Biblio

This paper considers a 2-player strategic game for network routing under link disruptions. Player 1 (defender) routes flow through a network to maximize her value of effective flow while facing transportation costs. Player 2 (attacker) simultaneously disrupts one or more links to maximize her value of lost flow but also faces cost of disrupting links. This game is strategically equivalent to a zero-sum game. Linear programming duality and the max-flow min-cut theorem are applied to obtain properties that are satisfied in any mixed Nash equilibrium. In any equilibrium, both players achieve identical payoffs. While the defender's expected transportation cost decreases in attacker's marginal value of lost flow, the attacker's expected cost of attack increases in defender's marginal value of effective flow. Interestingly, the expected amount of effective flow decreases in both these parameters. These results can be viewed as a generalization of the classical max-flow with minimum transportation cost problem to adversarial environments.

Users receive a multitude of digital- and physical- security advice every day. Indeed, if we implemented all the security advice we received, we would never leave our houses or use the Internet. Instead, users selectively choose some advice to accept and some (most) to reject; however, it is unclear whether they are effectively prioritizing what is most important or most useful. If we can understand from where and why users take security advice, we can develop more effective security interventions.

As a first step, we conducted 25 semi-structured interviews of a demographically broad pool of users. These interviews resulted in several interesting findings: (1) participants evaluated digital-security advice based on the trustworthiness of the advice source, but evaluated physical-security advice based on their intuitive assessment of the advice content; (2) negative-security events portrayed in well-crafted fictional narratives with relatable characters (such as those shown in TV or movies) may be effective teaching tools for both digital- and physical-security behaviors; and (3) participants rejected advice for many reasons, including finding that the advice contains too much marketing material or threatens their privacy.

Cyber-attacks and breaches are often detected too late to avoid damage. While “classical” reactive cyber defenses usually work only if we have some prior knowledge about the attack methods and “allowable” patterns, properly constructed redundancy-based anomaly detectors can be more robust and often able to detect even zero day attacks. They are a step toward an oracle that uses knowable behavior of a healthy system to identify abnormalities. In the world of Internet of Things (IoT), security, and anomalous behavior of sensors and other IoT components, will be orders of magnitude more difficult unless we make those elements security aware from the start. In this article we examine the ability of redundancy-based a nomaly detectors to recognize some high-risk and difficult to detect attacks on web servers—a likely management interface for many IoT stand-alone elements. In real life, it has taken long, a number of years in some cases, to identify some of the vulnerabilities and related attacks. We discuss practical relevance of the approach in the context of providing high-assurance Webservices that may belong to autonomous IoT applications and devices

This paper investigates security of Kepler scientific workflow engine. We are especially interested in Kepler-based scientific workflows that may operate in cloud environments. We find that (1) three security properties (i.e., input validation, remote access validation, and data integrity) are essential for making Kepler-based workflows more secure, and (2) that use of the Kepler provenance module may help secure Kepler based workflows. We implemented a prototype security enhanced Kepler engine to demonstrate viability of use of the Kepler provenance module in provision and management of the desired security properties.
 

The overarching vision of social machines is to facilitate social processes by having computers provide administrative support. We conceive of a social machine as a sociotechnical system (STS): a software-supported system in which autonomous principals such as humans and organizations interact to exchange information and services. Existing approaches for social machines emphasize the technical aspects and inadequately support the meanings of social processes, leaving them informally realized in human interactions. We posit that a fundamental rethinking is needed to incorporate accountability, essential for addressing the openness of the Web and the autonomy of its principals. We introduce Interaction-Oriented Software Engineering (IOSE) as a paradigm expressly suited to capturing the social basis of STSs. Motivated by promoting openness and autonomy, IOSE focuses not on implementation but on social protocols, specifying how social relationships, characterizing the accountability of the concerned parties, progress as they interact. Motivated by providing computational support, IOSE adopts the accountability representation to capture the meaning of a social machine's states and transitions.

We demonstrate IOSE via examples drawn from healthcare. We reinterpret the classical software engineering (SE) principles for the STS setting and show how IOSE is better suited than traditional software engineering for supporting social processes. The contribution of this paper is a new paradigm for STSs, evaluated via conceptual analysis.

Realizing the benefits of SDN for many network management applications (e.g., traffic engineering, service chaining, topology reconfiguration) involves addressing complex optimizations that are central to these problems. Unfortunately, such optimization problems require (a) significant manual effort and expertise to express and (b) non-trivial computation and/or carefully crafted heuristics to solve. Our goal is to simplify the deployment of SDN applications using general high-level abstractions for capturing optimization requirements from which we can efficiently generate optimal solutions. To this end, we present SOL, a framework that demonstrates that it is possible to simultaneously achieve generality and efficiency. The insight underlying SOL is that many SDN applications can be recast within a unifying path-based optimization abstraction. Using this, SOL can efficiently generate near-optimal solutions and device configurations to implement them. We show that SOL provides comparable or better scalability than custom optimization solutions for diverse applications, allows a balancing of optimality and route churn per reconfiguration, and interfaces with modern SDN controllers.

Anonymous communications are important for many of the applications of mobile ad hoc networks (MANETs) deployed in adversary environments. A major requirement on the network is the ability to provide unidentifiability and unlinkability for mobile nodes and their traffic. Although a number of anonymous secure routing protocols have been proposed, the requirement is not fully satisfied. The existing protocols are vulnerable to the attacks of fake routing packets or denial-of-service broadcasting, even the node identities are protected by pseudonyms. In this paper, we propose a new routing protocol, i.e., authenticated anonymous secure routing (AASR), to satisfy the requirement and defend against the attacks. More specifically, the route request packets are authenticated by a group signature, to defend against potential active attacks without unveiling the node identities. The key-encrypted onion routing with a route secret verification message is designed to prevent intermediate nodes from inferring a real destination. Simulation results have demonstrated the effectiveness of the proposed AASR protocol with improved performance as compared with the existing protocols.

Atomic sets are a synchronization mechanism in which the programmer specifies the groups of data that must be ac- cessed as a unit. The compiler can check this specifica- tion for consistency, detect deadlocks, and automatically add the primitives to prevent interleaved access. Atomic sets relieve the programmer from the burden of recognizing and pruning execution paths which lead to interleaved ac- cess, thereby reducing the potential for data races. However, manually converting programs from lock-based synchroniza- tion to atomic sets requires reasoning about the program’s concurrency structure, which can be a challenge even for small programs. Our analysis eliminates the challenge by automating the reasoning. Our implementation of the anal- ysis allowed us to derive the atomic sets for large code bases such as the Java collections framework in a matter of min- utes. The analysis is based on execution traces; assuming all traces reflect intended behavior, our analysis enables safe concurrency by preventing unobserved interleavings which may harbor latent Heisenbugs.

Session types have been proposed as a means of statically verifying implementations of communication protocols. Although prior work has been successful in verifying some classes of protocols, it does not cope well with parameterized, multi-actor scenarios with inherent asynchrony. For example, the sliding window protocol is inexpressible in previously proposed session type systems. This paper describes System-A, a new typing language which overcomes many of the expressiveness limitations of prior work. System-A explicitly supports asynchrony and parallelism, as well as multiple forms of parameterization. We define System-A and show how it can be used for the static verification of a large class of asynchronous communication protocols.

Verification algorithms for networks of nonlinear hybrid automata (HA) can aid us understand and control biological processes such as cardiac arrhythmia, formation of memory, and genetic regulation. We present an algorithm for over-approximating reach sets of networks of nonlinear HA which can be used for sound and relatively complete invariant checking. First, it uses automatically computed input-to-state discrepancy functions for the individual automata modules in the network A for constructing a low-dimensional model M. Simulations of both A and M are then used to compute the reach tubes for A. These techniques enable us to handle a challenging verification problem involving a network of cardiac cells, where each cell has four continuous variables and 29 locations. Our prototype tool can check bounded-time invariants for networks with 5 cells (20 continuous variables, 29⁵ locations) typically in less than 15 minutes for up to reasonable time horizons. From the computed reach tubes we can infer biologically relevant properties of the network from a set of initial states.

Design and testing of pacemaker is challenging because of the need to capture the interaction between the physical processes (e.g. voltage signal in cardiac tissue) and the embedded software (e.g. a pacemaker). At the same time, there is a growing need for design and certification methodologies that can provide quality assurance for the embedded software. We describe recent progress in simulation-based techniques that are capable of ensuring guaranteed coverage. Our methods employ discrep- ancy functions, which impose bounds on system dynamics, and proceed through iteratively constructing over-approximations of the reachable set of states. We are able to prove time bounded safety or produce counterexamples. We illustrate the techniques by analyzing a family of pacemaker designs against time duration requirements and synthesize safe parameter ranges. We conclude by outlining the potential uses of this technology to improve the safety of medical device designs.

Applications in mobile marketplaces may leak private user information without notification. Existing mobile platforms provide little information on how applications use private user data, making it difficult for experts to validate appli- cations and for users to grant applications access to their private data. We propose a user-aware-privacy-control approach, which reveals how private information is used inside applications. We compute static information flows and classify them as safe/un- safe based on a tamper analysis that tracks whether private data is obscured before escaping through output channels. This flow information enables platforms to provide default settings that expose private data for only safe flows, thereby preserving privacy and minimizing decisions required from users. We build our approach into TouchDe- velop, an application-creation environment that allows users to write scripts on mobile devices and install scripts published by other users. We evaluate our approach by studying 546 scripts published by 194 users, and the results show that our approach effectively reduces the need to make access-granting choices to only 10.1 % (54) of all scripts. We also conduct a user survey that involves 50 TouchDevelop users to assess the effectiveness and usability of our approach. The results show that 90 % of the users consider our approach useful in protecting their privacy, and 54 % prefer our approach over other privacy-control approaches.

Cheap ubiquitous computing enables the collectionof massive amounts of personal data in a wide variety of domains.Many organizations aim to share such data while obscuring fea-tures that could disclose identities or other sensitive information.Much of the data now collected exhibits weak structure (e.g.,natural language text) and machine learning approaches havebeen developed to identify and remove sensitive entities in suchdata. Learning-based approaches are never perfect and relyingupon them to sanitize data can leak sensitive information as aconsequence. However, a small amount of risk is permissiblein practice, and, thus, our goal is to balance the value ofdata published and the risk of an adversary discovering leakedsensitive information. We model data sanitization as a gamebetween 1) a publisher who chooses a set of classifiers to applyto data and publishes only instances predicted to be non-sensitiveand 2) an attacker who combines machine learning and manualinspection to uncover leaked sensitive entities (e.g., personal names). We introduce an iterative greedy algorithm for thepublisher that provably executes no more than a linear numberof iterations, and ensures a low utility for a resource-limitedadversary. Moreover, using several real world natural languagecorpora, we illustrate that our greedy algorithm leaves virtuallyno automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93% of the original data, and completes after at most 5 iterations.

The quantity of personal data gathered by service providers via our daily activities continues to grow at a rapid pace. The sharing, and the subsequent analysis of, such data can support a wide range of activities, but concerns around privacy often prompt an organization to transform the data to meet certain protection models (e.g., k-anonymity or E-differential privacy). These models, however, are based on simplistic adversarial frameworks, which can lead to both under- and over-protection. For instance, such models often assume that an adversary attacks a protected record exactly once. We introduce a principled approach to explicitly model the attack process as a series of steps. Specically, we engineer a factored Markov decision process (FMDP) to optimally plan an attack from the adversary's perspective and assess the privacy risk accordingly. The FMDP captures the uncertainty in the adversary's belief (e.g., the number of identied individuals that match the de-identified data) and enables the analysis of various real world deterrence mechanisms beyond a traditional protection model, such as a penalty for committing an attack. We present an algorithm to solve the FMDP and illustrate its efficiency by simulating an attack on publicly accessible U.S. census records against a real identied resource of over 500,000 individuals in a voter registry. Our results demonstrate that while traditional privacy models commonly expect an adversary to attack exactly once per record, an optimal attack in our model may involve exploiting none, one, or more indiviuals in the pool of candidates, depending on context.

We examine the problem of aggregating the results of multiple anti-virus (AV) vendors' detectors into a single authoritative ground-truth label for every binary. To do so, we adapt a well-known generative Bayesian model that postulates the existence of a hidden ground truth upon which the AV labels depend. We use training based on Expectation Maximization for this fully unsupervised technique. We evaluate our method using 279,327 distinct binaries from VirusTotal, each of which appeared for the rst time between January 2012 and June 2014.

Our evaluation shows that our statistical model is consistently more accurate at predicting the future-derived ground truth than all unweighted rules of the form \k out of n" AV detections. In addition, we evaluate the scenario where partial ground truth is available for model building. We train a logistic regression predictor on the partial label information. Our results show that as few as a 100 randomly selected training instances with ground truth are enough to achieve 80% true positive rate for 0.1% false positive rate. In comparison, the best unweighted threshold rule provides only 60% true positive rate at the same false positive rate.

It is critical to ensure that network policy remains consistent during state transitions. However, existing techniques impose a high cost in update delay, and/or FIB space. We propose the Customizable Consistency Generator (CCG), a fast and generic framework to support customizable consistency policies during network updates. CCG effectively reduces the task of synthesizing an update plan under the constraint of a given consistency policy to a verification problem, by checking whether an update can safely be installed in the network at a particular time, and greedily processing network state transitions to heuristically minimize transition delay. We show a large class of consistency policies are guaranteed by this greedy heuristic alone; in addition, CCG makes judicious use of existing heavier-weight network update mechanisms to provide guarantees when necessary. As such, CCG nearly achieves the “best of both worlds”: the efficiency of simply passing through updates in most cases, with the consistency guarantees of more heavyweight techniques. Mininet and physical testbed evaluations demonstrate CCG’s capability to achieve various types of consistency, such as path and bandwidth properties, with zero switch memory overhead and up to a 3× delay reduction compared to previous solutions.

A fundamental drawback of current anomaly detection systems (ADSs) is the ability of a skilled attacker to evade detection. This is due to the flawed assumption that an attacker does not have any information about an ADS. Advanced persistent threats that are capable of monitoring network behavior can always estimate some information about ADSs which makes these ADSs susceptible to evasion attacks. Hence in this paper, we first assume the role of an attacker to launch evasion attacks on anomaly detection systems. We show that the ADSs can be completely paralyzed by parameter estimation attacks. We then present a mathematical model to measure evasion margin with the aim to understand the science of evasion due to ADS design. Finally, to minimize the evasion margin, we propose a key-based randomization scheme for existing ADSs and discuss its robustness against evasion attacks. Case studies are presented to illustrate the design methodology and extensive experimentation is performed to corroborate the results.
 

Phishing is an act of technology-based deception that targets individuals to obtain information. To minimize the number of phishing attacks, factors that influence the ability to identify phishing attempts must be examined. The present study aimed to determine how individual differences relate to performance on a phishing task. Undergraduate students completed a questionnaire designed to assess impulsivity, trust, personality characteristics, and Internet/security habits. Participants performed an email task where they had to discriminate between legitimate emails and phishing attempts. Researchers assessed performance in terms of correctly identifying all email types (overall accuracy) as well as accuracy in identifying phishing emails (phishing accuracy). Results indicated that overall and phishing accuracy each possessed unique trust, personality, and impulsivity predictors, but shared one significant behavioral predictor. These results present distinct predictors of phishing susceptibility that should be incorporated in the development of anti-phishing technology and training.

Software as a Service (SaaS) is the most prevalent service delivery mode for cloud systems. This paper surveys common security vulnerabilities and corresponding countermeasures for SaaS. It is primarily focused on the work published in the last five years. We observe current SaaS security trends and a lack of sufficiently broad and robust countermeasures in some of the SaaS security area such as Identity and Access management due to the growth of SaaS applications.
 

Software-defined networking (SDN) can enable diverse network management applications such as traffic engineering, service chaining, network function outsourcing, and topology reconfiguration. Realizing the benefits of SDN for these applications, however, entails addressing complex network optimizations that are central to these problems. Unfortunately, such optimization problems require significant manual effort and expertise to express and non-trivial computation and/or carefully crafted heuristics to solve. Our vision is to simplify the deployment of SDN applications using general high-level abstractions for capturing optimization requirements from which we can efficiently generate optimal solutions. To this end, we present SOL, a framework that demonstrates that it is indeed possible to simultaneously achieve generality and efficiency. The insight underlying SOL is that SDN applications can be recast within a unifying path-based optimization abstraction, from which it efficiently generates near-optimal solutions, and device configurations to implement those solutions. We illustrate the generality of SOL by prototyping diverse and new applications. We show that SOL simplifies the development of SDN-based network optimization applications and provides comparable or better scalability than custom optimization solutions.

An identity authentication scheme is proposed combining with biometric encryption, public key cryptography of homomorphism and predicate encryption technology under the cloud computing environment. Identity authentication scheme is proposed based on the voice and homomorphism technology. The scheme is divided into four stages, register and training template stage, voice login and authentication stage, authorization stage, and audit stage. The results prove the scheme has certain advantages in four aspects.

Most of the detection approaches like Signature based, Anomaly based and Specification based are not able to analyze and detect all types of malware. Signature-based approach for malware detection has one major drawback that it cannot detect zero-day attacks. The fundamental limitation of anomaly based approach is its high false alarm rate. And specification-based detection often has difficulty to specify completely and accurately the entire set of valid behaviors a malware should exhibit. Modern malware developers try to avoid detection by using several techniques such as polymorphic, metamorphic and also some of the hiding techniques. In order to overcome these issues, we propose a new approach for malware analysis and detection that consist of the following twelve stages Inbound Scan, Inbound Attack, Spontaneous Attack, Client-Side Exploit, Egg Download, Device Infection, Local Reconnaissance, Network Surveillance, & Communications, Peer Coordination, Attack Preparation, and Malicious Outbound Propagation. These all stages will integrate together as interrelated process in our proposed approach. This approach had solved the limitations of all the three approaches by monitoring the behavioral activity of malware at each any every stage of life cycle and then finally it will give a report of the maliciousness of the files or software's.

An intelligent recovery evaluation system is presented for objective assessment and performance monitoring of anterior cruciate ligament reconstructed (ACL-R) subjects. The system acquires 3-D kinematics of tibiofemoral joint and electromyography (EMG) data from surrounding muscles during various ambulatory and balance testing activities through wireless body-mounted inertial and EMG sensors, respectively. An integrated feature set is generated based on different features extracted from data collected for each activity. The fuzzy clustering and adaptive neuro-fuzzy inference techniques are applied to these integrated feature sets in order to provide different recovery progress assessment indicators (e.g., current stage of recovery, percentage of recovery progress as compared to healthy group, etc.) for ACL-R subjects. The system was trained and tested on data collected from a group of healthy and ACL-R subjects. For recovery stage identification, the average testing accuracy of the system was found above 95% (95-99%) for ambulatory activities and above 80% (80-84%) for balance testing activities. The overall recovery evaluation performed by the proposed system was found consistent with the assessment made by the physiotherapists using standard subjective/objective scores. The validated system can potentially be used as a decision supporting tool by physiatrists, physiotherapists, and clinicians for quantitative rehabilitation analysis of ACL-R subjects in conjunction with the existing recovery monitoring systems.
 

The importance and potential advantages with a comprehensive product architecture description are well described in the literature. However, developing such a description takes additional resources, and it is difficult to maintain consistency with evolving implementations. This paper presents an approach and industrial experience which is based on architecture recovery from source code at truck manufacturer Scania CV AB. The extracted representation of the architecture is presented in several views and verified on CAN signal level. Lessons learned are discussed.