Biblio

From financial services platforms to social networks to vehicle control, software has come to mediate many activities of daily life. Governing bodies and standards organizations have responded to this trend by creating regulations and standards to address issues such as safety, security and privacy. In this environment, the compliance of software development to standards and regulations has emerged as a key requirement. Compliance claims and arguments are often captured in assurance cases, with linked evidence of compliance. Evidence can come from testcases, verification proofs, human judgement, or a combination of these. That is, we try to build (safety-critical) systems carefully according to well justified methods and articulate these justifications in an assurance case that is ultimately judged by a human. Yet software is deeply rooted in uncertainty making pragmatic assurance more inductive than deductive: most of complex open-world functionality is either not completely specifiable (due to uncertainty) or it is not cost-effective to do so, and deductive verification cannot happen without specification. Inductive assurance, achieved by sampling or testing, is easier but generalization from finite set of examples cannot be formally justified. And of course the recent popularity of constructing software via machine learning only worsens the problem - rather than being specified by predefined requirements, machine-learned components learn existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, this ability is extremely useful for hard-to specify concepts, e.g., the definition of a pedestrian in a pedestrian detection component of a vehicle. On the other, safety assessment and assurance of such components becomes very challenging. In this talk, I focus on two specific approaches to arguing about safety and security of software under uncertainty. The first one is a framework for managing uncertainty in assurance cases (for "conventional" and "machine-learned" systems) by systematically identifying, assessing and addressing it. The second is recent work on supporting development of requirements for machine-learned components in safety-critical domains.

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.

The paper discusses the architectural, algorithmic and computing aspects of creating and operating a class of expert system for managing technological safety of an enterprise, in conditions of a large flow of diagnostic variables. The algorithm for finding a faulty technological chain uses expert information, formed as a set of evidence on the influence of diagnostic variables on the correctness of the technological process. Using the Dempster-Schafer trust function allows determining the overall probability measure on subsets of faulty process chains. To combine different evidence, the orthogonal sums of the base probabilities determined for each evidence are calculated. The procedure described above is converted into the rules of the knowledge base production. The description of the developed prototype of the expert system, its architecture, algorithmic and software is given. The functionality of the expert system and configuration tools for a specific type of production are under discussion.

Several operational and economic factors impact the patching decisions of critical infrastructures. The constraints imposed by such factors could prevent organizations from fully remedying all of the vulnerabilities that expose their (critical) assets to risk. Therefore, an involved decision maker (e.g. security officer) has to strategically decide on the allocation of possible remediation efforts towards minimizing the inherent security risk. This, however, involves the use of comparative judgments to prioritize risks and remediation actions. Throughout this work, the security risk is quantified using the security metric Time-To-Compromise (TTC). Our main contribution is to provide a generic TTC estimator to comparatively assess the security posture of computer networks taking into account interdependencies between the network components, different adversary skill levels, and characteristics of (known and zero-day) vulnerabilities. The presented estimator relies on a stochastic TTC model and Monte Carlo simulation (MCS) techniques to account for the input data variability and inherent prediction uncertainties.

Since the term “Fog Computing” has been coined by Cisco Systems in 2012, security and privacy issues of this promising paradigm are still open challenges. Among various security challenges, Access Control is a crucial concern for all cloud computing-like systems (e.g. Fog computing, Mobile edge computing) in the IoT era. Therefore, assigning the precise level of access in such an inherently scalable, heterogeneous and dynamic environment is not easy to perform. This work defines the uncertainty challenge for authentication phase of the access control in fog computing because on one hand fog has a number of characteristics that amplify uncertainty in authentication and on the other hand applying traditional access control models does not result in a flexible and resilient solution. Therefore, we have proposed a novel prediction model based on the extension of Attribute Based Access Control (ABAC) model. Our data-driven model is able to handle uncertainty in authentication. It is also able to consider the mobility of mobile edge devices in order to handle authentication. In doing so, we have built our model using and comparing four supervised classification algorithms namely as Decision Tree, Naïve Bayes, Logistic Regression and Support Vector Machine. Our model can achieve authentication performance with 88.14% accuracy using Logistic Regression.

Large-scale failures in communication networks due to natural disasters or malicious attacks can severely affect critical communications and threaten lives of people in the affected area. In the absence of a proper communication infrastructure, rescue operation becomes extremely difficult. Progressive and timely network recovery is, therefore, a key to minimizing losses and facilitating rescue missions. To this end, we focus on network recovery assuming partial and uncertain knowledge of the failure locations. We proposed a progressive multi-stage recovery approach that uses the incomplete knowledge of failure to find a feasible recovery schedule. Next, we focused on failure recovery of multiple interconnected networks. In particular, we focused on the interaction between a power grid and a communication network. Then, we focused on network monitoring techniques that can be used for diagnosing the performance of individual links for localizing soft failures (e.g. highly congested links) in a communication network. We studied the optimal selection of the monitoring paths to balance identifiability and probing cost. Finally, we addressed, a minimum disruptive routing framework in software defined networks. Extensive experimental and simulation results show that our proposed recovery approaches have a lower disruption cost compared to the state-of-the-art while we can configure our choice of trade-off between the identifiability, execution time, the repair/probing cost, congestion and the demand loss.

Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain, adversarial evidence by enhancing Collective Subjective Logic (CSL) which is developed by combining SL and Probabilistic Soft Logic (PSL). The key idea behind the Adv-COI is to learn a model of robust ways against uncertain, adversarial evidence which is formulated as a min-max problem. We validate the out-performance of the Adv-COI compared to baseline models and its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean absolute error in the expected truth probability while producing the lowest running time among all.

The potential risk of agricultural product supply chain is huge because of the complex attributes specific to it. Actually the safety incidents of edible agricultural product emerge frequently in recent years, which expose the fragility of the agricultural product supply chain. In this paper the possible risk factors in agricultural product supply chain is analyzed in detail, the agricultural product supply chain risk evaluation index system and evaluation model are established, and an empirical analysis is made using BP neural network method. The results show that the risk ranking of the simulated evaluation is consistent with the target value ranking, and the risk assessment model has a good generalization and extension ability, and the model has a good reference value for preventing agricultural product supply chain risk.

As opposed to a traditional power grid, a smart grid can help utilities to save energy and therefore reduce the cost of operation. It also increases reliability of the system In smart grids the quality of monitoring and control can be adequately improved by incorporating computing and intelligent communication knowledge. However, this exposes the system to false data injection (FDI) attacks and the system becomes vulnerable to intrusions. Therefore, it is important to detect such false data injection attacks and provide an algorithm for the protection of system against such attacks. In this paper a comparison between three FDI detection methods has been made. An H2 control method has then been proposed to detect and control the false data injection on a 12th order model of a smart grid. Disturbances and uncertainties were added to the system and the results show the system to be fully controllable. This paper shows the implementation of a feedback controller to fully detect and mitigate the false data injection attacks. The controller can be incorporated in real life smart grid operations.

Antifragile systems enhance their capabilities and become stronger when exposed to adverse conditions, stresses or attacks, making antifragility a desirable property for cyber defence systems that operate in contested military environments. Self-improvement in autonomic systems refers to the improvement of their self-* capabilities, so that they are able to (a) better handle previously known (anticipated) situations, and (b) deal with previously unknown (unanticipated) situations. In this position paper, we present a vision of using self-improvement through learning to achieve antifragility in autonomic cyber defence systems. We first enumerate some of the major challenges associated with realizing distributed self-improvement. We then propose a reference model for middleware frameworks for self-improving autonomic systems and a set of desirable features of such frameworks.

Hardware vulnerabilities are often due to design mistakes because the designer does not sufficiently consider potential security vulnerabilities at the design stage. As a result, various security solutions have been developed to protect ICs, among which the language-based hardware security verification serves as a promising solution. The verification process will be performed while compiling the HDL of the design. However, similar to other formal verification methods, the language-based approach also suffers from scalability issue. Furthermore, existing solutions either lead to hardware overhead or are not designed for vulnerable or malicious logic detection. To alleviate these challenges, we propose a new language based framework, QIF-Verilog, to evaluate the trustworthiness of a hardware system at register transfer level (RTL). This framework introduces a quantified information flow (QIF) model and extends Verilog type systems to provide more expressiveness in presenting security rules; QIF is capable of checking the security rules given by the hardware designer. Secrets are labeled by the new type and then parsed to data flow, to which a QIF model will be applied. To demonstrate our approach, we design a compiler for QIF-Verilog and perform vulnerability analysis on benchmarks from Trust-Hub and OpenCore. We show that Trojans or design faults that leak information from circuit outputs can be detected automatically, and that our method evaluates the security of the design correctly.

Covert or low probability of detection communication is crucial to protect user privacy and provide a strong security. We analyze the joint impact of imperfect knowledge of the channel gain (channel uncertainty) and noise power (noise uncertainty) on the average probability of detection error at the eavesdropper and the covert throughput in Rayleigh fading channel. We characterize the covert throughput gain provided by the channel uncertainty as well as the covert throughput loss caused by the channel fading as a function of the noise uncertainty. Our result shows that the channel fading is essential to hiding the signal transmission, particularly when the noise uncertainty is below a threshold and/or the receive SNR is above a threshold. The impact of the channel uncertainty on the average probability of detection error and covert throughput is more significant when the noise uncertainty is larger.

Cloud service has the computing characteristics of self-organizing strain on demand, which is prone to failure or loss of responsibility in its extensive application. In the prediction or accountability of this, the modeling of cloud service structure becomes an insurmountable priority. This paper reviews the modeling of cloud service network architecture. It mainly includes: Firstly, the research status of cloud service structure modeling is analyzed and reviewed. Secondly, the classification of time-varying structure of cloud services and the classification of time-varying structure modeling methods are summarized as a whole. Thirdly, it points out the existing problems. Finally, for cloud service accountability, research approach of time-varying structure modeling is proposed.

The choice of threshold is an important part of fault diagnosis. Most of the current methods use a constant threshold for detection and it is difficult to meet the robustness and sensitivity requirements of the diagnosis system. This article develops a dynamic threshold algorithm for aircraft engine fault detection and isolation systems. The algorithm firstly analyzes the bounded norm uncertainty that may appear in the process of model based on the state space equation, and gives the time domain response range calculation formula under the influence of uncertain parameters; then the Kalman filter is combined to calculate the threshold with the real-time change of state; the simulation is performed at the end. The simulation results show that dynamic threshold range changes with status in real time.

Current methods for producing and disseminating analytic products contribute to the latency of relaying actionable information and analysis to the U.S. Intelligence Community's (IC's) principal customers, U.S. policymakers and warfighters. To circumvent these methods, which can often serve as a bottleneck, we report on the results of a public prize challenge that explored the potential for artificial intelligence techniques to generate useful analytic products. The challenge tasked solvers to develop algorithms capable of searching and processing nearly 15,000 unstructured text files into a 1-2 page analytic product without human intervention; these analytic products were subsequently evaluated and scored using established IC methodologies and criteria. Experimental results from this challenge demonstrate the promise for the ma-chine generation of analytic products to ensure that the IC warns and informs in a more timely fashion.

As we are living in the era of big data, high volumes of wide varieties of data which may be of different veracity (e.g., precise data, imprecise and uncertain data) are easily generated or collected at a high velocity in many real-life applications. Embedded in these big data is valuable knowledge and useful information, which can be discovered by big data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring merchandise items and/or events. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. Hence, in this paper, we present an item-centric algorithm for mining frequent patterns from big uncertain data. In recent years, big data have been gaining the attention from the research community as driven by relevant technological innovations (e.g., clouds) and novel paradigms (e.g., social networks). As big data are typically published online to support knowledge management and fruition processes, these big data are usually handled by multiple owners with possible secure multi-part computation issues. Thus, privacy and security of big data has become a fundamental problem in this research context. In this paper, we present, not only an item-centric algorithm for mining frequent patterns from big uncertain data, but also a privacy-preserving algorithm. In other words, we present- in this paper-a privacy-preserving item-centric algorithm for mining frequent patterns from big uncertain data. Results of our analytical and empirical evaluation show the effectiveness of our algorithm in mining frequent patterns from big uncertain data in a privacy-preserving manner.

Computational trust is the digital counterpart of the human notion of trust as applied in social systems. Its main purpose is to improve the reliability of interactions in online communities and of knowledge transfer in information management systems. Trust models are formal frameworks in which the notion of computational trust is described rigorously and where its dynamics are explained precisely. In this paper we will consider and extend a computational trust model, i.e., JØsang's Subjective Logic: we will show how this model is well-suited to describe the dynamics of computational trust, but lacks effective tools to compute initial trust values to feed in the model. To overcome some of the issues with subjective logic, we will introduce a logical language which can be employed to describe and reason about trust. The core ideas behind the logical language will turn out to be useful in computing initial trust values to feed into subjective logic. The aim of the paper is, therefore, that of providing an improvement on subjective logic.

Wireless Mesh Network (WMN) is a promising networking technology, which is cost effective, robust, easily maintainable and provides reliable service coverage. WMNs do not rely on a centralized administration and have a distributed nature in which nodes can participate in routing packets. Such design and structure makes WMNs vulnerable to a variety of security threats. Therefore, to ensure secure route discovery in WMNs, we propose a trust model which is based on Evidence- Based Subjective Logic (EBSL). The proposed trust model computes trust values of individual nodes and manages node reputation. We use watchdog detection mechanism to monitor selfish behavior in the network. A node's final trust value is calculated by aggregating the nodes direct and recommendation trust information. The proposed trust model ensures secure routing of packets by avoiding paths with untrusted nodes. The trust model is able to detect selfish behavior such as black-hole and gray-hole attacks.

Popularization of the Internet-of-Things (IoT) has brought widespread concerns on IoT security, especially in face of several recent security incidents related to IoT devices. Due to the resource-constrained nature of many IoT devices, security offloading has been proposed to provide good-enough security for IoT with minimum overhead on the devices. In this paper, we investigate the inevitable risk associated with security offloading: the unprotected and unmonitored transmission from IoT devices to the offloaded security mechanisms. An important challenge in modeling the security risk is the dynamic nature of IoT due to demand fluctuations and infrastructure instability. We propose a stochastic model to capture both the expected and worst-case security risks of an IoT system. We then propose a framework to efficiently address the optimal robust deployment of security mechanisms in IoT. We use results from extensive simulations to demonstrate the superb performance and efficiency of our approach compared to several other algorithms.

Cyber reconnaissance is the process of gathering information about a target network for the purpose of compromising systems within that network. Network-based deception has emerged as a promising approach to disrupt attackers' reconnaissance efforts. However, limited work has been done so far on measuring the effectiveness of network-based deception. Furthermore, given that Software-Defined Networking (SDN) facilitates cyber deception by allowing network traffic to be modified and injected on-the-fly, understanding the effectiveness of employing different cyber deception strategies is critical. In this paper, we present a model to study the reconnaissance surface of a network and model the process of gathering information by attackers as interactions with a cyber defensive system that may use deception. To capture the evolution of the attackers' knowledge during reconnaissance, we design a belief system that is updated by using a Bayesian inference method. For the proposed model, we present two metrics based on KL-divergence to quantify the effectiveness of network deception. We tested the model and the two metrics by conducting experiments with a simulated attacker in an SDN-based deception system. The results of the experiments match our expectations, providing support for the model and proposed metrics.

One of the effective ways to improve the quality of airport security (AS) is to improve the quality of management of the state of the system for countering acts of unlawful interference by intruders into the airports (SCAUI), which is a set of AS employees, technical systems and devices used for passenger screening, luggage, other operational procedures, as well as to protect the restricted areas of the airports. Proactive control of the SCAUI state includes ongoing conducting assessment of airport AS quality by experts, identification of SCAUI elements (functional state of AS employees, characteristics of technical systems and devices) that have a predominant influence on AS, and improvement of their performance. This article presents principles of the model and the method for conducting expert quality assessment of airport AS, whose application allows to increase the efficiency and quality of AS assessment by experts, and, consequently, the quality of SCAUI state control.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

In online payment, customers must transmit their personal and financial information through the website to conclude their purchase and pay the services or items selected. They may face possible fears from online transactions raised by their risk perception about financial or privacy loss. They may have concerns over the payment decision with the possible negative behaviors such as shopping cart abandonment. Therefore, customers have three major players that need to be addressed in online payment: the online seller, the payment page, and their own perception. However, few studies have explored these three players in an online purchasing environment. In this paper, we focus on the customer concerns and examine the antecedents of trust, payment security perception as well as their joint effect on two fundamentally important customers' aspects privacy concerns and financial fear perception. A total of 392 individuals participated in an online survey. The results highlight the importance, of the seller website's components (such as ease of use, security signs, and quality information) and their impact on the perceived payment security as well as their impact on customer's trust and financial fear perception. The objective of our study is to design a research model that explains the factors contributing to an online payment decision.

We consider a pharmaceutical supply chain where the manufacturer sources a customized product with unique attributes from a set of unreliable suppliers. We model the likelihood of a supplier to successfully deliver the product via Bayesian logistic regression and use simulation to obtain the posterior distribution of the unknown parameters of this model. We study the role of so-called input-model uncertainty in estimating the likelihood of the supply failure, which is the probability that none of the suppliers in a given supplier portfolio can successfully deliver the product. We investigate how the input-model uncertainty changes with respect to the characteristics of the historical data on the past realizations of the supplier performances and the product attributes.

At present, cloud computing technology has made outstanding contributions to the Internet in data unification and sharing applications. However, the problem of information security in cloud computing environment has to be paid attention to and effective measures have to be taken to solve it. In order to control the data security under cloud services, the DS evidence theory method is introduced. The trust management mechanism is established from the source of big data, and a cloud computing security assessment model is constructed to achieve the quantifiable analysis purpose of cloud computing security assessment. Through the simulation, the innovative way of quantifying the confidence criterion through big data trust management and DS evidence theory not only regulates the data credible quantification mechanism under cloud computing, but also improves the effectiveness of cloud computing security assessment, providing a friendly service support platform for subsequent cloud computing service.