Biblio

As wireless networks become more pervasive, the amount of the wireless data is rapidly increasing. One of the biggest challenges of wide adoption of distributed data storage is how to store these data securely. In this work, we study the frequency-based attack, a type of attack that is different from previously well-studied ones, that exploits additional adversary knowledge of domain values and/or their exact/approximate frequencies to crack the encrypted data. To cope with frequency-based attacks, the straightforward 1-to-1 substitution encryption functions are not sufficient. We propose a data encryption strategy based on 1-to-n substitution via dividing and emulating techniques to defend against the frequency-based attack, while enabling efficient query evaluation over encrypted data. We further develop two frameworks, incremental collection and clustered collection, which are used to defend against the global frequency-based attack when the knowledge of the global frequency in the network is not available. Built upon our basic encryption schemes, we derive two mechanisms, direct emulating and dual encryption, to handle updates on the data storage for energy-constrained sensor nodes and wireless devices. Our preliminary experiments with sensor nodes and extensive simulation results show that our data encryption strategy can achieve high security guarantee with low overhead.

As smart meters continue to be deployed around the world collecting unprecedented levels of fine-grained data about consumers, we need to find mechanisms that are fair to both, (1) the electric utility who needs the data to improve their operations, and (2) the consumer who has a valuation of privacy but at the same time benefits from sharing consumption data. In this paper we address this problem by proposing privacy contracts between electric utilities and consumers with the goal of maximizing the social welfare of both. Our mathematical model designs an optimization problem between a population of users that have different valuations on privacy and the costs of operation by the utility. We then show how contracts can change depending on the probability of a privacy breach. This line of research can help inform not only current but also future smart meter collection practices.

Typing is a human activity that can be affected by a number of situational and task-specific factors. Changes in typing behavior resulting from the manipulation of such factors can be predictably observed through key-level input analytics. Here we present a study designed to explore these relationships. Participants play a typing game in which letter composition, word length and number of words appearing together are varied across levels. Inter-keystroke timings and other higher order statistics (such as bursts and pauses), as well as typing strategies, are analyzed from game logs to find the best set of metrics that quantify the effect that different experimental factors have on observable metrics. Beyond task-specific factors, we also study the effects of habituation by recording changes in performance with practice. Currently a work in progress, this research aims at developing a predictive model of human typing. We believe this insight can lead to the development of novel security proofs for interactive systems that can be deployed on existing infrastructure with minimal overhead. Possible applications of such predictive capabilities include anomalous behavior detection, authentication using typing signatures, bot detection using word challenges etc.

The success of machine learning, particularly in supervised settings, has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to evade the classifiers deployed to detect them. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. In particular, we demonstrate severe shortcomings of feature reduction in adversarial settings using several natural adversarial objective functions, an observation that is particularly pronounced when the adversary is able to substitute across similar features (for example, replace words with synonyms or replace letters in words). We offer a simple heuristic method for making learning more robust to feature cross-substitution attacks. We then present a more general approach based on mixed-integer linear programming with constraint generation, which implicitly trades off overfitting and feature selection in an adversarial setting using a sparse regularizer along with an evasion model. Our approach is the first method for combining an adversarial classification algorithm with a very general class of models of adversarial classifier evasion. We show that our algorithmic approach significantly outperforms state-of-the-art alternatives.

Intuitively, two protocols P₁ and P₂ are indistinguishable if an attacker cannot tell the difference between interactions with P₁ and with P₂ . In this paper we: (i) propose an intuitive notion of indistinguishability in Maude-NPA; (ii) formalize such a notion in terms of state unreachability conditions on their synchronous product; (iii) prove theorems showing how —assuming the protocol’s algebraic theory has a finite variant (FV) decomposition – these conditions can be checked by the Maude-NPA tool; and (iv) illustrate our approach with concrete examples. This provides for the first time a framework for automatic analysis of indistinguishability modulo as wide a class of algebraic properties as FV, which includes many associative-commutative theories of interest to cryptographic protocol analysis.

Hypervisor activity is designed to be hidden from guest Virtual Machines (VM) as well as external observers. In this paper, we demonstrate that this does not always occur. We present a method by which an external observer can learn sensitive information about hypervisor internals, such as VM scheduling or hypervisor-level monitoring schemes, by observing a VM. We refer to this capability as Hypervisor Introspection (HI).

HI can be viewed as the inverse process of the well-known Virtual Machine Introspection (VMI) technique. VMI is a technique to extract VMs’ internal state from the hypervi- sor, facilitating the implementation of reliability and security monitors[1]. Conversely, HI is a technique that allows VMs to autonomously extract hypervisor information. This capability enables a wide range of attacks, for example, learning a hypervisor’s properties (version, configuration, etc.), defeating hypervisor-level monitoring systems, and compromising the confidentiality of co-resident VMs. This paper focuses on the discovery of a channel to implement HI, and then leveraging that channel for a novel attack against traditional VMI.

In order to perform HI, there must be a method of extracting information from the hypervisor. Since this information is intentionally hidden from a VM, we make use of a side channel. When the hypervisor checks a VM using VMI, VM execution (e.g. network communication between a VM and a remote system) must pause. Therefore, information regarding the hypervisor’s activity can be leaked through this suspension of execution. We call this side channel the VM suspend side channel, illustrated in Fig. 1. As a proof of concept, this paper presents how correlating the results of in-VM micro- benchmarking and out-of-VM reference monitoring can be used to determine when hypervisor-level monitoring tools are vulnerable to attacks.

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

After the occurrence of numerous worldwide financial scandals, the importance of related issues such as internal control and information security has greatly increased. This study develops an internal control framework that can be applied within an enterprise resource planning (ERP) system. A literature review is first conducted to examine the necessary forms of internal control in information technology (IT) systems. The control criteria for the establishment of the internal control framework are then constructed. A case study is conducted to verify the feasibility of the established framework. This study proposes a 12-dimensional framework with 37 control items aimed at helping auditors perform effective audits by inspecting essential internal control points in ERP systems. The proposed framework allows companies to enhance IT audit efficiency and mitigates control risk. Moreover, companies that refer to this framework and consider the limitations of their own IT management can establish a more robust IT management mechanism.

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.

Online cyber threat descriptions are rich, but little research has attempted to systematically analyze these descriptions. In this paper, we process and analyze two of Symantec’s online threat description corpora. The Anti-Virus (AV) corpus contains descriptions of more than 12,400 threats detected by Symantec’s AV, and the Intrusion Prevention System (IPS) corpus contains descriptions of more than 2,700 attacks detected by Symantec’s IPS. In our analysis, we quantify the over time evolution of threat severity and type in the corpora. We also assess the amount of time Symantec takes to release signatures for newly discovered threats. Our analysis indicates that a very small minority of threats in the AV corpus are high-severity, whereas the majority of attacks in the IPS corpus are high-severity. Moreover, we find that the prevalence of different threat types such as worms and viruses in the corpora varies considerably over time. Finally, we find that Symantec prioritizes releasing signatures for fast propagating threats.

This paper presents a framework for multiagent systems trust modeling that reasons about both user credibility and user similarity. Through simulation, we are able to show that our approach works well in social networking environments by presenting messages to users with high predicted benefit.

In this paper, we define a new homomorphic signature for identity management in mobile cloud computing. A mobile user firstly computes a full signature on all his sensitive personal information (SPI), and stores it in a trusted third party (TTP). During the valid period of his full signature, if the user wants to call a cloud service, he should authenticate him to the cloud service provider (CSP) through TTP. In our scheme, the mobile user only needs to send a  vector to the access controlling server (TTP). The access controlling server who doesnʼt know the secret key can compute a partial signature on a small part of userʼs SPI, and then sends it to the CSP. We give a formal secure definition of this homomorphic signature, and construct a scheme from GHR signature. We prove that our scheme is secure under GHR signature.

This paper presents a model for generating personalized passwords (i.e., passwords based on user and service profile). A user's password is generated from a list of personalized words, each word is drawn from a topic relating to a user and the service in use. The proposed model can be applied to: (i) assess the strength of a password (i.e., determine how many guesses are used to crack the password), and (ii) generate secure (i.e., contains digits, special characters, or capitalized characters) yet easy to memorize passwords.

This paper presents a system named SPOT to achieve high accuracy and preemptive detection of attacks. We use security logs of real-incidents that occurred over a six-year period at National Center for Supercomputing Applications (NCSA) to evaluate SPOT. Our data consists of attacks that led directly to the target system being compromised, i.e., not detected in advance, either by the security analysts or by intrusion detection systems. Our approach can detect 75 percent of attacks as early as minutes to tens of hours before attack payloads are executed.

This paper presents a solution that simultaneously addresses both reliability and security (RnS) in a monitoring framework. We identify the commonalities between reliability and security to guide the design of HyperTap, a hypervisor-level framework that efficiently supports both types of monitoring in virtualization environments. In HyperTap, the logging of system events and states is common across monitors and constitutes the core of the framework. The audit phase of each monitor is implemented and operated independently. In addition, HyperTap relies on hardware invariants to provide a strongly isolated root of trust. HyperTap uses active monitoring, which can be adapted to enforce a wide spectrum of RnS policies. We validate Hy- perTap by introducing three example monitors: Guest OS Hang Detection (GOSHD), Hidden RootKit Detection (HRKD), and Privilege Escalation Detection (PED). Our experiments with fault injection and real rootkits/exploits demonstrate that HyperTap provides robust monitoring with low performance overhead.

The Maude-NRL Protocol Analyzer (Maude-NPA) is a tool for reasoning about the security of cryptographic protocols in which the cryptosystems satisfy different equational properties. It tries to find secrecy or authentication attacks by searching backwards from an insecure attack state pattern that may contain logical variables, in such a way that logical variables become properly instantiated in order to find an initial state. The execution mechanism for this logical reachability is narrowing modulo an equational theory. Although Maude-NPA also possesses a forwards semantics naturally derivable from the backwards semantics, it is not suitable for state space exploration or protocol simulation.

In this paper we define an executable forwards semantics for Maude-NPA, instead of its usual backwards one, and restrict it to the case of concrete states, that is, to terms without logical variables. This case corresponds to standard rewriting modulo an equational theory. We prove soundness and completeness of the backwards narrowing-based semantics with respect to the rewriting-based forwards semantics. We show its effectiveness as an analysis method that complements the backwards analysis with new prototyping, simulation, and explicit-state model checking features by providing some experimental results.

Programming languages often include specialized syntax for common
datatypes (e.g. lists) and some also build in support for specific specialized
datatypes (e.g. regular expressions), but user-defined types must use generalpurpose
syntax. Frustration with this causes developers to use strings, rather than
structured data, with alarming frequency, leading to correctness, performance,
security, and usability issues. Allowing library providers to modularly extend a
language with new syntax could help address these issues. Unfortunately, prior
mechanisms either limit expressiveness or are not safely composable: individually
unambiguous extensions can still cause ambiguities when used together.
We introduce type-specific languages (TSLs): logic associated with a type that
determines how the bodies of generic literals, able to contain arbitrary syntax,
are parsed and elaborated, hygienically. The TSL for a type is invoked only
when a literal appears where a term of that type is expected, guaranteeing noninterference.
We give evidence supporting the applicability of this approach and
formally specify it with a bidirectionally typed elaboration semantics for the
Wyvern programming language.