Biblio

This paper proposes a forensic method for identifying whether an image was previously compressed by JPEG and also proposes an improved anti-forensics method to enhance the quality of noise added image. Stamm and Liu's anti-forensics method disable the detection capabilities of various forensics methods proposed in the literature, used for identifying the compressed images. However, it also degrades the quality of the image. First, we analyze the anti-forensics method and then use the decimal histogram of the coefficients to distinguish the never compressed images from the previously compressed; even the compressed image processed anti-forensically. After analyzing the noise distribution in the AF image, we propose a method to remove the Gaussian noise caused by image dithering which in turn enhances the image quality. The paper is organized in the following manner: Section I is the introduction, containing previous literature. Section II briefs Anti-forensic method proposed by Stamm et al. In section III, we have proposed a forensic approach and section IV comprises of improved anti-forensic approach. Section V covers details of experimentation followed by the conclusion.

The usage of Information and Communication Technologies (ICTs) pervades everyday's life. If it is true that ICT contributed to improve the quality of our life, it is also true that new forms of (cyber)crime have emerged in this setting. The diversity and amount of information forensic investigators need to cope with, when tackling a cyber-crime case, call for tools and techniques where knowledge is the main actor. Current approaches leave to the investigator the chore of integrating the diverse sources of evidence relevant for a case thus hindering the automatic generation of reusable knowledge. This paper describes an architecture that lifts the classical phases of a digital forensic investigation to a knowledge-driven setting. We discuss how the usage of languages and technologies originating from the Semantic Web proposal can complement digital forensics tools so that knowledge becomes a first-class citizen. Our architecture enables to perform in an integrated way complex forensic investigations and, as a by-product, build a knowledge base that can be consulted to gain insights from previous cases. Our proposal has been inspired by real-world scenarios emerging in the context of an Italian research project about cyber security.

Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature. We introduce a new similarity function for heterogeneous graphs that compares two graphs based on their relative frequency of local substructures, represented as short strings. This function lends itself to a vector representation of a graph, which is (a) fast to compute, and (b) amenable to a sketched version with bounded size that preserves similarity. StreamSpot exhibits desirable properties that a streaming application requires: it is (i) fully-streaming; processing the stream one edge at a time as it arrives, (ii) memory-efficient; requiring constant space for the sketches and the clustering, (iii) fast; taking constant time to update the graph sketches and the cluster summaries that can process over 100,000 edges per second, and (iv) online; scoring and flagging anomalies in real time. Experiments on datasets containing simulated system-call flow graphs from normal browser activity and various attack scenarios (ground truth) show that StreamSpot is high-performance; achieving above 95% detection accuracy with small delay, as well as competitive time and memory usage.  

Internet Service Providers (ISPs) use the Border Gateway Protocol (BGP) to announce and exchange routes for de- livering packets through the internet. ISPs must carefully configure their BGP routers to ensure traffic is routed reli- ably and securely. Correctly configuring BGP routers has proven challenging in practice, and misconfiguration has led to worldwide outages and traffic hijacks. This paper presents Bagpipe, a system that enables ISPs to declaratively express BGP policies and that automatically verifies that router configurations implement such policies. The novel initial network reduction soundly reduces policy verification to a search for counterexamples in a finite space. An SMT-based symbolic execution engine performs this search efficiently. Bagpipe reduces the size of its search space using predicate abstraction and parallelizes its search using symbolic variable hoisting. Bagpipe's policy specification language is expressive: we expressed policies inferred from real AS configurations, policies from the literature, and policies for 10 Juniper TechLibrary configuration scenarios. Bagpipe is efficient: we ran it on three ASes with a total of over 240,000 lines of Cisco and Juniper BGP configuration. Bagpipe is effective: it revealed 19 policy violations without issuing any false positives.

Outlier detection is a fundamental data science task with applications ranging from data cleaning to network security. Recently, a new class of outlier detection algorithms has emerged, called contextual outlier detection, and has shown improved performance when studying anomalous behavior in a specific context. However, as we point out in this article, such approaches have limited applicability in situations where the context is sparse (i.e., lacking a suitable frame of reference). Moreover, approaches developed to date do not scale to large datasets. To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral model based on the relevant contexts, which is then integrated in a natural and robust fashion. We run ROCOD on both synthetic and real-world datasets and demonstrate that it outperforms other competitive baselines on the axes of efficacy and efficiency. We also drill down and perform a fine-grained analysis to shed light on the rationale for the performance gains of ROCOD and reveal its effectiveness when handling objects with sparse contexts.

We propose the first verifier-local revocation scheme for privacy-enhancing attribute-based credentials (PABCs) that is practically usable in large-scale applications, such as national eID cards, public transportation and physical access control systems. By using our revocation scheme together with existing PABCs, it is possible to prove attribute ownership in constant time and verify the proof and the revocation status in the time logarithmic in the number of revoked users, independently of the number of all valid users in the system. Proofs can be efficiently generated using only offline constrained devices, such as existing smart-cards. These features are achieved by using a new construction called \$n\$-times unlinkable proofs. We show the full cryptographic description of the scheme, prove its security, discuss parameters influencing scalability and provide details on implementation aspects. As a side result of independent interest, we design a more efficient proof of knowledge of weak Boneh-Boyen signatures, that does not require any pairing computation on the prover side.

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.

Cloud computing has been a great enabler for both the Internet of Things and Big Data. However, as with all new computing developments, development of the technology is usually much faster than consideration for, and development of, solutions for security and privacy. In a previous paper, we proposed that a unikernel solution could be used to improve security and privacy in a cloud scenario. In this paper, we outline how we might apply this approach to the Internet of Things, which can demonstrate an improvement over existing approaches.

Role-based Access Control (RBAC) is a popular solution for implementing information security however there is no pervasive methodology used to produce scalable access control systems for large organizations with hundreds or thousands of employees. As a result ten engineers will likely arrive at ten different solutions to the same problem where there is no right or wrong answer but there is both an immediate and long term cost. Moreover, they would have difficulty communicating the important aspects of their design implementations to each other. This is an interesting deficiency because despite their diversity, large organizations are built upon two key concepts, roles and responsibilities, where a role like Departmental Chair is identified and assigned responsibilities. In this paper, our objective is to introduce ORGODEX, a new model and practical methodology for engineering scalable RBAC systems in large organizations where employees require access to information on a need to know basis. First, we motivate the requirement for a new RBAC dichotomy, distinguishing between roles and responsibilities. Next, we introduce our new model for describing and reasoning about RBAC systems with this new dichotomy. Finally, we produce a new iterative methodology for engineering scalable access control systems.

Because of rampant security breaches in IoT devices, searching vulnerabilities in massive IoT ecosystems is more crucial than ever. Recent studies have demonstrated that control-flow graph (CFG) based bug search techniques can be effective and accurate in IoT devices across different architectures. However, these CFG-based bug search approaches are far from being scalable to handle an enormous amount of IoT devices in the wild, due to their expensive graph matching overhead. Inspired by rich experience in image and video search, we propose a new bug search scheme which addresses the scalability challenge in existing cross-platform bug search techniques and further improves search accuracy. Unlike existing techniques that directly conduct searches based upon raw features (CFGs) from the binary code, we convert the CFGs into high-level numeric feature vectors. Compared with the CFG feature, high-level numeric feature vectors are more robust to code variation across different architectures, and can easily achieve realtime search by using state-of-the-art hashing techniques. We have implemented a bug search engine, Genius, and compared it with state-of-art bug search approaches. Experimental results show that Genius outperforms baseline approaches for various query loads in terms of speed and accuracy. We also evaluated Genius on a real-world dataset of 33,045 devices which was collected from public sources and our system. The experiment showed that Genius can finish a search within 1 second on average when performed over 8,126 firmware images of 420,558,702 functions. By only looking at the top 50 candidates in the search result, we found 38 potentially vulnerable firmware images across 5 vendors, and confirmed 23 of them by our manual analysis. We also found that it took only 0.1 seconds on average to finish searching for all 154 vulnerabilities in two latest commercial firmware images from D-LINK. 103 of them are potentially vulnerable in these images, and 16 of them were confirmed.

MPI includes all processes in MPI\_COMM\_WORLD; this is untenable for reasons of scale, resiliency, and overhead. This paper offers a new approach, extending MPI with a new concept called Sessions, which makes two key contributions: a tighter integration with the underlying runtime system; and a scalable route to communication groups. This is a fundamental change in how we organise and address MPI processes that removes well-known scalability barriers by no longer requiring the global communicator MPI\_COMM\_WORLD.

Funded under the European Union's Horizon 2020 research and innovation programme, SAFEcrypto will provide a new generation of practical, robust and physically secure post-quantum cryptographic solutions that ensure long-term security for future ICT systems, services and applications. The project will focus on the remarkably versatile field of Lattice-based cryptography as the source of computational hardness, and will deliver optimised public key security primitives for digital signatures and authentication, as well identity based encryption (IBE) and attribute based encryption (ABE). This will involve algorithmic and design optimisations, and implementations of lattice-based cryptographic schemes addressing cost, energy consumption, performance and physical robustness. As the National Institute of Standards and Technology (NIST) prepares for the transition to a post-quantum cryptographic suite B, urging organisations that build systems and infrastructures that require long-term security to consider this transition in architectural designs; the SAFEcrypto project will provide Proof-of-concept demonstrators of schemes for three practical real-world case studies with long-term security requirements, in the application areas of satellite communications, network security and cloud. The goal is to affirm Lattice-based cryptography as an effective replacement for traditional number-theoretic public-key cryptography, by demonstrating that it can address the needs of resource-constrained embedded applications, such as mobile and battery-operated devices, and of real-time high performance applications for cloud and network management infrastructures.

In threshold schemes one represents each sensitive variable by a number n of shares such that their (usually) bitwise sum equals that variable. These shares are initially generated in such a way that any subset of n-1 shares gives no information about the sensitive variable. Functions (S-boxes, mixing layers, round functions, etc.) are computed on the shares of the inputs resulting in the output as a number of shares. An essential property of a threshold implementation of a function is that each output share is computed from at most n-1 input shares. This is called incompleteness and guarantees that that computation cannot leak information about sensitive variables. The resulting output is then typically subject to some further computation, again in the form of separate, incomplete, computation on shares. For these subsequent computations to not leak information about the sensitive variables, the output of the previous stage must still be uniform. Hence, in an iterative cryptographic primitive such as a block cipher, we need a threshold implementation of the round function that yields a uniformly shared output if its input is uniformly shared. This property of the threshold implementation is called uniformity. Threshold schemes form a good protection mechanism against differential power analysis (DPA). In particular, using it allows building cryptographic hardware that is guaranteed to be unattackable with first-order DPA, assuming certain leakage models of the cryptographic hardware at hand and for a plausible definition of "first order". Constructing an incomplete threshold implementation of a non-linear function is rather straightforward. To offer resistance against first-order DPA, the number of shares equals the algebraic degree of the function plus one. However, constructing one that is at the same time incomplete and uniform may present a challenge. For instance, for the Keccak non-linear layer, incomplete 3-share threshold implementations are easy to generate but no uniform one is known. Exhaustive investigations have been performed on all small S-boxes (3 to 5 bits) and there are many S-boxes for which it is not known to build uniform threshold implementations with d+1 shares if their algebraic degree is d. Uniformity of a threshold implementation is essential in its information-theoretical proof of resistance against first-order DPA. However, given a non-uniform threshold implementation, it is not immediate how to exploit its non-uniformity in an attack. In my talk I discuss the local and global effects of non-uniformity in iterated functions and their significance on the resistance against DPA. I treat methods to quantitatively limit the amount of non-uniformity and to keep it away from where it may be harmful. These techniques are relatively cheap and can reduce non-uniformity to such a low level that it would require an astronomical amount of samples to measure it.

In order to retrieve the secret key in a side-channel attack, the attacker computes distinguisher values using all the available data. A profiling stage is very useful to provide some a priori information about the leakage model. However, profiling is essentially empirical and may not be exhaustive. Therefore, during the attack, the attacker may come up on previously unseen data, which can be troublesome. A lazy workaround is to ignore all such novel observations altogether. In this paper, we show that this is not optimal and can be avoided. Our proposed techniques eventually improve the performance of classical information-theoretic distinguishers in terms of success rate.

A private information retrieval (abbreviated as PIR) protocol deals with the schemes that allow a user to retrieve privately an element of a non-replicated database. The security of PIR protocol is that the user wants to retrieve information in a database without the database knowing which information has being retrieved. This is widely applied in medical files, video or songs databases or even stock exchanges share prices. At ISIT 2008, Carlos Aguilar Melchor and Philippe Gaborit presented a lattice-based PIR protocol, whose security based on problems close to coding theory problems known to be NP-complete. In this paper, we present a practical attack on this PIR protocol when the number of elements in the database is not big. More specifically, we can firstly uncover the hidden linear relationship between the public matrices and noisy matrices, and then propose an efficient dimension-reduced attack to locate the index of the element which the user retrieved.

Cloud computing platforms are becoming increasingly prevalent and readily available nowadays, providing us alternative and economic services for resource-constrained clients to perform large-scale computation. In this work, we address the problem of secure outsourcing of large-scale nonnegative matrix factorization (NMF) to a cloud in a way that the client can verify the correctness of results with small overhead. The input matrix protection is achieved by a lightweight, permutation-based encryption mechanism. By exploiting the iterative nature of NMF computation, we propose a single-round verification strategy, which can be proved to be effective. Both theoretical and experimental results are given to demonstrate the superior performance of our scheme.

Pseudo-random number generators (PRNGs) are a critical infrastructure for cryptography and security of many computer applications. At the same time, PRNGs are surprisingly difficult to design, implement, and debug. This paper presents the first static analysis technique specifically for quality assurance of cryptographic PRNG implementations. The analysis targets a particular kind of implementation defect, the entropy loss. Entropy loss occurs when the entropy contained in the PRNG seed is not utilized to the full extent for generating the pseudo-random output stream. The Debian OpenSSL disaster, probably the most prominent PRNG-related security incident, was one but not the only manifestation of such a defect. Together with the static analysis technique, we present its implementation, a tool named Entroposcope. The tool offers a high degree of automation and practicality. We have applied the tool to five real-world PRNGs of different designs and show that it effectively detects both known and previously unknown instances of entropy loss.

We consider the task of secure multi-party computation of arithmetic circuits over a finite field. Unlike Boolean circuits, arithmetic circuits allow natural computations on integers to be expressed easily and efficiently. In the strongest setting of malicious security with a dishonest majority –- where any number of parties may deviate arbitrarily from the protocol –- most existing protocols require expensive public-key cryptography for each multiplication in the preprocessing stage of the protocol, which leads to a high total cost. We present a new protocol that overcomes this limitation by using oblivious transfer to perform secure multiplications in general finite fields with reduced communication and computation. Our protocol is based on an arithmetic view of oblivious transfer, with careful consistency checks and other techniques to obtain malicious security at a cost of less than 6 times that of semi-honest security. We describe a highly optimized implementation together with experimental results for up to five parties. By making extensive use of parallelism and SSE instructions, we improve upon previous runtimes for MPC over arithmetic circuits by more than 200 times.

We present a unified framework for studying secure multiparty computation (MPC) with arbitrarily restricted interaction patterns such as a chain, a star, a directed tree, or a directed graph. Our study generalizes both standard MPC and recent models for MPC with specific restricted interaction patterns, such as those studied by Halevi et al. (Crypto 2011), Goldwasser et al. (Eurocrypt 2014), and Beimel et al. (Crypto 2014). Since restricted interaction patterns cannot always yield full security for MPC, we start by formalizing the notion of "best possible security" for any interaction pattern. We then obtain the following main results: Completeness theorem. We prove that the star interaction pattern is complete for the problem of MPC with general interaction patterns. Positive results. We present both information-theoretic and computationally secure protocols for computing arbitrary functions with general interaction patterns. We also present more efficient protocols for computing symmetric functions, both in the computational and in the information-theoretic setting. Our computationally secure protocols for general functions necessarily rely on indistinguishability obfuscation while the ones for computing symmetric functions make simple use of multilinear maps. Negative results. We show that, in many cases, the complexity of our information-theoretic protocols is essentially the best that can be achieved. All of our protocols rely on a correlated randomness setup, which is necessary in our setting (for computing general functions). In the computational case, we also present a generic procedure to make any correlated randomness setup reusable, in the common random string model. Although most of our information-theoretic protocols have exponential complexity, they may be practical for functions on small domains (e.g., f0; 1g20), where they are concretely faster than their computational counterparts.

In this work we propose a model for conducting efficient and mutually beneficial information sharing between two competing entities, focusing specifically on software vulnerability sharing. We extend the two-stage game-theoretic model proposed by Khouzani et al. [18] for bug sharing, addressing two key features: we allow security information to be associated with different categories and severities, but also remove a large proportion of player homogeneity assumptions the previous work makes. We then analyse how these added degrees of realism affect the trading dynamics of the game. Secondly, we develop a new private set operation (PSO) protocol that enables the removal of the trusted mediation requirement. The PSO functionality allows for bilateral trading between the two entities up to a mutually agreed threshold on the value of information shared, keeping all other input information secret. The protocol scales linearly with set sizes and we give an implementation that establishes the practicality of the design for varying input parameters. The resulting model and protocol provide a framework for practical and secure information sharing between competing entities.

We propose a game theoretic framework for task allocation in mobile cloud computing that corresponds to offloading of compute tasks to a group of nearby mobile devices. Specifically, in our framework, a distributor node holds a multidimensional auction for allocating the tasks of a job among nearby mobile nodes based on their computational capabilities and also the cost of computation at these nodes, with the goal of reducing the overall job completion time. Our proposed auction also has the desired incentive compatibility property that ensures that mobile devices truthfully reveal their capabilities and costs and that those devices benefit from the task allocation. To deal with node mobility, we perform multiple auctions over adaptive time intervals. We develop a heuristic approach to dynamically find the best time intervals between auctions to minimize unnecessary auctions and the accompanying overheads. We evaluate our framework and methods using both real world and synthetic mobility traces. Our evaluation results show that our game theoretic framework improves the job completion time by a factor of 2-5 in comparison to the time taken for executing the job locally, while minimizing the number of auctions and the accompanying overheads. Our approach is also profitable for the nearby nodes that execute the distributor's tasks with these nodes receiving a compensation higher than their actual costs.

As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, manually analyzing these collected data to detect important cyber security events become impossible. Hence, data mining techniques are becoming an essential tool for real-world cyber security applications. For example, a report from Gartner [gartner12] claims that "Information security is becoming a big data analytics problem, where massive amounts of data will be correlated, analyzed and mined for meaningful patterns". Of course, data mining/analytics is a means to an end where the ultimate goal is to provide cyber security analysts with prioritized actionable insights derived from big data. This raises the question, can we directly apply existing techniques to cyber security applications? One of the most important differences between data mining for cyber security and many other data mining applications is the existence of malicious adversaries that continuously adapt their behavior to hide their actions and to make the data mining models ineffective. Unfortunately, traditional data mining techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the data miner's reactions, and data mining algorithms constructed based on a training dataset degrades quickly. To address these concerns, over the last couple of years new and novel data mining techniques which is more resilient to such adversarial behavior are being developed in machine learning and data mining community. We believe that lessons learned as a part of this research direction would be beneficial for cyber security researchers who are increasingly applying machine learning and data mining techniques in practice. To give an overview of recent developments in adversarial data mining, in this three hour long tutorial, we introduce the foundations, the techniques, and the applications of adversarial data mining to cyber security applications. We first introduce various approaches proposed in the past to defend against active adversaries, such as a minimax approach to minimize the worst case error through a zero-sum game. We then discuss a game theoretic framework to model the sequential actions of the adversary and the data miner, while both parties try to maximize their utilities. We also introduce a modified support vector machine method and a relevance vector machine method to defend against active adversaries. Intrusion detection and malware detection are two important application areas for adversarial data mining models that will be discussed in details during the tutorial. Finally, we discuss some practical guidelines on how to use adversarial data mining ideas in generic cyber security applications and how to leverage existing big data management tools for building data mining algorithms for cyber security.

Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to incorporate knowledge of potential adversarial data manipulations into the learning algorithm. Despite these techniques have been shown to be effective in some adversarial learning tasks, their adoption in practice is hindered by different factors, including the difficulty of meeting specific theoretical requirements, the complexity of implementation, and scalability issues, in terms of computational time and space required during training. In this work, we aim to develop secure kernel machines against evasion attacks that are not computationally more demanding than their non-secure counterparts. In particular, leveraging recent work on robustness and regularization, we show that the security of a linear classifier can be drastically improved by selecting a proper regularizer, depending on the kind of evasion attack, as well as unbalancing the cost of classification errors. We then discuss the security of nonlinear kernel machines, and show that a proper choice of the kernel function is crucial. We also show that unbalancing the cost of classification errors and varying some kernel parameters can further improve classifier security, yielding decision functions that better enclose the legitimate data. Our results on spam and PDF malware detection corroborate our analysis.

Bitcoin provides two incentives for miners: block rewards and transaction fees. The former accounts for the vast majority of miner revenues at the beginning of the system, but it is expected to transition to the latter as the block rewards dwindle. There has been an implicit belief that whether miners are paid by block rewards or transaction fees does not affect the security of the block chain. We show that this is not the case. Our key insight is that with only transaction fees, the variance of the block reward is very high due to the exponentially distributed block arrival time, and it becomes attractive to fork a "wealthy" block to "steal" the rewards therein. We show that this results in an equilibrium with undesirable properties for Bitcoin's security and performance, and even non-equilibria in some circumstances. We also revisit selfish mining and show that it can be made profitable for a miner with an arbitrarily low hash power share, and who is arbitrarily poorly connected within the network. Our results are derived from theoretical analysis and confirmed by a new Bitcoin mining simulator that may be of independent interest. We discuss the troubling implications of our results for Bitcoin's future security and draw lessons for the design of new cryptocurrencies.

Decoy routing is a promising new approach for censorship circumvention that relies on traffic re-direction by volunteer autonomous systems. Decoy routing is subject to a fundamental censorship attack, called routing around decoy (RAD), in which the censors re-route their clients' Internet traffic in order to evade decoy routing autonomous systems. Recently, there has been a heated debate in the community on the real-world feasibility of decoy routing in the presence of the RAD attack. Unfortunately, previous studies rely their analysis on heuristic-based mechanisms for decoy placement strategies as well as ad hoc strategies for the implementation of the RAD attack by the censors. In this paper, we perform the first systematic analysis of decoy routing in the presence of the RAD attack. We use game theory to model the interactions between decoy router deployers and the censors in various settings. Our game-theoretic analysis finds the optimal decoy placement strategies–-as opposed to heuristic-based placements–-in the presence of RAD censors who take their optimal censorship actions–-as opposed to some ad hoc implementation of RAD. That is, we investigate the best decoy placement given the best RAD censorship. We consider two business models for the real-world deployment of decoy routers: a central deployment that resembles that of Tor and a distributed deployment where autonomous systems individually decide on decoy deployment based on their economic interests. Through extensive simulation of Internet routes, we derive the optimal strategies in the two models for various censoring countries and under different assumptions about the budget and preferences of the censors and decoy deployers. We believe that our study is a significant step forward in understanding the practicality of the decoy routing circumvention approach.