foregoing is based on a developed conceptual framework for information security digital divide. The framework strives to divide system end-users into “specific information haves and have-nots”. It intends to assist organizations to continually evaluate and improve on their security governance, standards, and policies which permit access on the basis of each end-user's work scope. The framework was tested through two surveys targeting 90 end-users and 35 security experts.

Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies; but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.

In the multi-cloud tenancy environments, Web Service offers an standard approach for discovering and using capabilities in an environment that transcends ownership domains. This brings into concern the ownership and security related to Web Service governance. Our approach for this issue involves an ESB-integrated middleware for security criteria regulation on Clouds. It uses an attribute-based security policy model for the exhibition of assets consumers' security profiles and deducing service accessing decision. Assets represent computing power/functionality and information/data provided by entities. Experiments show the middleware to bring minor governance burdens on the hardware aspect, as well as better performance with colosum scaling property, dealing well with cumbersome policy files, which is probably the situation of complex composite service scenarios.

Learning analytics open up a complex landscape of privacy and policy issues, which, in turn, influence how learning analytics systems and practices are designed. Research and development is governed by regulations for data storage and management, and by research ethics. Consequently, when moving solutions out the research labs implementers meet constraints defined in national laws and justified in privacy frameworks. This paper explores how the OECD, APEC and EU privacy frameworks seek to regulate data privacy, with significant implications for the discourse of learning, and ultimately, an impact on the design of tools, architectures and practices that now are on the drawing board. A detailed list of requirements for learning analytics systems is developed, based on the new legal requirements defined in the European General Data Protection Regulation, which from 2018 will be enforced as European law. The paper also gives an initial account of how the privacy discourse in Europe, Japan, South-Korea and China is developing and reflects upon the possible impact of the different privacy frameworks on the design of LA privacy solutions in these countries. This research contributes to knowledge of how concerns about privacy and data protection related to educational data can drive a discourse on new approaches to privacy engineering based on the principles of Privacy by Design. For the LAK community, this study represents the first attempt to conceptualise the issues of privacy and learning analytics in a cross-cultural context. The paper concludes with a plan to follow up this research on privacy policies and learning analytics systems development with a new international study.

Fast Health Interoperability Services (FHIR) is the most recent in the line of standards for healthcare resources. FHIR represents different types of medical artifacts as resources and also provides recommendations for their authorized disclosure using web-based protocols including O-Auth and OpenId Connect and also defines security labels. In most cases, Role Based Access Control (RBAC) is used to secure access to FHIR resources. We provide an alternative approach based on Attribute Based Access Control (ABAC) that allows attributes of subjects and objects to take part in authorization decision. Our system allows various stakeholders to define policies governing the release of healthcare data. It also authenticates the end user requesting access. Our system acts as a middle-layer between the end-user and the FHIR server. Our system provides efficient release of individual and batch resources both during normal operations and also during emergencies. We also provide an implementation that demonstrates the feasibility of our approach.

As the use of cloud computing and autonomous computing increases, integrity verification of the software stack used in a system becomes a critical issue. In this paper, we analyze the internal behavior of IMA (Integrity Measurement Architecture), one of the most well-known integrity verification frameworks employed in the Linux kernel. For integrity verification, IMA measures all executables and their configuration files in a trusty manner using TPM (Trust Platform Module). Our analysis reveals that there are two obstacles in IMA, measurement overhead and nondeterminism. To address these problems, we propose two novel techniques, called batch extend and core measurement. The former is a technique that accumulates the measured values of executables/files and extends them into TPM in a batch fashion. The second technique measures some specified executables/files only so that it verifies the core integrity of a system in which a user or a remote party is interested. Real implementation based evaluation shows that our proposal can reduce the booting time from 122 to 23 seconds, while supporting the same integrity verification capability of the default IMA policy.

With the coming of the age of big data, efforts to institutionalize de-identification of personal information to protect privacy but also at the same time, to allow the use of personal information, have been actively carried out and already, many countries are in the stage of implementing and establishing de-identification policies quite actively. But even with such efforts to protect and use personal information at the same time, the danger posed by re-identification based on de-identified information is real enough to warrant serious consideration for a management mechanism of such risks as well as a mechanism for distributing the responsibilities and liabilities that follow these risks in the event of accidents and incidents involving the invasion of privacy. So far, most countries implementing the de-identification policies are focusing on defining what de-identification is and the exemption requirements to allow free use of de-identified personal information; in fact, it seems that there is a lack of discussion and consideration on how to distribute the responsibility of the risks and liabilities involved in the process of de-identification of personal information. This study proposes to take a look at the various de-identification policies worldwide and contemplate on these policies in the perspective of risk-liability theory. Also, the constituencies of the de-identification policies will be identified in order to analyze the roles and responsibilities of each of these constituencies thereby providing the theoretical basis on which to initiate the discussions on the distribution of burden and responsibilities arising from the de-identification policies.

Modern browsers implement different security policies such as the Content Security Policy (CSP), a mechanism designed to mitigate popular web vulnerabilities, and the Same Origin Policy (SOP), a mechanism that governs interactions between resources of web pages. In this work, we describe how CSP may be violated due to the SOP when a page contains an embedded iframe from the same origin. We analyse 1 million pages from 10,000 top Alexa sites and report that at least 31.1% of current CSP-enabled pages are potentially vulnerable to CSP violations. Further considering real-world situations where those pages are involved in same-origin nested browsing contexts, we found that in at least 23.5% of the cases, CSP violations are possible. During our study, we also identified a divergence among browsers implementations in the enforcement of CSP in srcdoc sandboxed iframes, which actually reveals a problem in Gecko-based browsers CSP implementation. To ameliorate the problematic conflicts of the security mechanisms, we discuss measures to avoid CSP violations.

Mobile application offloading, with the purpose of extending battery lifetime and increasing performance has been intensively discussed recently, resulting in various different solutions: mobile device clones operated as virtual machines in the cloud, simultaneously running applications on the mobile device and on a distant server, as well as flexible solutions dynamically acquiring other mobile devices' resources in the user's surrounding. Existing solutions have gaps in the fields of data security and application security. These gaps can be closed by integrating data usage policies, as well as application-flow policies. In this paper, we propose and evaluate a novel approach of integrating XACML into existing mobile application offloading-frameworks. Data owners remain in full control of their data, still, technologies like device-to-device offloading can be used.

Logic locking has been conceived as a promising proactive defense strategy against intellectual property (IP) piracy, counterfeiting, hardware Trojans, reverse engineering, and overbuilding attacks. Yet, various attacks that use a working chip as an oracle have been launched on logic locking to successfully retrieve its secret key, undermining the defense of all existing locking techniques. In this paper, we propose stripped-functionality logic locking (SFLL), which strips some of the functionality of the design and hides it in the form of a secret key(s), thereby rendering on-chip implementation functionally different from the original one. When loaded onto an on-chip memory, the secret keys restore the original functionality of the design. Through security-aware synthesis that creates a controllable mismatch between the reverse-engineered netlist and original design, SFLL provides a quantifiable and provable resilience trade-off between all known and anticipated attacks. We demonstrate the application of SFLL to large designs (textgreater100K gates) using a computer-aided design (CAD) framework that ensures attaining the desired security level at minimal implementation cost, 8%, 5%, and 0.5% for area, power, and delay, respectively. In addition to theoretical proofs and simulation confirmation of SFLL's security, we also report results from the silicon implementation of SFLL on an ARM Cortex-M0 microprocessor in 65nm technology.

Government-sanctioned and market-based anti-piracy measures can both mitigate economic harm from piracy.