Computer Security - ESORICS 2014

Frontmatter

Public-Key Revocation and Tracing Schemes with Subset Difference Methods Revisited

Abstract

Broadcast encryption is a very powerful primitive since it can send an encrypted message to a set of users excluding a set of revoked users. Public-key broadcast encryption (PKBE) is a special type of broadcast encryption such that anyone can run the encryption algorithm to create an encrypted message by using a public key. In this paper, we propose a new technique to construct an efficient PKBE scheme by using the subset cover framework. First, we introduce a new concept of public-key encryption named single revocation encryption (SRE) and propose an efficient SRE scheme in the random oracle model. A user in SRE is represented as a group that he belongs and a member in the group. In SRE, a sender can create a ciphertext for a specified group where one member in the group is revoked, and a receiver can decrypt the ciphertext if he belongs to the group in the ciphertext and he is not revoked in the group. Second, we show that the subset difference (SD) scheme (or the layered subset difference (LSD) scheme) and an SRE scheme can be combined to construct a public-key revocation encryption (PKRE) scheme such that a set of revoked users is specified in a ciphertext. Our PKRE scheme using the LSD scheme and our SRE scheme can reduce the size of private keys and public keys by logN factor compared with the previous scheme of Dodis and Fazio.

Kwangsu Lee, Woo Kwon Koo, Dong Hoon Lee, Jong Hwan Park

NORX: Parallel and Scalable AEAD

Abstract

This paper introduces NORX, a novel authenticated encryption scheme supporting arbitrary parallelism degree and based on ARX primitives, yet not using modular additions. NORX has a unique parallel architecture based on the monkeyDuplex construction, with an original domain separation scheme for a simple processing of header, payload and trailer data. Furthermore, NORX specifies a dedicated datagram to facilitate interoperability and avoid users the trouble of defining custom encoding and signalling. NORX was optimized for efficiency in both software and hardware, with a SIMD-friendly core, almost byte-aligned rotations, no secret-dependent memory lookups, and only bitwise operations. On a Haswell processor, a serial version of NORX runs at 2.51 cycles per byte. Simulations of a hardware architecture for 180 nm UMC ASIC give a throughput of approximately 10Gbps at 125MHz.

Jean-Philippe Aumasson, Philipp Jovanovic, Samuel Neves

Even More Practical Secure Logging: Tree-Based Seekable Sequential Key Generators

Abstract

Sequential key generators produce a forward-secure sequence of symmetric cryptographic keys and are traditionally based on hash chains. An inherent disadvantage of such constructions is that they do not offer a fast-forward capability, i.e., lack a way to efficiently skip a large number of keys—a functionality often required in practice. This limitation was overcome only recently, with the introduction of seekable sequential key generators (SSKGs). The only currently known construction is based on the iterated evaluation of a shortcut one-way permutation, a factoring-based —and hence in practice not too efficient— building block. In this paper we revisit the challenge of marrying forward-secure key generation with seekability and show that symmetric primitives like PRGs, block ciphers, and hash functions suffice for obtaining secure SSKGs. Our scheme is not only considerably more efficient than the prior number-theoretic construction, but also extends the seeking functionality in a way that we believe is important in practice. Our construction is provably (forward-)secure in the standard model.

Giorgia Azzurra Marson, Bertram Poettering

Large Universe Ciphertext-Policy Attribute-Based Encryption with White-Box Traceability

Abstract

A Ciphertext-Policy Attribute-Based Encryption (CP-ABE) system extracts the decryption keys over attributes shared by multiple users. It brings plenty of advantages in ABE applications. CP-ABE enables fine-grained access control to the encrypted data for commercial applications. There has been significant progress in CP-ABE over the recent years because of two properties called traceability and large universe, greatly enriching the commercial applications of CP-ABE. Traceability is the ability of ABE to track the malicious users or traitors who intentionally leak the partial or modified decryption keys to others for profits. Nevertheless, due to the nature of CP-ABE, it is difficult to identify the original key owner from an exposed key since the decryption privilege is shared by multiple users who have the same attributes. On the other hand, the property of large universe in ABE proposed by Lewko and Waters enlarges the practical applications by supporting flexible number of attributes. Several systems have been proposed to obtain either of the above properties. However, none of them achieve the two properties simultaneously in practice, which limits the commercial applications of CP-ABE to a certain extent. In this paper, we propose a practical large universe CP-ABE system supporting white-box traceability, which is suitable for commercial applications. Compared to existing systems, our new system has three advantages: (1) The number of attributes is not polynomially bounded; (2) Malicious users who leak their decryption keys could be traced; and, (3) The storage overhead for traitor tracing is constant. We also prove the selective security of our new system in the standard model under “q-type” assumption.

Jianting Ning, Zhenfu Cao, Xiaolei Dong, Lifei Wei, Xiaodong Lin

PPDCP-ABE: Privacy-Preserving Decentralized Ciphertext-Policy Attribute-Based Encryption

Abstract

Cipher-policy attribute-based encryption (CP-ABE) is a more efficient and flexible encryption system as the encryptor can control the access structure when encrypting a message. In this paper, we propose a privacy-preserving decentralized CP-ABE (PPDCP-ABE) scheme where the central authority is not required, namely each authority can work independently without the cooperation to initialize the system. Meanwhile, a user can obtain secret keys from multiple authorities without releasing his global identifier (GID) and attributes to them. This is contrasted to the previous privacy-preserving multi-authority ABE (PPMA-ABE) schemes where a user can obtain secret keys from multiple authorities with them knowing his attributes and a central authority is required. However, some sensitive attributes can also release the user’s identity information. Hence, contemporary PPMA-ABE schemes cannot fully protect users’ privacy as multiple authorities can cooperate to identifier a user by collecting and analyzing his attributes. Therefore, it remains a challenging and important work to construct a PPMA-ABE scheme where the central authority is not required and both the identifiers and the attributes are considered.

Jinguang Han, Willy Susilo, Yi Mu, Jianying Zhou, Man Ho Au

Practical Direct Chosen Ciphertext Secure Key-Policy Attribute-Based Encryption with Public Ciphertext Test

Abstract

We propose a direct Key-Policy Attribute-Based Encryption (KP-ABE) scheme with semantic security against adaptively chosen ciphertext attacks (CCA2) in the standard model. Compared with its counterpart with security against chosen-plaintext attacks (CPA), the cost of our scheme is only a Chameleon hash. In contrast to the Boyen-Mei-Waters shrink approach from CPA-secure (l + 1)-Hierarchical Identity Based Encryption ((l + 1)-HIBE) to CCA2-secure l-HIBE, our approach only adds one on-the-fly dummy attribute. Further, our approach only requires that the underlying ABE is selectively secure and allows public ciphertext test. A major obstacle for the security proof in this scenario is that the simulator cannot prepare the challenge ciphertext associated with the on-the-fly dummy attribute due to the selective security constraint. We circumvent this obstacle with a Chameleon hash. Technically, unlike existing use of Chameleon hash in (online/offline) signature applications, our work shows Chameleon hash can also have unique applications in encryption schemes.

Weiran Liu, Jianwei Liu, Qianhong Wu, Bo Qin, Yunya Zhou

Privacy-Preserving Auditing for Attribute-Based Credentials

Abstract

Privacy-enhancing attribute-based credentials (PABCs) allow users to authenticate to verifiers in a data-minimizing way, in the sense that users are unlinkable between authentications and only disclose those attributes from their credentials that are relevant to the verifier. We propose a practical scheme to apply the same data minimization principle when the verifiers’ authentication logs are subjected to external audits. Namely, we propose an extended PABC scheme where the verifier can further remove attributes from presentation tokens before handing them to an auditor, while preserving the verifiability of the audit tokens. We present a generic construction based on a signature, a signature of knowledge and a trapdoor commitment scheme, prove it secure in the universal composability framework, and give an efficient instantiation based on the strong RSA assumption in the random-oracle model.

Jan Camenisch, Anja Lehmann, Gregory Neven, Alfredo Rial

What’s the Gist? Privacy-Preserving Aggregation of User Profiles

Abstract

Over the past few years, online service providers have started gathering increasing amounts of personal information to build user profiles and monetize them with advertisers and data brokers. Users have little control of what information is processed and are often left with an all-or-nothing decision between receiving free services or refusing to be profiled. This paper explores an alternative approach where users only disclose an aggregate model – the “gist” – of their data. We aim to preserve data utility and simultaneously provide user privacy. We show that this approach can be efficiently supported by letting users contribute encrypted and differentially-private data to an aggregator. The aggregator combines encrypted contributions and can only extract an aggregate model of the underlying data. We evaluate our framework on a dataset of 100,000 U.S. users obtained from the U.S. Census Bureau and show that (i) it provides accurate aggregates with as little as 100 users, (ii) it can generate revenue for both users and data brokers, and (iii) its overhead is appreciably low.

Igor Bilogrevic, Julien Freudiger, Emiliano De Cristofaro, Ersin Uzun

Challenging Differential Privacy:The Case of Non-interactive Mechanisms

Abstract

In this paper, we consider personalized recommendation systems in which before publication, the profile of a user is sanitized by a non-interactive mechanism compliant with the concept of differential privacy. We consider two existing schemes offering a differentially private representation of profiles: BLIP (BLoom-and-flIP) and JLT (Johnson-Lindenstrauss Transform). For assessing their security levels, we play the role of an adversary aiming at reconstructing a user profile. We compare two inference attacks, namely single and joint decoding. The first one decides of the presence of a single item in the profile, and sequentially explores all the item set. The latter strategy decides whether a subset of items is likely to be the user profile, and considers all the possible subsets. Our contributions are a theoretical analysis as well as a practical implementation of both attacks, which were evaluated on datasets of real user profiles. The results obtained clearly demonstrates that joint decoding is the most powerful attack, while also giving useful insights on how to set the differential privacy parameter ε.

Raghavendran Balu, Teddy Furon, Sébastien Gambs

Optimality and Complexity of Inference-Proof Data Filtering and CQE

Abstract

The ample literature on confidentiality-preserving data publishing – and controlled query evaluation (CQE) in particular – leaves several questions open. Are the greedy data-filtering algorithms adopted in the literature maximally cooperative? Can novel secure view formats or answer distortion methods improve security or cooperativeness? What is the inherent complexity of confidentiality-preserving data publishing under different constraints, such as cooperativeness and availability? Can the theoretical results on CQE be systematically extended to more general settings? In this paper we answer the above questions using a completely generic, abstract data filtering framework, independent from any syntactic details and data source encodings, and compatible with all possible distortion methods. Some of the main results are: Refusal-based filterings can be adopted as a normal form for all kinds of filterings; greedy refusal-based filterings are optimal; cooperativeness checks and some availability checks are coNP-hard in the simplest case.

Joachim Biskup, Piero A. Bonatti, Clemente Galdi, Luigi Sauro

New Insight to Preserve Online Survey Accuracy and Privacy in Big Data Era

Abstract

An online survey system provides a convenient way for people to conduct surveys. It removes the necessity of human resources to hold paper surveys or telephone interviews and hence reduces the cost significantly. Nevertheless, accuracy and privacy remain as the major obstacles that need additional attention. To conduct an accurate survey, privacy maybe lost, and vice versa. In this paper, we provide new insight to preserve these two seeming contradictory issues in online survey systems especially suitable in big data era. We propose a secure system, which is shown to be efficient and practical by simulation data. Our analysis further shows that the proposed solution is desirable not only in online survey systems but also in several potential applications, including E-Voting, Smart-Grid and Vehicular Ad Hoc Networks.

Joseph K. Liu, Man Ho Au, Xinyi Huang, Willy Susilo, Jianying Zhou, Yong Yu

Software Countermeasures for Control Flow Integrity of Smart Card C Codes

Abstract

Fault attacks can target smart card programs in order to disrupt an execution and gain an advantage over the data or the embedded functionalities. Among all possible attacks, control flow attacks aim at disrupting the normal execution flow. Identifying harmful control flow attacks as well as designing countermeasures at software level are tedious and tricky for developers. In this paper, we propose a methodology to detect harmful intra-procedural jump attacks at source code level and to automatically inject formally-proven countermeasures. The proposed software countermeasures defeat 100% of attacks that jump over at least two C source code statements or beyond. Experiments show that the resulting code is also hardened against unexpected function calls and jump attacks at assembly level.

Jean-François Lalande, Karine Heydemann, Pascal Berthomé

LeakWatch: Estimating Information Leakage from Java Programs

Abstract

Programs that process secret data may inadvertently reveal information about those secrets in their publicly-observable output. This paper presents LeakWatch, a quantitative information leakage analysis tool for the Java programming language; it is based on a flexible “point-to-point” information leakage model, where secret and publicly-observable data may occur at any time during a program’s execution. LeakWatch repeatedly executes a Java program containing both secret and publicly-observable data and uses robust statistical techniques to provide estimates, with confidence intervals, for min-entropy leakage (using a new theoretical result presented in this paper) and mutual information.We demonstrate how LeakWatch can be used to estimate the size of information leaks in a range of real-world Java programs.

Tom Chothia, Yusuke Kawamoto, Chris Novakovic

SigPath: A Memory Graph Based Approach for Program Data Introspection and Modification

Abstract

Examining and modifying data of interest in the memory of a target program is an important capability for security applications such as memory forensics, rootkit detection, game hacking, and virtual machine introspection. In this paper we present a novel memory graph based approach for program data introspection and modification, which does not require source code, debugging symbols, or any API in the target program. It takes as input a sequence of memory snapshots taken while the program executes, and produces a path signature, which can be used in different executions of the program to efficiently locate and traverse the in-memory data structures where the data of interest is stored. We have implemented our approach in a tool called SigPath. We have applied SigPath to game hacking, building cheats for 10 popular real-time and turn-based games, and for memory forensics, recovering from snapshots the contacts a user has stored in four IM applications including Skype and Yahoo Messenger.

David Urbina, Yufei Gu, Juan Caballero, Zhiqiang Lin

ID-Based Two-Server Password-Authenticated Key Exchange

Abstract

In two-server password-authenticated key exchange (PAKE) protocol, a client splits its password and stores two shares of its password in the two servers, respectively, and the two servers then cooperate to authenticate the client without knowing the password of the client. In case one server is compromised by an adversary, the password of the client is required to remain secure. In this paper, we present a compiler that transforms any two-party PAKE protocol to a two-server PAKE protocol. This compiler is mainly built on two-party PAKE and identity-based encryption (IBE), where the identities of the two servers are used as their public keys. By our compiler, we can construct a two-server PAKE protocol which achieves implicit authentication with only two communications between the client and the servers. As long as the underlying two-party PAKE protocol and IBE scheme have provable security without random oracles, the two-server PAKE protocol constructed by our compiler can be proven to be secure without random oracles.

Xun Yi, Feng Hao, Elisa Bertino

Modelling Time for Authenticated Key Exchange Protocols

Abstract

The notion of time plays an important role in many practically deployed cryptographic protocols, ranging from One-Time-Password (OTP) tokens to the Kerberos protocol. However, time is difficult to model in a Turing machine environment.

We propose the first such model, where time is modelled as a global counter \(\cal T\). We argue that this model closely matches several implementations of time in computer environments. The usefulness of the model is shown by giving complexity-theoretic security proofs for OTP protocols and HMQV-like one-round AKE protocols.

Jörg Schwenk

Zero-Knowledge Password Policy Checks and Verifier-Based PAKE

Abstract

Zero-Knowledge Password Policy Checks (ZKPPC), introduced in this work, enable blind registration of client passwords at remote servers, i.e., client passwords are never transmitted to the servers. This eliminates the need for trusting servers to securely process and store client passwords. A ZKPPC protocol, executed as part of the registration procedure, allows clients to further prove compliance of chosen passwords with respect to password policies defined by the servers.

The main benefit of ZKPPC-based password registration is that it guarantees that registered passwords never appear in clear on the server side. At the end of the registration phase the server only receives and stores some verification information that can later be used for authentication in a suitable Verifier-based Password Authenticated Key Exchange (VPAKE) protocol.

We give general and concrete constructions of ZKPPC protocols and suitable VPAKE protocols for ASCII-based passwords and policies that are commonly used on the web. To this end we introduce a reversible mapping of ASCII characters to integers that can be used to preserve the structure of the password string and a new randomized password hashing scheme for ASCII-based passwords.

Franziskus Kiefer, Mark Manulis

Bitcoin Transaction Malleability and MtGox

Abstract

In Bitcoin, transaction malleability describes the fact that the signatures that prove the ownership of bitcoins being transferred in a transaction do not provide any integrity guarantee for the signatures themselves. This allows an attacker to mount a malleability attack in which it intercepts, modifies, and rebroadcasts a transaction, causing the transaction issuer to believe that the original transaction was not confirmed. In February 2014 MtGox, once the largest Bitcoin exchange, closed and filed for bankruptcy claiming that attackers used malleability attacks to drain its accounts. In this work we use traces of the Bitcoin network for over a year preceding the filing to show that, while the problem is real, there was no widespread use of malleability attacks before the closure of MtGox.

Christian Decker, Roger Wattenhofer

Election Verifiability for Helios under Weaker Trust Assumptions

Abstract

Most electronic voting schemes aim at providing verifiability: voters should trust the result without having to rely on some authorities. Actually, even a prominent voting system like Helios cannot fully achieve verifiability since a dishonest bulletin board may add ballots. This problem is called ballot stuffing.

In this paper we give a definition of verifiability in the computational model to account for a malicious bulletin board that may add ballots. Next, we provide a generic construction that transforms a voting scheme that is verifiable against an honest bulletin board and an honest registration authority (weak verifiability) into a verifiable voting scheme under the weaker trust assumption that the registration authority and the bulletin board are not simultaneously dishonest (strong verifiability). This construction simply adds a registration authority that sends private credentials to the voters, and publishes the corresponding public credentials.

We further provide simple and natural criteria that imply weak verifiability. As an application of these criteria, we formally prove the latest variant of Helios by Bernhard, Pereira and Warinschi weakly verifiable. By applying our generic construction we obtain a Helios-like scheme that has ballot privacy and strong verifiability (and thus prevents ballot stuffing). The resulting voting scheme, Helios-C, retains the simplicity of Helios and has been implemented and tested.

Véronique Cortier, David Galindo, Stéphane Glondu, Malika Izabachène

CoinShuffle: Practical Decentralized Coin Mixing for Bitcoin

Abstract

The decentralized currency network Bitcoin is emerging as a potential new way of performing financial transactions across the globe. Its use of pseudonyms towards protecting users’ privacy has been an attractive feature to many of its adopters. Nevertheless, due to the inherent public nature of the Bitcoin transaction ledger, users’ privacy is severely restricted to linkable anonymity, and a few transaction deanonymization attacks have been reported thus far.

In this paper we propose CoinShuffle, a completely decentralized Bitcoin mixing protocol that allows users to utilize Bitcoin in a truly anonymous manner. CoinShuffle is inspired by the accountable anonymous group communication protocol Dissent and enjoys several advantages over its predecessor Bitcoin mixing protocols. It does not require any (trusted, accountable or untrusted) third party and it is perfectly compatible with the current Bitcoin system. CoinShuffle introduces only a small communication overhead for its users, while completely avoiding additional anonymization fees and minimalizing the computation and communication overhead for the rest of the Bitcoin system.

Tim Ruffing, Pedro Moreno-Sanchez, Aniket Kate

LESS Is More: Host-Agent Based Simulator for Large-Scale Evaluation of Security Systems

Abstract

Recently proposed network security systems have demonstrated the benefits of scale for achieving many security goals, including the detection of worm outbreaks, botnets, and denial of service attacks. However, scale is also a barrier to further advancement of such systems: obtaining and working with appropriately large data sets is difficult, and existing simulation techniques are ill suited for this domain. To overcome these challenges, we propose a host behavior simulator, LESS, designed for evaluating large scale network security systems. LESS build and automatically configures the behaviors of host agents using background traffic samples and malicious traffic models. In turn, host agents communicate with each other throughout a simulation, generating traffic records. We demonstrate the applicability and benefits of LESS by tuning it with publicly available traces, and then using generated records to reproduce results from several recently proposed systems. We also used LESS to extend the evaluations of these systems, highlighting dimensions of large scale security system performance that would be difficult to study without simulation.

John Sonchack, Adam J. Aviv

Detecting Insider Information Theft Using Features from File Access Logs

Abstract

Access control is a necessary, but often insufficient, mechanism for protecting sensitive resources. In some scenarios, the cost of anticipating information needs and specifying precise access control policies is prohibitive. For this reason, many organizations provide employees with excessive access to some resources, such as file or source code repositories. This allows the organization to maximize the benefit employees get from access to troves of information, but exposes the organization to excessive risk. In this work we investigate how to build profiles of normal user activity on file repositories for uses in anomaly detection, insider threats, and risk mitigation. We illustrate how information derived from other users’ activity and the structure of the filesystem hierarchy can be used to detect abnormal access patterns. We evaluate our methods on real access logs from a commercial source code repository on tasks of user identification and users seeking to leak resources by accessing more than they have a need for.

Christopher Gates, Ninghui Li, Zenglin Xu, Suresh N. Chari, Ian Molloy, Youngja Park

SRID: State Relation Based Intrusion Detection for False Data Injection Attacks in SCADA

Abstract

Advanced false data injection attack in targeted malware intrusion is becoming an emerging severe threat to the Supervisory Control And Data Acquisition (SCADA) system. Several intrusion detection schemes have been proposed previously [1, 2]. However, designing an effective real-time detection system for a resource-constraint device is still an open problem for the research community. In this paper, we propose a new relation-graph-based detection scheme to defeat false data injection attacks at the SCADA system, even when injected data may seemly fall within a valid/normal range. To balance effectiveness and efficiency, we design a novel detection model, alternation vectors with state relation graph. Furthermore, we propose a new inference algorithm to infer the injection point(s), i.e., the attack origin, in the system. We evaluate SRID with a real-world power plant simulator. The experiment results show that SRID can detect various false data injection attacks with a low false positive rate at 0.0125%. Meanwhile, SRID can dramatically reduce the search space of attack origins and accurately locate most of attack origins.

Yong Wang, Zhaoyan Xu, Jialong Zhang, Lei Xu, Haopei Wang, Guofei Gu

Click Fraud Detection on the Advertiser Side

Abstract

Click fraud—malicious clicks at the expense of pay-per-click advertisers—is posing a serious threat to the Internet economy. Although click fraud has attracted much attention from the security community, as the direct victims of click fraud, advertisers still lack effective defense to detect click fraud independently. In this paper, we propose a novel approach for advertisers to detect click frauds and evaluate the return on investment (ROI) of their ad campaigns without the helps from ad networks or publishers. Our key idea is to proactively test if visiting clients are full-fledged modern browsers and passively scrutinize user engagement. In particular, we introduce a new functionality test and develop an extensive characterization of user engagement. Our detection can significantly raise the bar for committing click fraud and is transparent to users. Moreover, our approach requires little effort to be deployed at the advertiser side. To validate the effectiveness of our approach, we implement a prototype and deploy it on a large production website; and then we run 10-day ad campaigns for the website on a major ad network. The experimental results show that our proposed defense is effective in identifying both clickbots and human clickers, while incurring negligible overhead at both the server and client sides.

Haitao Xu, Daiping Liu, Aaron Koehl, Haining Wang, Angelos Stavrou

Botyacc: Unified P2P Botnet Detection Using Behavioural Analysis and Graph Analysis

Abstract

The detection and isolation of peer-to-peer botnets is an ongoing problem. We propose a novel technique for detecting P2P botnets. Detection is based on unifying behavioural analysis with structured graph analysis. First, our inference technique exploits a fundamental property of botnet design. Modern botnets use peer-to-peer communication topologies which are fundamental to botnet resilience. Second, our technique extends conventional graph-based detection by incorporating behavioural analysis into structured graph analysis, thus unifying graph-theoretic detection with behavioural detection under a single algorithmic framework. We carried out evaluation over real-world P2P botnet traffic and show that the resulting algorithm can localise the majority of bots with low false-positive rate.

Shishir Nagaraja

Feature-Distributed Malware Attack: Risk and Defence

Abstract

Modern computing platforms have progressed to more secure environments with various defensive techniques such as application-based permission and application whitelisting. In addition, anti-virus solutions are improving their detection techniques, especially based on behavioural properties. To overcome these hurdles, the adversary has been developing malware techniques including the use of legitimate digital certificates; hence it is important to explore possible offensive techniques in a security-improved environment.

In this paper, first we propose the new technique of feature-distributed malware that dynamically distributes its features to multiple software components in order to bypass various security mechanisms such as application whitelisting and anti-virus’ behavioural detection. To evaluate our approach, we have implemented a tool that automatically generates such malware instances, and have performed a series of experiments showing the risks of such advanced malware. We also suggest an effective defence mechanism. It prevents loading of malicious components by utilising digital certificates of software components. We have implemented a Windows service that provides our defence mechanism, and evaluated it against the proposed malware. Another useful characteristic of our defence is that it is capable of blocking general abuse of legitimate digital certificates with dynamic software component loading.

Byungho Min, Vijay Varadharajan

RootkitDet: Practical End-to-End Defense against Kernel Rootkits in a Cloud Environment

Abstract

In cloud environments, kernel-level rootkits still pose serious security threats to guest OSes. Existing defenses against kernel-level rootkit have limitations when applied to cloud environments. In this paper, we propose RootkitDet, an end-to-end defense system capable of detecting and diagnosing rootkits in guest OSes with the intent to recover the system modifications caused by the rootkits in cloud environments. RootkitDet detects rootkits by identifying suspicious code region in the kernel space of guest OSes through the underneath hypervisor, performs diagnosis on the code of the detected rootkit to categorize it and identify modifications, and reverses the modifications if possible to eliminate the effect of rootkits. Our evaluation results show that the RootkitDet is effective on detection of kernel-level rootkits and recovery modifications with less than 1% performance overhead to the guest OSes and the computation and network overhead is linear with the quantity of the VM instances being monitored.

Lingchen Zhang, Sachin Shetty, Peng Liu, Jiwu Jing

Modeling Network Diversity for Evaluating the Robustness of Networks against Zero-Day Attacks

Abstract

The interest in diversity as a security mechanism has recently been revived in various applications, such as Moving Target Defense (MTD), resisting worms in sensor networks, and improving the robustness of network routing. However, most existing efforts on formally modeling diversity have focused on a single system running diverse software replicas or variants. At a higher abstraction level, as a global property of the entire network, diversity and its impact on security have received limited attention. In this paper, we take the first step towards formally modeling network diversity as a security metric for evaluating the robustness of networks against potential zero day attacks. Specifically, we first devise a biodiversity-inspired metric based on the effective number of distinct resources. We then propose two complementary diversity metrics, based on the least and the average attacking efforts, respectively. Finally, we evaluate our algorithm and metrics through simulation.

Lingyu Wang, Mengyuan Zhang, Sushil Jajodia, Anoop Singhal, Massimiliano Albanese

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