Biblio

Currently, different forms of ransomware are increasingly threatening Internet users. Modern ransomware encrypts important user data, and it is only possible to recover it once a ransom has been paid. In this article we show how software-defined networking can be utilized to improve ransomware mitigation. In more detail, we analyze the behavior of popular ransomware - CryptoWall - and, based on this knowledge, propose two real-time mitigation methods. Then we describe the design of an SDN-based system, implemented using OpenFlow, that facilitates a timely reaction to this threat, and is a crucial factor in the case of crypto ransomware. What is important is that such a design does not significantly affect overall network performance. Experimental results confirm that the proposed approach is feasible and efficient.

The prodigious amount of user-generated content continues to grow at an enormous rate. While it greatly facilitates the flow of information and ideas among people and communities, it may pose great threat to our individual privacy. In this paper, we demonstrate that the private traits of individuals can be inferred from user-generated content by using text classification techniques. Specifically, we study three private attributes on Twitter users: religion, political leaning, and marital status. The ground truth labels of the private traits can be readily collected from the Twitter bio field. Based on the tweets posted by the users and their corresponding bios, we show that text classification yields a high accuracy of identification of these personal attributes, which poses a great privacy risk on user-generated content. We further propose a constrained utility maximization framework for preserving user privacy. The goal is to maximize the utility of data when modifying the user-generated content, while degrading the prediction performance of the adversary. The KL divergence is minimized between the prior knowledge about the private attribute and the posterior probability after seeing the user-generated data. Based on this proposed framework, we investigate several specific data sanitization operations for privacy preservation: add, delete, or replace words in the tweets. We derive the exact transformation of the data under each operation. The experiments demonstrate the effectiveness of the proposed framework.

In this paper, we develop a new framework to analyze stability and stabilizability of Linear Switched Systems (LSS) as well as their gain computations. Our approach is based on a combination of state space operator descriptions and the Youla parametrization and provides a unified way for analysis and synthesis of LSS, and in fact of Linear Time Varying (LTV) systems, in any lp induced norm sense. By specializing to the l∞ case, we show how Linear Programming (LP) can be used to test stability, stabilizability and to synthesize stabilizing controllers that guarantee a near optimal closed-loop gain.

In this paper we develop a new framework to analyze stability and stabilizability of Linear Switched Systems (LSS) as well as their gain computations. Our approach is based on a combination of state space operator descritions and the Youda parametrization and provides a unified way to analysis an synthesis of LSS and in fact of Linear Time Varying (LTV) systems, in any lp induced norm sense. By specializing to the l case, we show how Linear Programming (LP) can be used to test stability, stabiliazbility and to synthesize stabilizing controllers that guarantee a near optimal closed-loop gain.

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 anomaly 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 Web-services that may belong to autonomous IoT applications and devices.

Embedded devices with constrained computational resources, such as wireless sensor network nodes, electronic tag readers, roadside units in vehicular networks, and smart watches and wristbands, are widely used in the Internet of Things. Many of such devices are deployed in untrustable environments, and others may be easy to lose, leading to possible capture by adversaries. Accordingly, in the context of security research, these devices are running in the white-box attack context, where the adversary may have total visibility of the implementation of the built-in cryptosystem with full control over its execution. It is undoubtedly a significant challenge to deal with attacks from a powerful adversary in white-box attack contexts. Existing encryption algorithms for white-box attack contexts typically require large memory use, varying from one to dozens of megabytes, and thus are not suitable for resource-constrained devices. As a countermeasure in such circumstances, we propose an ultra-lightweight encryption scheme for protecting the confidentiality of data in white-box attack contexts. The encryption is executed with secret components specialized for resource-constrained devices against white-box attacks, and the encryption algorithm requires a relatively small amount of static data, ranging from 48 to 92 KB. The security and efficiency of the proposed scheme have been theoretically analyzed with positive results, and experimental evaluations have indicated that the scheme satisfies the resource constraints in terms of limited memory use and low computational cost.

This paper presents an unsupervised method for systematically identifying anomalies in music datasets. The model integrates categorical regression and robust estimation techniques to infer anomalous scores in music clips. When applied to a music genre recognition dataset, the new method is able to detect corrupted, distorted, or mislabeled audio samples based on commonly used features in music information retrieval. The evaluation results show that the algorithm outperforms other anomaly detection methods and is capable of finding problematic samples identified by human experts. The proposed method introduces a preliminary framework for anomaly detection in music data that can serve as a useful tool to improve data integrity in the future.

The validation of simulation models (e.g., of electronic control units for vehicles) in industry is becoming increasingly challenging due to their growing complexity. To systematically assess the quality of such models, software metrics seem to be promising. In this paper we explore the use of software metrics and outlier analysis as a means to assess the quality of model-based software. More specifically, we investigate how results from regression analysis applied to measurement data received from size and complexity metrics can be mapped to software quality. Using the moving averages approach, models were fit to data received from over 65,000 software revisions for 71 simulation models that represent different electronic control units of real premium vehicles. Consecutive investigations using studentized deleted residuals and Cook’s Distance revealed outliers among the measurements. From these outliers we identified a subset, which provides meaningful information (anomalies) by comparing outlier scores with expert opinions. Eight engineers were interviewed separately for outlier impact on software quality. Findings were validated in consecutive workshops. The results show correlations between outliers and their impact on four of the considered quality characteristics. They also demonstrate the applicability of this approach in industry.

A two-server password-based authentication (2PA) protocol is a special kind of authentication primitive that provides additional protection for the user's password. Through a 2PA protocol, a user can distribute his low-entropy password between two authentication servers in the initialization phase and authenticate himself merely via a matching password in the login phase. No single server can learn any information about the user's password, nor impersonate the legitimate user to authenticate to the honest server. In this paper, we first formulate and realize the security definition of two-server password-based authentication in the well-known universal composability (UC) framework, which thus provides desirable properties such as composable security. We show that our construction is suitable for the asymmetric communication model in which one server acts as the front-end server interacting directly with the user and the other stays backstage. Then, we show that our protocol could be easily extended to more complicate password-based cryptographic protocols such as two-server password-authenticated key exchange (2PAKE) and two-server password-authenticated secret sharing (2PASS), which enjoy stronger security guarantees and better efficiency performances in comparison with the existing schemes.

This demo dramatically illustrates how replacing 'Classic' TCP congestion control (Reno, Cubic, etc.) with a 'Scalable' alternative like Data Centre TCP (DCTCP) keeps queuing delay ultra-low; not just for a select few light applications like voice or gaming, but even when a variety of interactive applications all heavily load the same (emulated) Internet access. DCTCP has so far been confined to data centres because it is too aggressive–-it starves Classic TCP flows. To allow DCTCP to be exploited on the public Internet, we developed DualQ Coupled Active Queue Management (AQM), which allows the two TCP types to safely co-exist. Visitors can test all these claims. As well as running Web-based apps, they can pan and zoom a panoramic video of a football stadium on a touch-screen, and experience how their personalized HD scene seems to stick to their finger, even though it is encoded on the fly on servers accessed via an emulated delay, representing 'the cloud'. A pair of VR goggles can be used at the same time, making a similar point. The demo provides a dashboard so that visitors can not only experience the interactivity of each application live, but they can also quantify it via a wide range of performance stats, updated live. It also includes controls so visitors can configure different TCP variants, AQMs, network parameters and background loads and immediately test the effect.

Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool.

We conducted a series of in-depth focus groups wherein users provided rationales for their own online privacy behaviors. Our data suggest that individuals often take action with little thought or evaluation, even showing surprise when confronted with their own behaviors. Our analysis yielded a battery of cognitive heuristics, i.e., mental shortcuts / rules of thumb, that users seem to employ when they disclose or withhold information at the spur of the moment. A total of 4 positive heuristics (promoting disclosure) and 4 negative heuristics (inhibiting disclosure) were discovered. An understanding of these heuristics can be valuable for designing interfaces that promote secure and trustworthy computing.

Visible Light Communication (VLC) allows to reuse a lighting infrastructure for communication while its main purpose of illumination can be carried out at the same time. Light sources based on Light Emitting Diodes (LEDs) are attractive as they are inexpensive, ubiquitous, and allow rapid modulation. This paper describes how to integrate smartphones into such a communication system that supports networking for a wide range of devices, such as toys with single LEDs as transmitter and receivers as well as interconnected LED light bulbs. The main challenge is how to employ the smartphone without any (hardware) modification as a receiver, using the integrated camera as a (slow) light sampling device. This paper presents a simple software-based solution, exploiting the rolling shutter effect and slow motion video capturing capabilities of latest smartphones to enable continuous reception and real-time integration into an existing VLC system. Evaluation results demonstrate a working prototype and report communication distances up to 3m and a maximum data throughput of more than 1200b/s, improving upon previous work.

Cyber-attacks are cheap, easy to conduct and often pose little risk in terms of attribution, but their impact could be lasting. The low attribution is because tracing cyber-attacks is primitive in the current network architecture. Moreover, even when attribution is known, the absence of enforcement provisions in international law makes cyber attacks tough to litigate, and hence attribution is hardly a deterrent. Rather than attributing attacks, we can re-look at cyber-attacks as societal events associated with social, political, economic and cultural (SPEC) motivations. Because it is possible to observe SPEC motives on the internet, social media data could be valuable in understanding cyber attacks. In this research, we use sentiment in Twitter posts to observe country-to-country perceptions, and Arbor Networks data to build ground truth of country-to-country DDoS cyber-attacks. Using this dataset, this research makes three important contributions: a) We evaluate the impact of heightened sentiments towards a country on the trend of cyber-attacks received by the country. We find that, for some countries, the probability of attacks increases by up to 27% while experiencing negative sentiments from other nations. b) Using cyber-attacks trend and sentiments trend, we build a decision tree model to find attacks that could be related to extreme sentiments. c) To verify our model, we describe three examples in which cyber-attacks follow increased tension between nations, as perceived in social media.

Since computers are machines, it's tempting to think of computer security as purely a technical problem. However, computing systems are created, used, and maintained by humans, and exist to serve the goals of human and institutional stakeholders. Consequently, effectively addressing the security problem requires understanding this human dimension.

In this tutorial, we discuss this challenge and survey principal research approaches to it.
 

Norms are a promising basis for governance in secure, collaborative environments---systems in which multiple principals interact. Yet, many aspects of norm-governance remain poorly understood, inhibiting adoption in real-life collaborative systems. This work focuses on the combined effects of sanction and the observability of the sanctioner in a secure, collaborative environment.  We present CARLOS, a multiagent simulation of graduate students performing research within a university lab setting, to explore these phenomena. The simulation consists of agents maintaining ``compliance" to enforced security norms while remaining ``motivated" as researchers. We hypothesize that (1) delayed observability of the environment would lead to greater motivation of agents to complete research tasks than immediate observability and (2) sanctioning a group for a violation would lead to greater compliance to security norms than sanctioning an individual. We find that only the latter hypothesis is supported.  Group sanction is an interesting topic for future research regarding a means for norm-governance which yields significant compliance with enforced security policy at a lower cost. Our ultimate contribution is to apply social simulation as a way to explore environmental properties and policies to evaluate key transitions in outcome, as a basis for guiding further and more demanding empirical research.