Biblio

Among the signature schemes most widely deployed in practice are the DSA (Digital Signature Algorithm) and its elliptic curves variant ECDSA. They are represented in many international standards, including IEEE P1363, ANSI X9.62, and FIPS 186-4. Their popularity stands in stark contrast to the absence of rigorous security analyses: Previous works either study modified versions of (EC)DSA or provide a security analysis of unmodified ECDSA in the generic group model. Unfortunately, works following the latter approach assume abstractions of non-algebraic functions over generic groups for which it remains unclear how they translate to the security of ECDSA in practice. For instance, it has been pointed out that prior results in the generic group model actually establish strong unforgeability of ECDSA, a property that the scheme de facto does not possess. As, further, no formal results are known for DSA, understanding the security of both schemes remains an open problem. In this work we propose GenericDSA, a signature framework that subsumes both DSA and ECDSA in unmodified form. It carefully models the "modulo q" conversion function of (EC)DSA as a composition of three independent functions. The two outer functions mimic algebraic properties in the function's domain and range, the inner one is modeled as a bijective random oracle. We rigorously prove results on the security of GenericDSA that indicate that forging signatures in (EC)DSA is as hard as solving discrete logarithms. Importantly, our proofs do not assume generic group behavior.

Motivated by the growing complexity and heterogeneity of modern data centers, and the prevalence of commodity component failures, this paper studies the failure-aware placement problem of placing tasks of a parallel job on machines in the data center with the goal of increasing availability. We consider two models of failures: adversarial and probabilistic. In the adversarial model, each node has a weight (higher weight implying higher reliability) and the adversary can remove any subset of nodes of total weight at most a given bound W and our goal is to find a placement that incurs the least disruption against such an adversary. In the probabilistic model, each node has a probability of failure and we need to find a placement that maximizes the probability that at least K out of N tasks survive at any time. For adversarial failures, we first show that (i) the problems are in Σ2, the second level of the polynomial hierarchy, (ii) a basic variant, that we call RobustFAP, is co-NP-hard, and (iii) an all-or-nothing version of RobustFAP is Σ2-complete. We then give a PTAS for RobustFAP, a key ingredient of which is a solution that we design for a fractional version of RobustFAP. We then study fractional RobustFAP over hierarchies, denoted HierRobustFAP, and introduce a notion of hierarchical max-min fairness/ and a novel Generalized Spreading/ algorithm which is simultaneously optimal for all W. These generalize the classical notion of max-min fairness to work with nodes of differing capacities, differing reliability weights and hierarchical structures. Using randomized rounding, we extend this to give an algorithm for integral HierRobustFAP. For the probabilistic version, we first give an algorithm that achieves an additive ε approximation in the failure probability for the single level version, called ProbFAP, while giving up a (1 + ε) multiplicative factor in the number of failures. We then extend the result to the hierarchical version, HierProbFAP, achieving an ε additive approximation in failure probability while giving up an (L + ε) multiplicative factor in the number of failures, where \$L\$ is the number of levels in the hierarchy.

Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks – at the level of small network subgraphs – remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns. Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

We explicitly construct an extractor for two independent sources on n bits, each with polylogarithmic min-entropy. Our extractor outputs one bit and has polynomially small error. The best previous extractor, by Bourgain, required each source to have min-entropy .499n. A key ingredient in our construction is an explicit construction of a monotone, almost-balanced Boolean functions that are resilient to coalitions. In fact, our construction is stronger in that it gives an explicit extractor for a generalization of non-oblivious bit-fixing sources on n bits, where some unknown n-q bits are chosen almost polylogarithmic-wise independently, and the remaining q bits are chosen by an adversary as an arbitrary function of the n-q bits. The best previous construction, by Viola, achieved q quadratically smaller than our result. Our explicit two-source extractor directly implies improved constructions of a K-Ramsey graph over N vertices, improving bounds obtained by Barak et al. and matching independent work by Cohen.

Cryptographic hash functions are used to protect the integrity of information. Hash functions are designed by using existing block ciphers as compression functions. This is due to challenges and difficulties that are encountered in constructing new hash functions from the scratch. However, the key generations for encryption process result to huge computational cost which affects the efficiency of the hash function. This paper proposes a new, secure and efficient compression function based on a pseudorandom function, that takes in two 2n-bits inputs and produce one n-bit output (2n-to-n bit). In addition, a new keyed hash function with three variants is proposed (PinTar 128 bits, 256 bits and 512 bits) which uses the proposed compression as its underlying building block. Statistical analysis shows that the compression function is an efficient one way random function. Similarly, statistical analysis of the keyed hash function shows that the proposed keyed function has strong avalanche property and is resistant to key exhaustive search attack. The proposed key hash function can be used as candidate for developing security systems.

We propose pseudo random bit generator(PRBG) by constructing an ergodic orbit of logistic map generated using intermittent perturbation and sampling the orbit using Bernoulli shift map. The pseudo randomness of the key streams is tested with the standard NIST statistical test suite. The key streams generated by the proposed PRBG pass all the tests of the test suite. As an application of PRBG we propose chaotic stream ciphers. Goodness-of-Fit tests are conducted for these ciphers. Stream ciphers generated using PRBG are shown to generate ciphers in which the distribution of the characters show significant correlation to the uniform distribution. It is also shown that the ciphers exhibit good avalanche effect when the initial seed is varied infinitesimally and we can also clearly observe the avalanche effect at different points within the ciphertext as the ciphertext is being generated. Also we show that the proposed PRBG is on par with conventional PRBG RC4. This indeed vindicates our hypothesis that stream of bits obtained from orbits of logistic map using perturbation and Bernoulli shift map exhibits randomness.

Growing numbers of ubiquitous electronic devices and services motivate the need for effortless user authentication and identification. While biometrics are a natural means of achieving these goals, their use poses privacy risks, due mainly to the difficulty of preventing theft and abuse of biometric data. One way to minimize information leakage is to derive biometric keys from users' raw biometric measurements. Such keys can be used in subsequent security protocols and ensure that no sensitive biometric data needs to be transmitted or permanently stored. This paper is the first attempt to explore the use of human body impedance as a biometric trait for deriving secret keys. Building upon Randomized Biometric Templates as a key generation scheme, we devise a mechanism that supports consistent regeneration of unique keys from users' impedance measurements. The underlying set of biometric features are found using a feature learning technique based on Siamese networks. Compared to prior feature extraction methods, the proposed technique offers significantly improved recognition rates in the context of key generation. Besides computing experimental error rates, we tailor a known key guessing approach specifically to the used key generation scheme and assess security provided by the resulting keys. We give a very conservative estimate of the number of guesses an adversary must make to find a correct key. Results show that the proposed key generation approach produces keys comparable to those obtained by similar methods based on other biometrics.

Wireless Sensor Network (WSN) consists of a numerous of small devices called sensor which has a limitation in resources such as low energy, memory, and computation. Sensors deployed in a harsh environment and vulnerable to various security issues and due to the resource restriction in a sensor, key management and provide robust security in this type of networks is a challenge. keys may be used in two ways in cryptography is symmetric or asymmetric, asymmetric is required more communication, memory, and computing when compared with symmetric, so it is not appropriate for WSN. In this paper, key management scheme based on symmetric keys has been proposed where each node uses pseudo-random generator (PRNG)to generate key that is shared with base station based on pre-distributed initial key and CBC - RC5 to reached to confidently, integrity and authentication.

Memory disclosure vulnerabilities enable an adversary to successfully mount arbitrary code execution attacks against applications via so-called just-in-time code reuse attacks, even when those applications are fortified with fine-grained address space layout randomization. This attack paradigm requires the adversary to first read the contents of randomized application code, then construct a code reuse payload using that knowledge. In this paper, we show that the recently proposed Execute-no-Read (XnR) technique fails to prevent just-in-time code reuse attacks. Next, we introduce the design and implementation of a novel memory permission primitive, dubbed No-Execute-After-Read (near), that foregoes the problems of XnR and provides strong security guarantees against just-in-time attacks in commodity binaries. Specifically, near allows all code to be disclosed, but prevents any disclosed code from subsequently being executed, thus thwarting just-in-time code reuse. At the same time, commodity binaries with mixed code and data regions still operate correctly, as legitimate data is still readable. To demonstrate the practicality and portability of our approach we implemented prototypes for both Linux and Android on the ARMv8 architecture, as well as a prototype that protects unmodified Microsoft Windows executables and dynamically linked libraries. In addition, our evaluation on the SPEC2006 benchmark demonstrates that our prototype has negligible runtime overhead, making it suitable for practical deployment.

There are currently no requirements (technical or otherwise) that routing paths must be contained within national boundaries. Indeed, some paths experience international detours, i.e., originate in one country, cross international boundaries and return to the same country. In most cases these are sensible traffic engineering or peering decisions at ISPs that serve multiple countries. In some cases such detours may be suspicious. Characterizing international detours is useful to a number of players: (a) network engineers trying to diagnose persistent problems, (b) policy makers aiming at adhering to certain national communication policies, (c) entrepreneurs looking for opportunities to deploy new networks, or (d) privacy-conscious states trying to minimize the amount of internal communication traversing different jurisdictions. In this paper we characterize international detours in the Internet during the month of January 2016. To detect detours we sample BGP RIBs every 8 hours from 461 RouteViews and RIPE RIS peers spanning 30 countries. We use geolocation of ASes which geolocates each BGP prefix announced by each AS, mapping its presence at IXPs and geolocation infrastructure IPs. Finally, we analyze each global BGP RIB entry looking for detours. Our analysis shows more than 5K unique BGP prefixes experienced a detour. 132 prefixes experienced more than 50% of the detours. We observe about 544K detours. Detours either last for a few days or persist the entire month. Out of all the detours, more than 90% were transient detours that lasted for 72 hours or less. We also show different countries experience different characteristics of detours.

One of the most challenging issues facing academic conferences and educational institutions today is plagiarism detection. Typically, these entities wish to ensure that the work products submitted to them have not been plagiarized from another source (e.g., authors submitting identical papers to multiple journals). Assembling large centralized databases of documents dramatically improves the effectiveness of plagiarism detection techniques, but introduces a number of privacy and legal issues: all document contents must be completely revealed to the database operator, making it an attractive target for abuse or attack. Moreover, this content aggregation involves the disclosure of potentially sensitive private content, and in some cases this disclosure may be prohibited by law. In this work, we introduce Elxa, the first scalable centralized plagiarism detection system that protects the privacy of the submissions. Elxa incorporates techniques from the current state of the art in plagiarism detection, as evaluated by the information retrieval community. Our system is designed to be operated on existing cloud computing infrastructure, and to provide incentives for the untrusted database operator to maintain the availability of the network. Elxa can be used to detect plagiarism in student work, duplicate paper submissions (and their associated peer reviews), similarities between confidential reports (e.g., malware summaries), or any approximate text reuse within a network of private documents. We implement a prototype using the Hadoop MapReduce framework, and demonstrate that it is feasible to achieve competitive detection effectiveness in the private setting.

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a trusted authority. Its network is formed from resources contributed by individuals known as miners. Users of Bitcoin currency create transactions that are stored in a specialised data structure called a block chain. Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners. These miners have to be synchronised with any update in the network, which produces high data traffic rates. Despite advances in mobile technology, no cryptocurrencies have been proposed for mobile devices. This is largely due to the lower processing capabilities of mobile devices when compared with conventional computers and the poorer Internet connectivity to that of the wired networking. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders. It is based on opportunistic networking rather than relying on infrastructure and incorporates characteristics of mobile networks such as users' locations and their coverage radius in order to employ an alternative proof-of-work scheme. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed block chain.

Friends, family and colleagues at work may repeatedly observe how their peers unlock their smartphones. These "insiders" may combine multiple partial observations to form a hypothesis of a target's secret. This changing landscape requires that we update the methods used to assess the security of unlocking mechanisms against human shoulder surfing attacks. In our paper, we introduce a methodology to study shoulder surfing risks in the insider threat model. Our methodology dissects the authentication process into minimal observations by humans. Further processing is based on simulations. The outcome is an estimate of the number of observations needed to break a mechanism. The flexibility of this approach benefits the design of new mechanisms. We demonstrate the application of our methodology by performing an analysis of the SwiPIN scheme published at CHI 2015. Our results indicate that SwiPIN can be defeated reliably by a majority of the population with as few as 6 to 11 observations.

Path prediction on the Internet has been a topic of research in the networking community for close to a decade. Applications of path prediction solutions have ranged from optimizing selection of peers in peer- to-peer networks to improving and debugging CDN predictions. Recently, revelations of traffic correlation and surveillance on the Internet have raised the topic of path prediction in the context of network security. Specifically, predicting network paths can allow us to identify and avoid given organizations on network paths (e.g., to avoid traffic correlation attacks in Tor) or to infer the impact of hijacks and interceptions when direct measurements are not available. In this poster we propose the design and implementation of PathCache which aims to reuse measurement data to estimate AS level paths on the Internet. Unlike similar systems, PathCache does not assume that routing on the Internet is destination based. Instead, we develop an algorithm to compute confidence in paths between ASes. These multiple paths ranked by their confidence values are returned to the user.

Resource discovery in unstructured peer-to-peer networks causes a search query to be flooded throughout the network via random nodes, leading to security and privacy issues. The owner of the search query does not have control over the transmission of its query through the network. Although algorithms have been proposed for policy-compliant query or data routing in a network, these algorithms mainly deal with authentic route computation and do not provide mechanisms to actually verify the network paths taken by the query. In this work, we propose an approach to deal with the problem of verifying network paths taken by a search query during resource discovery, and detection of malicious forwarding of search query. Our approach aims at being secure and yet very scalable, even in the presence of huge number of nodes in the network.

WebRTC is one of the latest additions to the ever growing repository of Web browser technologies, which push the envelope of native Web application capabilities. WebRTC allows real-time peer-to-peer audio and video chat, that runs purely in the browser. Unlike existing video chat solutions, such as Skype, that operate in a closed identity ecosystem, WebRTC was designed to be highly flexible, especially in the domains of signaling and identity federation. This flexibility, however, opens avenues for identity fraud. In this paper, we explore the technical underpinnings of WebRTC's identity management architecture. Based on this analysis, we identify three novel attacks against endpoint authenticity. To answer the identified threats, we propose and discuss defensive strategies, including security improvements for the WebRTC specifications and mitigation techniques for the identity and service providers.

Aliasing is a known source of challenges in the context of imperative object-oriented languages, which have led to important advances in type systems for aliasing control. However, their large-scale adoption has turned out to be a surprisingly difficult challenge. While new language designs show promise, they do not address the need of aliasing control in existing languages. This paper presents a new approach to isolation and uniqueness in an existing, widely-used language, Scala. The approach is unique in the way it addresses some of the most important obstacles to the adoption of type system extensions for aliasing control. First, adaptation of existing code requires only a minimal set of annotations. Only a single bit of information is required per class. Surprisingly, the paper shows that this information can be provided by the object-capability discipline, widely-used in program security. We formalize our approach as a type system and prove key soundness theorems. The type system is implemented for the full Scala language, providing, for the first time, a sound integration with Scala's local type inference. Finally, we empirically evaluate the conformity of existing Scala open-source code on a corpus of over 75,000 LOC.

This paper outlines a set of 10 cyber security concerns associated with Industrial Control Systems (ICS). The concerns address software and hardware development, implementation, and maintenance practices, supply chain assurance, the need for cyber forensics in ICS, a lack of awareness and training, and finally, a need for test beds which can be used to address the first 9 cited concerns. The concerns documented in this paper were developed based on the authors' combined experience conducting research in this field for the US Department of Homeland Security, the National Science Foundation, and the Department of Defense. The second half of this paper documents a virtual test bed platform which is offered as a tool to address the concerns listed in the first half of the paper. The paper discusses various types of test beds proposed in literature for ICS research, provides an overview of the virtual test bed platform developed by the authors, and lists future works required to extend the existing test beds to serve as a development platform.

A major component of modern vehicles is the infotainment system, which interfaces with its drivers and passengers. Other mobile devices, such as handheld phones and laptops, can relay information to the embedded infotainment system through Bluetooth and vehicle WiFi. The ability to extract information from these systems would help forensic analysts determine the general contents that is stored in an infotainment system. Based off the data that is extracted, this would help determine what stored information is relevant to law enforcement agencies and what information is non-essential when it comes to solving criminal activities relating to the vehicle itself. This would overall solidify the Intelligent Transport System and Vehicular Ad Hoc Network infrastructure in combating crime through the use of vehicle forensics. Additionally, determining the content of these systems will allow forensic analysts to know if they can determine anything about the end-user directly and/or indirectly.

Modern OS kernels including Windows, Linux, and Mac OS all have adopted kernel Address Space Layout Randomization (ASLR), which shifts the base address of kernel code and data into different locations in different runs. Consequently, when performing introspection or forensic analysis of kernel memory, we cannot use any pre-determined addresses to interpret the kernel events. Instead, we must derandomize the address space layout and use the new addresses. However, few efforts have been made to derandomize the kernel address space and yet there are many questions left such as which approach is more efficient and robust. Therefore, we present the first systematic study of how to derandomize a kernel when given a memory snapshot of a running kernel instance. Unlike the derandomization approaches used in traditional memory exploits in which only remote access is available, with introspection and forensics applications, we can use all the information available in kernel memory to generate signatures and derandomize the ASLR. In other words, there exists a large volume of solutions for this problem. As such, in this paper we examine a number of typical approaches to generate strong signatures from both kernel code and data based on the insight of how kernel code and data is updated, and compare them from efficiency (in terms of simplicity, speed etc.) and robustness (e.g., whether the approach is hard to be evaded or forged) perspective. In particular, we have designed four approaches including brute-force code scanning, patched code signature generation, unpatched code signature generation, and read-only pointer based approach, according to the intrinsic behavior of kernel code and data with respect to kernel ASLR. We have gained encouraging results for each of these approaches and the corresponding experimental results are reported in this paper.

Social Networking is fundamentally shifting the way we communicate, sharing idea and form opinions. All people try to use social media for there need, people from every age group are involved in social media site or e-commerce site. Nowadays almost every illegal activity is happened using the social network and instant messages. It means that present system is not capable to found all suspicious words. In this paper, we provided a brief description of problem and review on the different framework developed so far. Propose a better system which can be indentify criminal activity through social networking more efficiently. Use Ontology Based Information Extraction (OBIE) technique to identify domain of word and Association Rule mining to generate rules. Heuristic method checks in user database for malicious users according to predefine elements and Naïve Bayes method is use to identify the context behind the message or post. The experimental result is used for further action on victim by cyber crime department.

With the outgrowth of video editing tools, video information trustworthiness becomes a hypersensitive field. Today many devices have the capability of capturing digital videos such as CCTV, digital cameras and mobile phones and these videos may transmitted over the Internet or any other non secure channel. As digital video can be used to as supporting evidence, it has to be protected against manipulation or tampering. As most video authentication techniques are based on watermarking and digital signatures, these techniques are effectively used in copyright purposes but difficult to implement in other cases such as video surveillance or in videos captured by consumer's cameras. In this paper we propose an intelligent technique for video authentication which uses the video local information which makes it useful for real world applications. The proposed algorithm relies on the video's statistical local information which was applied on a dataset of videos captured by a range of consumer video cameras. The results show that the proposed algorithm has potential to be a reliable intelligent technique in digital video authentication without the need to use for SVM classifier which makes it faster and less computationally expensive in comparing with other intelligent techniques.

Intrusive multi-step attacks, such as Advanced Persistent Threat (APT) attacks, have plagued enterprises with significant financial losses and are the top reason for enterprises to increase their security budgets. Since these attacks are sophisticated and stealthy, they can remain undetected for years if individual steps are buried in background "noise." Thus, enterprises are seeking solutions to "connect the suspicious dots" across multiple activities. This requires ubiquitous system auditing for long periods of time, which in turn causes overwhelmingly large amount of system audit events. Given a limited system budget, how to efficiently handle ever-increasing system audit logs is a great challenge. This paper proposes a new approach that exploits the dependency among system events to reduce the number of log entries while still supporting high-quality forensic analysis. In particular, we first propose an aggregation algorithm that preserves the dependency of events during data reduction to ensure the high quality of forensic analysis. Then we propose an aggressive reduction algorithm and exploit domain knowledge for further data reduction. To validate the efficacy of our proposed approach, we conduct a comprehensive evaluation on real-world auditing systems using log traces of more than one month. Our evaluation results demonstrate that our approach can significantly reduce the size of system logs and improve the efficiency of forensic analysis without losing accuracy.

We address the known problem of detecting a previous compression in JPEG images, focusing on the challenging case of high and very high quality factors (textgreater= 90) as well as repeated compression with identical or nearly identical quality factors. We first revisit the approaches based on Benford–Fourier analysis in the DCT domain and block convergence analysis in the spatial domain. Both were originally conceived for specific scenarios. Leveraging decision tree theory, we design a combined approach complementing the discriminatory capabilities. We obtain a set of novel detectors targeted to high quality grayscale JPEG images.

There has been growing interest in using convolutional neural networks (CNNs) in the fields of image forensics and steganalysis, and some promising results have been reported recently. These works mainly focus on the architectural design of CNNs, usually, a single CNN model is trained and then tested in experiments. It is known that, neural networks, including CNNs, are suitable to form ensembles. From this perspective, in this paper, we employ CNNs as base learners and test several different ensemble strategies. In our study, at first, a recently proposed CNN architecture is adopted to build a group of CNNs, each of them is trained on a random subsample of the training dataset. The output probabilities, or some intermediate feature representations, of each CNN, are then extracted from the original data and pooled together to form new features ready for the second level of classification. To make best use of the trained CNN models, we manage to partially recover the lost information due to spatial subsampling in the pooling layers when forming feature vectors. Performance of the ensemble methods are evaluated on BOSSbase by detecting S-UNIWARD at 0.4 bpp embedding rate. Results have indicated that both the recovery of the lost information, and learning from intermediate representation in CNNs instead of output probabilities, have led to performance improvement.