Biblio
Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is highly periodic. However, it is sometimes multiplexed, due to multi-threaded scheduling. In previous work we introduced a Statechart model which includes multiple Deterministic Finite Automata (DFA), one per cyclic pattern. We demonstrated that Statechart-based anomaly detection is highly effective on multiplexed cyclic traffic when the individual cyclic patterns are known. The challenge is to construct the Statechart, by unsupervised learning, from a captured trace of the multiplexed traffic, especially when the same symbols (ICS messages) can appear in multiple cycles, or multiple times in a cycle. Previously we suggested a combinatorial approach for the Statechart construction, based on Euler cycles in the Discrete Time Markov Chain (DTMC) graph of the trace. This combinatorial approach worked well in simple scenarios, but produced a false-alarm rate that was excessive on more complex multiplexed traffic. In this paper we suggest a new Statechart construction method, based on spectral analysis. We use the Fourier transform to identify the dominant periods in the trace. Our algorithm then associates a set of symbols with each dominant period, identifies the order of the symbols within each period, and creates the cyclic DFAs and the Statechart. We evaluated our solution on long traces from two production ICS: one using the Siemens S7-0x72 protocol and the other using Modbus. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulate multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The resulting Statecharts model the traces with an overall median false-alarm rate as low as 0.16% on the synthetic datasets, and with zero false-alarms on production S7-0x72 traffic. Moreover, the spectral analysis Statecharts consistently out-performed the previous combinatorial Statecharts, exhibiting significantly lower false alarm rates and more compact model sizes.
We tackle the problem of automated exploit generation for web applications. In this regard, we present an approach that significantly improves the state-of-art in web injection vulnerability identification and exploit generation. Our approach for exploit generation tackles various challenges associated with typical web application characteristics: their multi-module nature, interposed user input, and multi-tier architectures using a database backend. Our approach develops precise models of application workflows, database schemas, and native functions to achieve high quality exploit generation. We implemented our approach in a tool called Chainsaw. Chainsaw was used to analyze 9 open source applications and generated over 199 first- and second-order injection exploits combined, significantly outperforming several related approaches.
Content Security Policy (CSP) is an emerging W3C standard introduced to mitigate the impact of content injection vulnerabilities on websites. We perform a systematic, large-scale analysis of four key aspects that impact on the effectiveness of CSP: browser support, website adoption, correct configuration and constant maintenance. While browser support is largely satisfactory, with the exception of few notable issues, our analysis unveils several shortcomings relative to the other three aspects. CSP appears to have a rather limited deployment as yet and, more crucially, existing policies exhibit a number of weaknesses and misconfiguration errors. Moreover, content security policies are not regularly updated to ban insecure practices and remove unintended security violations. We argue that many of these problems can be fixed by better exploiting the monitoring facilities of CSP, while other issues deserve additional research, being more rooted into the CSP design.
Recent technology shifts such as cloud computing, the Internet of Things, and big data lead to a significant transfer of sensitive data out of trusted edge networks. To counter resulting privacy concerns, we must ensure that this sensitive data is not inadvertently forwarded to third-parties, used for unintended purposes, or handled and stored in violation of legal requirements. Related work proposes to solve this challenge by annotating data with privacy policies before data leaves the control sphere of its owner. However, we find that existing privacy policy languages are either not flexible enough or require excessive processing, storage, or bandwidth resources which prevents their widespread deployment. To fill this gap, we propose CPPL, a Compact Privacy Policy Language which compresses privacy policies by taking advantage of flexibly specifiable domain knowledge. Our evaluation shows that CPPL reduces policy sizes by two orders of magnitude compared to related work and can check several thousand of policies per second. This allows for individual per-data item policies in the context of cloud computing, the Internet of Things, and big data.
Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between ε-differential privacy and (ε,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.
Browser fingerprinting is a widely used technique to uniquely identify web users and to track their online behavior. Until now, different tools have been proposed to protect the user against browser fingerprinting. However, these tools have usability restrictions as they deactivate browser features and plug-ins (like Flash) or the HTML5 canvas element. In addition, all of them only provide limited protection, as they randomize browser settings with unrealistic parameters or have methodical flaws, making them detectable for trackers. In this work we demonstrate the first anti-fingerprinting strategy, which protects against Flash fingerprinting without deactivating it, provides robust and undetectable anti-canvas fingerprinting, and uses a large set of real word data to hide the actual system and browser properties without losing usability. We discuss the methods and weaknesses of existing anti-fingerprinting tools in detail and compare them to our enhanced strategies. Our evaluation against real world fingerprinting tools shows a successful fingerprinting protection in over 99% of 70.000 browser sessions.
Recently, PIN-TRUST, a method to predict future trust relationships between users is proposed. PIN-TRUST out-performs existing trust prediction methods by exploiting all types of interactions between users and the reciprocation of ones. In this paper, we validate whether its consideration on the reciprocation of interactions is really effective in trust prediction. Furthermore, we consider a new concept, the "uncertainty" of untrustworthy users that is devised to reflect the difficulty on modeling the activities of untrustworthy users in PIN-TRUST. Then, we also validate the effectiveness this uncertainty concepts. Through the validation, we reveal that the consideration of the reciprocation of interactions is effective for trust prediction with PIN-TRUST, and it is necessary to regard the uncertainty of untrustworthy users same as that of other users.
The software-as-a-service (SaaS) market is growing very fast, but still many clients are concerned about the confidentiality of their data in the cloud. Motivated hackers or malicious insiders could try to steal the clients' data. Encryption is a potential solution, but supporting the necessary functionality also in existing applications is difficult. In this paper, we examine encrypting analytical web applications that perform extensive number processing operations in the database. Existing solutions for encrypting data in web applications poorly support such encryption. We employ a proxy that adjusts the encryption to the level necessary for the client's usage and also supports additively homomorphic encryption. This proxy is deployed at the client and all encryption keys are stored and managed there, while the application is running in the cloud. Our proxy is stateless and we only need to modify the database driver of the application. We evaluate an instantiation of our architecture on an exemplary application. We only slightly increase page load time on average from 3.1 seconds to 4.7. However, roughly 40% of all data columns remain probabilistic encrypted. The client can set the desired security level for each column using our policy mechanism. Hence our proxy architecture offers a solution to increase the confidentiality of the data at the cloud provider at a moderate performance penalty.
In this research paper, we present a function-based methodology to evaluate the resilience of gas pipeline systems under two different cyber-physical attack scenarios. The first attack scenario is the pressure integrity attack on the natural gas high-pressure transmission pipeline. Through simulations, we have analyzed the cyber attacks that propagate from cyber to the gas pipeline physical domain, the time before which the SCADA system should respond to such attacks, and finally, an attack which prevents the response of the system. We have used the combined results of simulations of a wireless mesh network for remote terminal units and of a gas pipeline simulation to measure the shortest Time to Criticality (TTC) parameter; the time for an event to reach the failure state. The second attack scenario describes how a failure of a cyber node controlling power grid functionality propagates from cyber to power to gas pipeline systems. We formulate this problem using a graph-theoretic approach and quantify the resilience of the networks by percentage of connected nodes and the length of the shortest path between them. The results show that parameters such as TTC, power distribution capacity of the power grid nodes and percentage of the type of cyber nodes compromised, regulate the efficiency and resilience of the power and gas networks. The analysis of such attack scenarios helps the gas pipeline system administrators design attack remediation algorithms and improve the response of the system to an attack.
In this paper we describe a system that allows the real time creation of firewall rules in response to geographic and political changes in the control-plane. This allows an organization to mitigate data exfiltration threats by analyzing Border Gateway Protocol (BGP) updates and blocking packets from being routed through problematic jurisdictions. By inspecting the autonomous system paths and referencing external data sources about the autonomous systems, a BGP participant can infer the countries that traffic to a particular destination address will traverse. Based on this information, an organization can then define constraints on its egress traffic to prevent sensitive data from being sent via an untrusted region. In light of the many route leaks and BGP hijacks that occur today, this offers a new option to organizations willing to accept reduced availability over the risk to confidentiality. Similar to firewalls that allow organizations to block traffic originating from specific countries, our approach allows blocking outbound traffic from transiting specific jurisdictions. To illustrate the efficacy of this approach, we provide an analysis of paths to various financial services IP addresses over the course of a month from a single BGP vantage point that quantifies the frequency of path alterations resulting in the traversal of new countries. We conclude with an argument for the utility of country-based egress policies that do not require the cooperation of upstream providers.
Function Secret Sharing (FSS), introduced by Boyle et al. (Eurocrypt 2015), provides a way for additively secret-sharing a function from a given function family F. More concretely, an m-party FSS scheme splits a function f : \0, 1\n -textgreater G, for some abelian group G, into functions f1,...,fm, described by keys k1,...,km, such that f = f1 + ... + fm and every strict subset of the keys hides f. A Distributed Point Function (DPF) is a special case where F is the family of point functions, namely functions f\_\a,b\ that evaluate to b on the input a and to 0 on all other inputs. FSS schemes are useful for applications that involve privately reading from or writing to distributed databases while minimizing the amount of communication. These include different flavors of private information retrieval (PIR), as well as a recent application of DPF for large-scale anonymous messaging. We improve and extend previous results in several ways: * Simplified FSS constructions. We introduce a tensoring operation for FSS which is used to obtain a conceptually simpler derivation of previous constructions and present our new constructions. * Improved 2-party DPF. We reduce the key size of the PRG-based DPF scheme of Boyle et al. roughly by a factor of 4 and optimize its computational cost. The optimized DPF significantly improves the concrete costs of 2-server PIR and related primitives. * FSS for new function families. We present an efficient PRG-based 2-party FSS scheme for the family of decision trees, leaking only the topology of the tree and the internal node labels. We apply this towards FSS for multi-dimensional intervals. We also present a general technique for extending FSS schemes by increasing the number of parties. * Verifiable FSS. We present efficient protocols for verifying that keys (k*/1,...,k*/m ), obtained from a potentially malicious user, are consistent with some f in F. Such a verification may be critical for applications that involve private writing or voting by many users.
The Google Identity Platform is a system that allows a user to sign in to applications and other services by using a Google account. Google Sign-In is one such method for providing one’s identity to the Google Identity Platform. Google Sign-In is available for Android applications and iOS applications, as well as for websites and other devices. Users of Google Sign-In find that it integrates well with the Android platform, but iOS users (iPhone, iPad, etc.) do not have the same experience. The user experience when logging in to a Google account on an iOS application can not only be more tedious than the Android experience, but it also conditions users to engage in behaviors that put the information in their Google accounts at risk.
In this paper, we propose a hierarchical monitoring intrusion detection system (HAMIDS) for industrial control systems (ICS). The HAMIDS framework detects the anomalies in both level 0 and level 1 of an industrial control plant. In addition, the framework aggregates the cyber-physical process data in one point for further analysis as part of the intrusion detection process. The novelty of this framework is its ability to detect anomalies that have a distributed impact on the cyber-physical process. The performance of the proposed framework evaluated as part of SWaT security showdown (S3) in which six international teams were invited to test the framework in a real industrial control system. The proposed framework outperformed other proposed academic IDS in term of detection of ICS threats during the S3 event, which was held from July 25-29, 2016 at Singapore University of Technology and Design.
Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic. In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\textbackslash% certainty.
Motivated by the impossibility of achieving fairness in secure computation [Cleve, STOC 1986], recent works study a model of fairness in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty to every other party that did not receive the output. These works show how to design protocols for secure computation with penalties that tolerate an arbitrary number of corruptions. In this work, we improve the efficiency of protocols for secure computation with penalties in a hybrid model where parties have access to the "claim-or-refund" transaction functionality. Our first improvement is for the ladder protocol of Bentov and Kumaresan (Crypto 2014) where we improve the dependence of the script complexity of the protocol (which corresponds to miner verification load and also space on the blockchain) on the number of parties from quadratic to linear (and in particular, is completely independent of the underlying function). Our second improvement is for the see-saw protocol of Kumaresan et al. (CCS 2015) where we reduce the total number of claim-or-refund transactions and also the script complexity from quadratic to linear in the number of parties.
TLS and SSH are two of the most commonly used protocols for securing Internet traffic. Many of the implementations of these protocols rely on the cryptographic primitives provided in the OpenSSL library. In this work we disclose a vulnerability in OpenSSL, affecting all versions and forks (e.g. LibreSSL and BoringSSL) since roughly October 2005, which renders the implementation of the DSA signature scheme vulnerable to cache-based side-channel attacks. Exploiting the software defect, we demonstrate the first published cache-based key-recovery attack on these protocols: 260 SSH-2 handshakes to extract a 1024/160-bit DSA host key from an OpenSSH server, and 580 TLS 1.2 handshakes to extract a 2048/256-bit DSA key from an stunnel server.
We give attacks on Feistel-based format-preserving encryption (FPE) schemes that succeed in message recovery (not merely distinguishing scheme outputs from random) when the message space is small. For \$4\$-bit messages, the attacks fully recover the target message using \$2textasciicircum1 examples for the FF3 NIST standard and \$2textasciicircum5 examples for the FF1 NIST standard. The examples include only three messages per tweak, which is what makes the attacks non-trivial even though the total number of examples exceeds the size of the domain. The attacks are rigorously analyzed in a new definitional framework of message-recovery security. The attacks are easily put out of reach by increasing the number of Feistel rounds in the standards.
Physical Unclonable Functions (PUFs) measure manufacturing variations inside integrated circuits to derive internal secrets during run-time and avoid to store secrets permanently in non-volatile memory. PUF responses are noisy such that they require error correction to generate reliable cryptographic keys. To date, when needed one single key is reproduced in the field and always used, regardless of its reliability. In this work, we compute online reliability information for a reproduced key and perform multiple PUF readout and error correction steps in case of an unreliable result. This permits to choose the most reliable key among multiple derived key candidates with different corrected error patterns. We achieve the same average key error probability from less PUF response bits with this approach. Our proof of concept design for a popular reference scenario uses Differential Sequence Coding (DSC) and a Viterbi decoder with reliability output information. It requires 39% less PUF response bits and 16% less helper data bits than the regular approach without the option for multiple readouts.
In the last few years, there has been significant interest in developing methods to search over encrypted data. In the case of range queries, a simple solution is to encrypt the contents of the database using an order-preserving encryption (OPE) scheme (i.e., an encryption scheme that supports comparisons over encrypted values). However, Naveed et al. (CCS 2015) recently showed that OPE-encrypted databases are extremely vulnerable to "inference attacks." In this work, we consider a related primitive called order-revealing encryption (ORE), which is a generalization of OPE that allows for stronger security. We begin by constructing a new ORE scheme for small message spaces which achieves the "best-possible" notion of security for ORE. Next, we introduce a "domain extension" technique and apply it to our small-message-space ORE. While our domain-extension technique does incur a loss in security, the resulting ORE scheme we obtain is more secure than all existing (stateless and non-interactive) OPE and ORE schemes which are practical. All of our constructions rely only on symmetric primitives. As part of our analysis, we also give a tight lower bound for OPE and show that no efficient OPE scheme can satisfy best-possible security if the message space contains just three messages. Thus, achieving strong notions of security for even small message spaces requires moving beyond OPE. Finally, we examine the properties of our new ORE scheme and show how to use it to construct an efficient range query protocol that is robust against the inference attacks of Naveed et al. We also give a full implementation of our new ORE scheme, and show that not only is our scheme more secure than existing OPE schemes, it is also faster: encrypting a 32-bit integer requires just 55 microseconds, which is more than 65 times faster than existing OPE schemes.
Privacy-preserving range queries allow encrypting data while still enabling queries on ciphertexts if their corresponding plaintexts fall within a requested range. This provides a data owner the possibility to outsource data collections to a cloud service provider without sacrificing privacy nor losing functionality of filtering this data. However, existing methods for range queries either leak additional information (like the ordering of the complete data set) or slow down the search process tremendously by requiring to query each ciphertext in the data collection. We present a novel scheme that only leaks the access pattern while supporting amortized poly-logarithmic search time. Our construction is based on the novel idea of enabling the cloud service provider to compare requested range queries. By doing so, the cloud service provider can use the access pattern to speed-up search time for range queries in the future. On the one hand, values that have fallen within a queried range, are stored in an interactively built index for future requests. On the other hand, values that have not been queried do not leak any information to the cloud service provider and stay perfectly secure. In order to show its practicability we have implemented our scheme and give a detailed runtime evaluation.
It is estimated that 50% of the global population lives in urban areas occupying just 0.4% of the Earth's surface. Understanding urban activity constitutes monitoring population density and its changes over time, in urban environments. Currently, there are limited mechanisms to non-intrusively monitor population density in real-time. The pervasive use of cellular phones in urban areas is one such mechanism that provides a unique opportunity to study population density by monitoring the mobility patterns in near real-time. Cellular carriers such as AT&T harvest such data through their cell towers; however, this data is proprietary and the carriers restrict access, due to privacy concerns. In this work, we propose a system that passively senses the population density and infers mobility patterns in an urban area by monitoring power spectral density in cellular frequency bands using periodic beacons from each cellphone without knowing who and where they are located. A wireless sensor network platform is being developed to perform spectral monitoring along with environmental measurements. Algorithms are developed to generate real-time fine-resolution population estimates.
Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, embody at their core a blockchain protocol –- a mechanism for a distributed network of computational nodes to periodically agree on a set of new transactions. Designing a secure blockchain protocol relies on an open challenge in security, that of designing a highly-scalable agreement protocol open to manipulation by byzantine or arbitrarily malicious nodes. Bitcoin's blockchain agreement protocol exhibits security, but does not scale: it processes 3–7 transactions per second at present, irrespective of the available computation capacity at hand. In this paper, we propose a new distributed agreement protocol for permission-less blockchains called ELASTICO. ELASTICO scales transaction rates almost linearly with available computation for mining: the more the computation power in the network, the higher the number of transaction blocks selected per unit time. ELASTICO is efficient in its network messages and tolerates byzantine adversaries of up to one-fourth of the total computational power. Technically, ELASTICO uniformly partitions or parallelizes the mining network (securely) into smaller committees, each of which processes a disjoint set of transactions (or "shards"). While sharding is common in non-byzantine settings, ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries. Our scalability experiments on Amazon EC2 with up to \$1, 600\$ nodes confirm ELASTICO's theoretical scaling properties.
The blockchain emerges as an innovative tool which proves to be useful in a number of application scenarios. A number of large industrial players, such as IBM, Microsoft, Intel, and NEC, are currently investing in exploiting the blockchain in order to enrich their portfolio of products. A number of researchers and practitioners speculate that the blockchain technology can change the way we see a number of online applications today. Although it is still early to tell for sure, it is expected that the blockchain will stimulate considerable changes to a large number of products and will positively impact the digital experience of many individuals around the globe. In this tutorial, we overview, detail, and analyze the security provisions of Bitcoin and its underlying blockchain-effectively capturing recently reported attacks and threats in the system. Our contributions go beyond the mere analysis of reported vulnerabilities of Bitcoin; namely, we describe and evaluate a number of countermeasures to deter threats on the system-some of which have already been incorporated in the system. Recall that Bitcoin has been forked multiple times in order to fine-tune the consensus (i.e., the block generation time and the hash function), and the network parameters (e.g., the size of blocks). As such, the results reported in this tutorial are not only restricted to Bitcoin, but equally apply to a number of "altcoins" which are basically clones/forks of the Bitcoin source code. Given the increasing number of alternative blockchain proposals, this tutorial extracts the basic security lessons learnt from the Bitcoin system with the aim to foster better designs and analysis of next-generation secure blockchain currencies and technologies.
Password vaults are used to store login credentials, usually encrypted by a master password, relieving the user from memorizing a large number of complex passwords. To manage accounts on multiple devices, vaults are often stored at an online service, which substantially increases the risk of leaking the (encrypted) vault. To protect the master password against guessing attacks, previous work has introduced cracking-resistant password vaults based on Honey Encryption. If decryption is attempted with a wrong master password, they output plausible-looking decoy vaults, thus seemingly disabling offline guessing attacks. In this work, we propose attacks against cracking-resistant password vaults that are able to distinguish between real and decoy vaults with high accuracy and thus circumvent the offered protection. These attacks are based on differences in the generated distribution of passwords, which are measured using Kullback-Leibler divergence. Our attack is able to rank the correct vault into the 1.3% most likely vaults (on median), compared to 37.8% of the best-reported attack in previous work. (Note that smaller ranks are better, and 50% is achievable by random guessing.) We demonstrate that this attack is, to a certain extent, a fundamental problem with all static Natural Language Encoders (NLE), where the distribution of decoy vaults is fixed. We propose the notion of adaptive NLEs and demonstrate that they substantially limit the effectiveness of such attacks. We give one example of an adaptive NLE based on Markov models and show that the attack is only able to rank the decoy vaults with a median rank of 35.1%.