Biblio

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.

Providing efficient protection against energy consumption based side channel attacks (SCAs) for block ciphers is a relevant topic for the research community, as current overheads are in the 100x range. Unprofiled SCAs exploit information leakage from the outmost rounds of a cipher; we propose a solution encasing it between keyed transformations amenable to an efficient SCA protection. Our solution can be employed as a drop in replacement for an unprotected implementation, or be retrofit to an existing one, while retaining communication capabilities with legacy insecure endpoints. Experiments on a Cortex-M4 μC, show performance improvements in the range of 60x, compared with available solutions.

All modern web browsers –- Internet Explorer, Firefox, Chrome, Opera, and Safari –- have a core rendering engine written in C++. This language choice was made because it affords the systems programmer complete control of the underlying hardware features and memory in use, and it provides a transparent compilation model. Unfortunately, this language is complex (especially to new contributors!), challenging to write correct parallel code in, and highly susceptible to memory safety issues that potentially lead to security holes. Servo is a project started at Mozilla Research to build a new web browser engine that preserves the capabilities of these other browser engines but also both takes advantage of the recent trends in parallel hardware and is more memory-safe. We use a new language, Rust, that provides us a similar level of control of the underlying system to C++ but which statically prevents many memory safety issues and provides direct support for parallelism and concurrency. In this paper, we show how a language with an advanced type system can address many of the most common security issues and software engineering challenges in other browser engines, while still producing code that has the same performance and memory profile. This language is also quite accessible to new open source contributors and employees, even those without a background in C++ or systems programming. We also outline several pitfalls encountered along the way and describe some potential areas for future improvement.

This paper evaluates a new video surveillance platform presented in a previous study, through an abandoned object detection task. The proposed platform has a function of automated detection and alerting, which is still a big challenge for a machine algorithm due to its recall-precision tradeoff problem. To achieve both high recall and high precision simultaneously, a hybrid approach using crowdsourcing after image analysis is proposed. This approach, however, is still not clear about what extent it can improve detection accuracy and raise quicker alerts. In this paper, the experiment is conducted for abandoned object detection, as one of the most common surveillance tasks. The results show that detection accuracy was improved from 50% (without crowdsourcing) to stable 95-100% (with crowdsourcing) by majority vote of 7 crowdworkers for each task. In contrast, alert time issue still remains open to further discussion since at least 7+ minutes are required to get the best performance.

We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.

The development of variable software, in general, and feature models, in particular, is an error-prone and time-consuming task. It gets increasingly more challenging with industrial-size models containing hundreds or thousands of features and constraints. Each change may lead to anomalies in the feature model such as making some features impossible to select. While the detection of anomalies is well-researched, giving explanations is still a challenge. Explanations must be as accurate and understandable as possible to support the developer in repairing the source of an error. We propose an efficient and generic algorithm for explaining different anomalies in feature models. Additionally, we achieve a benefit for the developer by computing short explanations expressed in a user-friendly manner and by emphasizing specific parts in explanations that are more likely to be the cause of an anomaly. We provide an open-source implementation in FeatureIDE and show its scalability for industrial-size feature models.

Smartphones have fueled a shift in the way we communicate with each other via Instant Messaging. With the convergence of Internet and telephony, new Over-The-Top (OTT) messaging applications (e.g., WhatsApp, Viber, WeChat etc.) have emerged as an important means of communication for millions of users. These applications use phone numbers as the only means of authentication and are becoming an attractive medium for attackers to deliver spam and carry out more targeted attacks. The universal reach of telephony along with its past trusted nature makes phone numbers attractive identifiers for reaching potential attack targets. In this paper, we explore the feasibility, automation, and scalability of a variety of targeted attacks that can be carried out by abusing phone numbers. These attacks can be carried out on different channels viz. OTT messaging applications, voice, e-mail, or SMS. We demonstrate a novel system that takes a phone number as an input, leverages information from applications like Truecaller and Facebook about the victim and his / her social network, checks the presence of phone number's owner (victim) on the attack channel (OTT messaging applications, voice, e-mail, or SMS), and finally targets the victim on the chosen attack channel. As a proof of concept, we enumerated through a random pool of 1.16 million phone numbers and demonstrated that targeted attacks could be crafted against the owners of 255,873 phone numbers by exploiting cross-application features. Due to the significantly increased user engagement via new mediums of communication like OTT messaging applications and ease with which phone numbers allow collection of pertinent information, there is a clear need for better protection of applications that rely on phone numbers.

The theoretical construct of a Trusted Third Party (TTP) has the potential to solve many security and privacy challenges. In particular, a TTP is an ideal way to achieve secure multiparty computation—a privacy-enhancing technique in which mutually distrusting participants jointly compute a function over their private inputs without revealing these inputs. Although there exist cryptographic protocols to achieve this, their performance often limits them to the two-party case, or to a small number of participants. However, many real-world applications involve thousands or tens of thousands of participants. Examples of this type of many-party application include privacy-preserving energy metering, location-based services, and mobile network roaming. Challenging the notion that a trustworthy TTP does not exist, recent research has shown how trusted hardware and remote attestation can be used to establish a sufficient level of assurance in a real system such that it can serve as a trustworthy remote entity (TRE). We explore the use of Intel SGX, the most recent and arguably most promising trusted hardware technology, as the basis for a TRE for many-party applications. Using privacy-preserving energy metering as a case study, we design and implement a prototype TRE using SGX, and compare its performance to a previous system based on the Trusted Platform Module (TPM). Our results show that even without specialized optimizations, SGX provides comparable performance to the optimized TPM system, and therefore has significant potential for large-scale many-party applications.

The deterministic nature of existing routing protocols has resulted into an ossified Internet with static and predictable network routes. This gives persistent attackers (e.g. eavesdroppers and DDoS attackers) plenty of time to study the network and identify the vulnerable (critical) links to plan devastating and stealthy attacks. Recently, Moving Target Defense (MTD) based approaches have been proposed to to defend against DoS attacks. However, MTD based approaches for route mutation are oriented towards re-configuring the parameters in Local Area Networks (LANs), and do not provide any protection against infrastructure level attacks, which inherently limits their use for mission critical services over the Internet infrastructure. To cope with these issues, we extend the current routing architecture to consider end-hosts as routing elements, and present a formal method based agile defense mechanism to embed resiliency in the existing cyber infrastructure. The major contributions of this paper include: (1) formalization of efficient and resilient End to End (E2E) reachability problem as a constraint satisfaction problem, which identifies the potential end-hosts to reach a destination while satisfying resilience and QoS constraints, (2) design and implementation of a novel decentralized End Point Route Mutation (EPRM) protocol, and (3) design and implementation of planning algorithm to minimize the overlap between multiple flows, for the sake of maximizing the agility in the system. Our PlanetLab based implementation and evaluation validates the correctness, effectiveness and scalability of the proposed approach.

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.

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.

In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to Łukasiewicz logic. The MaxSAT problem for a set of formulae Φ is the problem of finding an assignment to the variables in Φ that satisfies the maximum number of formulae. Three possible solutions (encodings) are proposed to the new problem: (1) Disjunctive Linear Relations (DLRs), (2)Mixed Integer Linear Programming (MILP) and (3)Weighted Constraint Satisfaction Problem (WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have numerous applications in optimization problems that involve vagueness.

Wireless sensor networks (WSN) are useful in many practical applications including agriculture, military and health care systems. However, the nodes in a sensor network are constrained by energy and hence the lifespan of such sensor nodes are limited due to the energy problem. Temporal logics provide a facility to predict the lifetime of sensor nodes in a WSN using the past and present traffic and environmental conditions. Moreover, fuzzy logic helps to perform inference under uncertainty. When fuzzy logic is combined with temporal constraints, it increases the accuracy of decision making with qualitative information. Hence, a new data collection and cluster based energy efficient routing algorithm is proposed in this paper by extending the existing LEACH protocol. Extensions are provided in this work by including fuzzy temporal rules for making data collection and routing decisions. Moreover, this proposed work uses fuzzy temporal logic for forming clusters and to perform cluster based routing. The main difference between other cluster based routing protocols and the proposed protocol is that two types of cluster heads are used here, one for data collection and other for routing. In this research work we conducted an experiment and it is observed that the proposed fuzzy cluster based routing algorithm with temporal constrains enhances the network life time reduces the energy consumption and enhances the quality of service by increasing the packet delivery ratio by reducing the delay.

Identifying threats contained within encrypted network traffic poses a unique set of challenges. It is important to monitor this traffic for threats and malware, but do so in a way that maintains the integrity of the encryption. Because pattern matching cannot operate on encrypted data, previous approaches have leveraged observable metadata gathered from the flow, e.g., the flow's packet lengths and inter-arrival times. In this work, we extend the current state-of-the-art by considering a data omnia approach. To this end, we develop supervised machine learning models that take advantage of a unique and diverse set of network flow data features. These data features include TLS handshake metadata, DNS contextual flows linked to the encrypted flow, and the HTTP headers of HTTP contextual flows from the same source IP address within a 5 minute window. We begin by exhibiting the differences between malicious and benign traffic's use of TLS, DNS, and HTTP on millions of unique flows. This study is used to design the feature sets that have the most discriminatory power. We then show that incorporating this contextual information into a supervised learning system significantly increases performance at a 0.00% false discovery rate for the problem of classifying encrypted, malicious flows. We further validate our false positive rate on an independent, real-world dataset.

Compression is desirable for network applications as it saves bandwidth. Differently, when data is compressed before being encrypted, the amount of compression leaks information about the amount of redundancy in the plaintext. This side channel has led to the “Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext (BREACH)” attack on web traffic protected by the TLS protocol. The general guidance to prevent this attack is to disable HTTP compression, preserving confidentiality but sacrificing bandwidth. As a more sophisticated countermeasure, fixed-dictionary compression was introduced in 2015 enabling compression while protecting high-value secrets, such as cookies, from attacks. The fixed-dictionary compression method is a cryptographically sound countermeasure against the BREACH attack, since it is proven secure in a suitable security model. In this project, we integrate the fixed-dictionary compression method as a countermeasure for BREACH attack, for real-world client-server setting. Further, we measure the performance of the fixed-dictionary compression algorithm against the DEFLATE compression algorithm. The results evident that, it is possible to save some amount of bandwidth, with reasonable compression/decompression time compared to DEFLATE operations. The countermeasure is easy to implement and deploy, hence, this would be a possible direction to mitigate the BREACH attack efficiently, rather than stripping off the HTTP compression entirely.

Within few years, Cloud computing has emerged as the most promising IT business model. Thanks to its various technical and financial advantages, Cloud computing continues to convince every day new users coming from scientific and industrial sectors. To satisfy the various users' requirements, Cloud providers must maximize the performance of their IT resources to ensure the best service at the lowest cost. The performance optimization efforts in the Cloud can be achieved at different levels and aspects. In the present paper, we propose to introduce a fuzzy logic process in scheduling strategy for public Cloud in order to improve the response time, processing time and total cost. In fact, fuzzy logic has proven his ability to solve the problem of optimization in several fields such as data mining, image processing, networking and much more.