Biblio

Modern browsers implement different security policies such as the Content Security Policy (CSP), a mechanism designed to mitigate popular web vulnerabilities, and the Same Origin Policy (SOP), a mechanism that governs interactions between resources of web pages. In this work, we describe how CSP may be violated due to the SOP when a page contains an embedded iframe from the same origin. We analyse 1 million pages from 10,000 top Alexa sites and report that at least 31.1% of current CSP-enabled pages are potentially vulnerable to CSP violations. Further considering real-world situations where those pages are involved in same-origin nested browsing contexts, we found that in at least 23.5% of the cases, CSP violations are possible. During our study, we also identified a divergence among browsers implementations in the enforcement of CSP in srcdoc sandboxed iframes, which actually reveals a problem in Gecko-based browsers CSP implementation. To ameliorate the problematic conflicts of the security mechanisms, we discuss measures to avoid CSP violations.

As our society massively relies on ICT, security services are becoming essential to protect users and entities involved. Amongst such services, non-repudiation provides evidences of actions, protects against their denial, and helps solving disputes between parties. For example, it prevents denial of past behaviors as having sent or received messages. Noteworthy, if the information flow is continuous, evidences should be produced for the entirety of the flow and not only at specific points. Further, non-repudiation should be guaranteed by mechanisms that do not reduce the usability of the system or application. To meet these challenges, in this paper, we propose two solutions for non-repudiation of remote services based on multi-biometric continuous authentication. We present an application scenario that discusses how users and service providers are protected with such solutions. We also discuss the technological readiness of biometrics for non-repudiation services: the outcome is that, under specific assumptions, it is actually ready.

Memory corruption errors in C/C++ programs remain the most common source of security vulnerabilities in today’s systems. Control-flow hijacking attacks exploit memory corruption vulnerabilities to divert program execution away from the intended control flow. Researchers have spent more than a decade studying and refining defenses based on Control-Flow Integrity (CFI); this technique is now integrated into several production compilers. However, so far, no study has systematically compared the various proposed CFI mechanisms nor is there any protocol on how to compare such mechanisms. We compare a broad range of CFI mechanisms using a unified nomenclature based on (i) a qualitative discussion of the conceptual security guarantees, (ii) a quantitative security evaluation, and (iii) an empirical evaluation of their performance in the same test environment. For each mechanism, we evaluate (i) protected types of control-flow transfers and (ii) precision of the protection for forward and backward edges. For open-source, compiler-based implementations, we also evaluate (iii) generated equivalence classes and target sets and (iv) runtime performance.

Hybrid mobile applications are coded in both standard web languages and native language. The including of web technologies results in that Hybrid applications introduce more security risks than the traditional web applications, which have more possible channels to inject malicious codes to gain much more powerful privileges. In this paper, Cross-site Scripting attacks specific to Android Hybrid apps developed with PhoneGap framework are investigated. We find out that the XSS vulnerability on Hybrid apps makes it possible for attackers to bypass the access control policies of WebView and WebKit to run malicious codes into victim's WebView. With the PhoneGap plugins, the malicious codes can steal user's private information and destroy user's file system, which are more damaging than cookie stealing.

Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in the industry, their outputs could affect business decision making, introducing incentives for an adversarial party to compromise the availability or integrity of such systems. We introduce a data poisoning attack on collaborative filtering systems. We demonstrate how a powerful attacker with full knowledge of the learner can generate malicious data so as to maximize his/her malicious objectives, while at the same time mimicking normal user behavior to avoid being detected. While the complete knowledge assumption seems extreme, it enables a robust assessment of the vulnerability of collaborative filtering schemes to highly motivated attacks. We present efficient solutions for two popular factorization-based collaborative filtering algorithms: the alternative minimization formulation and the nuclear norm minimization method. Finally, we test the effectiveness of our proposed algorithms on real-world data and discuss potential defensive strategies.

We present an approach for improving the performance of complex knowledge-based processes by providing data-driven step-by-step recommendations. Our framework uses the associations between similar historic process performances and contextual information to determine the prototypical way of enacting the process. We introduce a novel similarity metric for grouping traces into clusters that incorporates temporal information about activity performance and handles concurrent activities. Our data-driven recommender system selects the appropriate prototype performance of the process based on user-provided context attributes. Our approach for determining the prototypes discovers the commonly performed activities and their temporal relationships. We tested our system on data from three real-world medical processes and achieved recommendation accuracy up to an F1 score of 0.77 (compared to an F1 score of 0.37 using ZeroR) with 63.2% of recommended enactments being within the first five neighbors of the actual historic enactments in a set of 87 cases. Our framework works as an interactive visual analytic tool for process mining. This work shows the feasibility of data-driven decision support system for complex knowledge-based processes.

Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cybersecurity. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated API call blocks, we then explore deep neural networks (i.e., Deep Belief Network (DBN) and Stacked AutoEncoders (SAEs)) for newly unknown Android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. The experimental results demonstrate that (1) our proposed feature extraction method (i.e., using API call blocks) outperforms using API calls directly in Android malware detection; (2) DBN works better than SAEs in this application; and (3) the detection performance of deep neural networks is better than shallow learning architectures.

Despite their ubiquity, many password meters provide inaccurate strength estimates. Furthermore, they do not explain to users what is wrong with their password or how to improve it. We describe the development and evaluation of a data-driven password meter that provides accurate strength measurement and actionable, detailed feedback to users. This meter combines neural networks and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about the user's password. We describe the meter's iterative development and final design. We detail the security and usability impact of the meter's design dimensions, examined through a 4,509-participant online study. Under the more common password-composition policy we tested, we found that the data-driven meter with detailed feedback led users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator.

We consider the problem of privacy-sensitive anomaly detection - screening to detect individuals, behaviors, areas, or data samples of high interest. What defines an anomaly is context-specific; for example, a spoofed rather than genuine user attempting to log in to a web site, a fraudulent credit card transaction, or a suspicious traveler in an airport. The unifying assumption is that the number of anomalous points is quite small with respect to the population, so that deep screening of all individual data points would potentially be time-intensive, costly, and unnecessarily invasive of privacy. Such privacy violations can raise concerns due sensitive nature of data being used, raise fears about violations of data use agreements, and make people uncomfortable with anomaly detection methods. Anomaly detection is well studied, but methods to provide anomaly detection along with privacy are less well studied. Our overall goal in this research is to provide a framework for identifying anomalous data while guaranteeing quantifiable privacy in a rigorous sense. Once identified, such anomalies could warrant further data collection and investigation, depending on the context and relevant policies. In this research, we focus on privacy protection during the deployment of anomaly detection. Our main contribution is a differentially private access mechanism for finding anomalies using a search algorithm based on adaptive noisy group testing. To achieve this, we take as our starting point the notion of group testing [1], which was most famously used to screen US military draftees for syphilis during World War II. In group testing, individuals are tested in groups to limit the number of tests. Using multiple rounds of screenings, a small number of positive individuals can be detected very efficiently. Group testing has the added benefit of providing privacy to individuals through plausible deniability - since the group tests use aggregate data, individual contributions to the test are masked by the group. We follow on these concepts by demonstrating a search model utilizing adaptive queries on aggregated group data. Our work takes the first steps toward strengthening and formalizing these privacy concepts by achieving differential privacy [2]. Differential privacy is a statistical measure of disclosure risk that captures the intuition that an individual's privacy is protected if the results of a computation have at most a very small and quantifiable dependence on that individual's data. In the last decade, there hpractical adoption underway by high-profile companies such as Apple, Google, and Uber. In order to make differential privacy meaningful in the context of a task that seeks to specifically identify some (anomalous) individuals, we introduce the notion of anomaly-restricted differential privacy. Using ideas from information theory, we show that noise can be added to group query results in a way that provides differential privacy for non-anomalous individuals and still enables efficient and accurate detection of the anomalous individuals. Our method ensures that using differentially private aggregation of groups of points, providing privacy to individuals within the group while refining the group selection to the point that we can probabilistically narrow attention to a small numbers of individuals or samples for further attention. To summarize: We introduce a new notion of anomaly-restriction differential privacy, which may be of independent interest. We provide a noisy group-based search algorithm that satisfies the anomaly-restricted differential privacy definition. We provide both theoretical and empirical analysis of our noisy search algorithm, showing that it performs well in some cases, and exhibits the usual privacy/accuracy tradeoff of differentially private mechanisms. Potential anomaly detection applications for our work might include spatial search for outliers: this would rely on new sensing technologies that can perform queries in aggregate to reveal and isolate anomalous outliers. For example, this could lead to privacy-sensitive methods for searching for outlying cell phone activity patterns or Internet activity patterns in a geographic location.

We present dRMT (disaggregated Reconfigurable Match-Action Table), a new architecture for programmable switches. dRMT overcomes two important restrictions of RMT, the predominant pipeline-based architecture for programmable switches: (1) table memory is local to an RMT pipeline stage, implying that memory not used by one stage cannot be reclaimed by another, and (2) RMT is hardwired to always sequentially execute matches followed by actions as packets traverse pipeline stages. We show that these restrictions make it difficult to execute programs efficiently on RMT. dRMT resolves both issues by disaggregating the memory and compute resources of a programmable switch. Specifically, dRMT moves table memories out of pipeline stages and into a centralized pool that is accessible through a crossbar. In addition, dRMT replaces RMT's pipeline stages with a cluster of processors that can execute match and action operations in any order. We show how to schedule a P4 program on dRMT at compile time to guarantee deterministic throughput and latency. We also present a hardware design for dRMT and analyze its feasibility and chip area. Our results show that dRMT can run programs at line rate with fewer processors compared to RMT, and avoids performance cliffs when there are not enough processors to run a program at line rate. dRMT's hardware design incurs a modest increase in chip area relative to RMT, mainly due to the crossbar.

Big data is a buzzword used to describe massive volumes of data that provides opportunities of exploring new insights through data analytics. However, big data is mostly structured but can be semi-structured or unstructured. It is normally so large that it is not only difficult but also slow to process using traditional computing systems. One of the solutions is to format the data as graph data structures and process them on shared memory architecture to use fast and novel policies such as transactional memory. In most graph applications in big data type problems such as bioinformatics, social networks, and cybersecurity, graphs are sparse in nature. Due to this sparsity, we have the opportunity to use Transactional Memory (TM) as the synchronization policy for critical sections to speedup applications. At low conflict probability TM performs better than most synchronization policies due to its inherent non-blocking characteristics. TM can be implemented in Software, Hardware or a combination of both. However, hardware TM implementations are fast but limited by scarce hardware resources while software implementations have high overheads which can degrade performance. In this paper, we develop a low overhead, yet simple, dynamically adaptive (i.e., at runtime) hybrid (i.e., combines hardware and software) TM (DyAd-HyTM) scheme that combines the best features of both Hardware TM (HTM) and Software TM (STM) while adapting to application's requirements. It performs better than coarse-grain lock by up to 8.12x, a low overhead STM by up to 2.68x, a couple of implementations of HTMs (by up to 2.59x), and other HyTMs (by up to 1.55x) for SSCA-2 graph benchmark running on a multicore machine with a large shared memory.

Memory corruptions are still the most prominent venue to attack otherwise secure programs. In order to make exploitation of software bugs more difficult, defenders introduced a vast number of post corruption security mitigations, such as w⊕x memory, Stack Canaries, and Address Space Layout Randomization (ASLR), to only name a few. In the following, we describe the Wiedergänger1-Attack, a new attack vector that reliably allows to escalate unbounded array access vulnerabilities occurring in specifically allocated memory regions to full code execution on programs running on i386/x86\_64 Linux. Wiedergänger-attacks abuse determinism in Linux ASLR implementation combined with the fact that (even with protection mechanisms such as relro and glibc's pointer mangling enabled) there exist easy-to-hijack, writable (function) pointers in application memory. To discover such pointers, we use taint analysis and backwards slicing at the binary level and calculate an over-approximation of vulnerable instruction sequences. To show the relevance of Wiedergänger, we exploit one of the discovered instruction sequences to perform an attack on Debian 10 (Buster) by overwriting structures used by the dynamic loader (dl) that are present in any application with glibc and the dynamic loader as dependency. In order to show generality, we solely focus on data structures dispatched at program shutdown, as this is a point that arguably all applications eventually have to reach. This results in a reliable compromise that effectively bypasses all protection mechanisms deployed on x86\_64/i386 Linux to date. We believe Wiedergänger to be part of an under-researched type of control flow hijacking attacks targeting internal control structures of the dynamic loader for which we propose to use the terminology Loader Oriented Programming (LOP).

In the face of large-scale automated cyber-attacks to large online services, fast detection and remediation of compromised accounts are crucial to limit the spread of new attacks and to mitigate the overall damage to users, companies, and the public at large. We advocate a fully automated approach based on machine learning to enable large-scale online service providers to quickly identify potentially compromised accounts. We develop an early warning system for the detection of suspicious account activity with the goal of quick identification and remediation of compromised accounts. We demonstrate the feasibility and applicability of our proposed system in a four month experiment at a large-scale online service provider using real-world production data encompassing hundreds of millions of users. We show that - even using only login data, features with low computational cost, and a basic model selection approach - around one out of five accounts later flagged as suspicious are correctly predicted a month in advance based on one week's worth of their login activity.

In order to compactly represent a set of data, its medoid (the element with minimum summed distance to all other elements) is a useful choice. This has applications in clustering, compression and visualisation of data. In multimedia data, the set of data is often sampled as a sequence in time or space, such as a video shot or views of a scene. The exact calculation of the medoid may be costly, especially if the distance function between elements is not trivial. While approximation methods for medoid selection exist, we show in this work that they do not perform well on sequences of images. We thus propose a novel algorithm for efficiently selecting an approximate medoid of a temporal sequence and assess its performance on two large-scale video data sets.

The past decade has shown us the power of cyber space and we getting dependent on the same. The exponential evolution in the domain has attracted attackers and defenders of technology equally. This inevitable domain has led to the increase in average human awareness and knowledge too. As we see the attack sophistication grow the protectors have always been a step ahead mitigating the attacks. A study of the various Threat Detection, Protection and Mitigation Systems revealed to us a common similarity wherein users have been totally ignored or the systems rely heavily on the user inputs for its correct functioning. Compiling the above we designed a study wherein user inputs were taken in addition to independent Detection and Prevention systems to identify and mitigate the risks. This approach led us to a conclusion that involvement of users exponentially enhances machine learning and segments the data sets faster for a more reliable output.

In this paper, we evaluate the usefulness of metrics that assess susceptibility to cascading blackouts. The metrics are computed using a matrix of Line Outage Distribution Factors (LODF, or DFAX matrix). The metrics are compared for several base cases with different load levels of the Western Interconnection (WI). A case corresponding to the September 8, 2011 pre-blackout state is used to compute these metrics and relate them to the origin of the cascading blackout. The correlation between the proposed metrics is determined to check redundancy. The analysis is also used to find vulnerable and critical hot spots in the power system.

We present a high-assurance software stack for secure function evaluation (SFE). Our stack consists of three components: i. a verified compiler (CircGen) that translates C programs into Boolean circuits; ii. a verified implementation of Yao's SFE protocol based on garbled circuits and oblivious transfer; and iii. transparent application integration and communications via FRESCO, an open-source framework for secure multiparty computation (MPC). CircGen is a general purpose tool that builds on CompCert, a verified optimizing compiler for C. It can be used in arbitrary Boolean circuit-based cryptography deployments. The security of our SFE protocol implementation is formally verified using EasyCrypt, a tool-assisted framework for building high-confidence cryptographic proofs, and it leverages a new formalization of garbled circuits based on the framework of Bellare, Hoang, and Rogaway (CCS 2012). We conduct a practical evaluation of our approach, and conclude that it is competitive with state-of-the-art (unverified) approaches. Our work provides concrete evidence of the feasibility of building efficient, verified, implementations of higher-level cryptographic systems. All our development is publicly available.

Concise is a Forwarding information base (FIB) design that uses very little memory to support fast query of a large number of dynamic network names or flow IDs. Concise makes use of minimal perfect hashing and the SDN framework to design and implement the data structure, protocols, and system. Experimental results show that Concise uses significantly smaller memory to achieve faster query speed compared to existing FIB solutions and it can be updated very efficiently.

Driven by recent advances in the mobile computing hardware ecosystem, wearable Virtual Reality (VR) is experiencing a boom in popularity, with many offerings becoming available. Modern VR head-mounted displays (HMDs) fall into two device classes: (i) Tethered HMDs: HMDs tethered to powerful, expensive gaming desktops, such as the Oculus Rift, HTC Vive, and Sony PlayStation VR; (ii) Mobile-rendered HMDs: self-contained, untethered HMDs that run on mobile phones slotted into head mounts, e.g., Google Cardboard and Samsung Gear VR.

Using dynamic Searchable Symmetric Encryption, a user with limited storage resources can securely outsource a database to an untrusted server, in such a way that the database can still be searched and updated efficiently. For these schemes, it would be desirable that updates do not reveal any information a priori about the modifications they carry out, and that deleted results remain inaccessible to the server a posteriori. If the first property, called forward privacy, has been the main motivation of recent works, the second one, backward privacy, has been overlooked. In this paper, we study for the first time the notion of backward privacy for searchable encryption. After giving formal definitions for different flavors of backward privacy, we present several schemes achieving both forward and backward privacy, with various efficiency trade-offs. Our constructions crucially rely on primitives such as constrained pseudo-random functions and puncturable encryption schemes. Using these advanced cryptographic primitives allows for a fine-grained control of the power of the adversary, preventing her from evaluating functions on selected inputs, or decrypting specific ciphertexts. In turn, this high degree of control allows our SSE constructions to achieve the stronger forms of privacy outlined above. As an example, we present a framework to construct forward-private schemes from range-constrained pseudo-random functions. Finally, we provide experimental results for implementations of our schemes, and study their practical efficiency.

Systems for verifiable outsourcing incur costs for a prover, a verifier, and precomputation; outsourcing makes sense when the combination of these costs is cheaper than not outsourcing. Yet, when prior works impose quantitative thresholds to analyze whether outsourcing is justified, they generally ignore prover costs. Verifiable ASICs (VA)—in which the prover is a custom chip—is the other way around: its cost calculations ignore precomputation. This paper describes a new VA system, called Giraffe; charges Giraffe for all three costs; and identifies regimes where outsourcing is worthwhile. Giraffe's base is an interactive proof geared to data-parallel computation. Giraffe makes this protocol asymptotically optimal for the prover and improves the verifier's main bottleneck by almost 3x, both of which are of independent interest. Giraffe also develops a design template that produces hardware designs automatically for a wide range of parameters, introduces hardware primitives molded to the protocol's data flows, and incorporates program analyses that expand applicability. Giraffe wins even when outsourcing several tens of sub-computations, scales to 500x larger computations than prior work, and can profitably outsource parts of programs that are not worthwhile to outsource in full.

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost, as many IoT devices may not have sufficient resources to host expensive security tools. In this paper, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security tools. We model our problem according to game theory, and provide a Pareto-optimal solution, in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack, are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies.

We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, or "compression," of an arbitrary dense symmetric positive definite (SPD) matrix. For many applications, GOFMM enables an approximate matrix-vector multiplication in N log N or even N time, where N is the matrix size. Compression requires N log N storage and work. In general, our scheme belongs to the family of hierarchical matrix approximation methods. In particular, it generalizes the fast multipole method (FMM) to a purely algebraic setting by only requiring the ability to sample matrix entries. Neither geometric information (i.e., point coordinates) nor knowledge of how the matrix entries have been generated is required, thus the term "geometry-oblivious." Also, we introduce a shared-memory parallel scheme for hierarchical matrix computations that reduces synchronization barriers. We present results on the Intel Knights Landing and Haswell architectures, and on the NVIDIA Pascal architecture for a variety of matrices.

Radio Frequency Identification (RFID) devices are widely used in many domains such as tracking, marking and management of goods, smart houses (IoT), supply chains, etc. However, there is a big number of challenges which must still be overcome to ensure RFID security and privacy. In addition, due to the low cost and low consumption power of UHF RFID tags, communications between tags and readers are not robust. In this paper, we present our approach to evaluate at the same time the security and the safety of UHF RFID systems in order to improve them. First, this approach allows validating UHF RFID systems by simulation of the system behavior in presence of faults in a real environment. Secondly, evaluating the system robustness and the security of the used protocols, this approach will enable us to propose the development of new more reliable and secure protocols. Finally, it leads us to develop and validate new low cost and secure tag hardware architectures.

HACL* is a verified portable C cryptographic library that implements modern cryptographic primitives such as the ChaCha20 and Salsa20 encryption algorithms, Poly1305 and HMAC message authentication, SHA-256 and SHA-512 hash functions, the Curve25519 elliptic curve, and Ed25519 signatures. HACL* is written in the F* programming language and then compiled to readable C code. The F* source code for each cryptographic primitive is verified for memory safety, mitigations against timing side-channels, and functional correctness with respect to a succinct high-level specification of the primitive derived from its published standard. The translation from F* to C preserves these properties and the generated C code can itself be compiled via the CompCert verified C compiler or mainstream compilers like GCC or CLANG. When compiled with GCC on 64-bit platforms, our primitives are as fast as the fastest pure C implementations in OpenSSL and libsodium, significantly faster than the reference C code in TweetNaCl, and between 1.1x-5.7x slower than the fastest hand-optimized vectorized assembly code in SUPERCOP. HACL* implements the NaCl cryptographic API and can be used as a drop-in replacement for NaCl libraries like libsodium and TweetNaCl. HACL* provides the cryptographic components for a new mandatory ciphersuite in TLS 1.3 and is being developed as the main cryptographic provider for the miTLS verified implementation. Primitives from HACL* are also being integrated within Mozilla's NSS cryptographic library. Our results show that writing fast, verified, and usable C cryptographic libraries is now practical.