Biblio

Knowing which part of a program processes which parts of an input can reveal the structure of the input as well as the structure of the program. In a URL textlesspretextgreaterhttp://www.example.com/path/textless/pretextgreater, for instance, the protocol textlesspretextgreaterhttptextless/pretextgreater, the host textlesspretextgreaterwww.example.comtextless/pretextgreater, and the path textlesspretextgreaterpathtextless/pretextgreater would be handled by different functions and stored in different variables. Given a set of sample inputs, we use dynamic tainting to trace the data flow of each input character, and aggregate those input fragments that would be handled by the same function into lexical and syntactical entities. The result is a context-free grammar that reflects valid input structure. In its evaluation, our AUTOGRAM prototype automatically produced readable and structurally accurate grammars for inputs like URLs, spreadsheets or configuration files. The resulting grammars not only allow simple reverse engineering of input formats, but can also directly serve as input for test generators.

We present sandbox mining, a technique to confine an application to resources accessed during automatic testing. Sandbox mining first explores software behavior by means of automatic test generation, and extracts the set of resources accessed during these tests. This set is then used as a sandbox, blocking access to resources not used during testing. The mined sandbox thus protects against behavior changes such as the activation of latent malware, infections, targeted attacks, or malicious updates. The use of test generation makes sandbox mining a fully automatic process that can be run by vendors and end users alike. Our BOXMATE prototype requires less than one hour to extract a sandbox from an Android app, with few to no confirmations required for frequently used functionality.

The security and typical attack behavior of Modbus/TCP industrial network communication protocol are analyzed. The data feature of traffic flow is extracted through the operation mode of the depth analysis abnormal behavior, and the intrusion detection method based on the support vector machine (SVM) is designed. The method analyzes the data characteristics of abnormal communication behavior, and constructs the feature input structure and detection system based on SVM algorithm by using the direct behavior feature selection and abnormal behavior pattern feature construction. The experimental results show that the method can effectively improve the detection rate of abnormal behavior, and enhance the safety protection function of industrial network.

Hardware security has emerged as an important topic in the wake of increasing threats on integrated circuits which include reverse engineering, intellectual property (IP) piracy and overbuilding. This paper explores obfuscation of circuits as a hardware security measure and specifically targets digital signal processing (DSP) circuits which are part of most modern systems. The idea of using desired and undesired modes to design obfuscated DSP functions is illustrated using the fast Fourier transform (FFT) as an example. The selection of a mode is dependent on a key input to the circuit. The system is said to work in its desired mode of operation only if the correct key is applied. Other undesired modes are built into the design to confuse an adversary. The approach to obfuscating the design involves control-flow modifications which alter the computations from the desired mode. We present simulation and synthesis results on a reconfigurable, 2-parallel FFT and discuss the security of this approach. It is shown that the proposed approach results in a reconfigurable and flexible design at an area overhead of 8% and a power overhead of 10%.

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.

Evolution in software systems is a necessary activity that occurs due to fixing bugs, adding functionality or improving system quality. Systems often need to be shown to comply with regulatory standards. Along with demonstrating compliance, an artifact, called an assurance case, is often produced to show that the system indeed satisfies the property imposed by the standard (e.g., safety, privacy, security, etc.). Since each of the system, the standard, and the assurance case can be presented as a model, we propose the extension and use of traditional model management operators to aid in the reuse of parts of the assurance case when the system undergoes an evolution. Specifically, we present a model management approach that eventually produces a partial evolved assurance case and guidelines to help the assurance engineer in completing it. We demonstrate how our approach works on an automotive subsystem regulated by the ISO 26262 standard.

An approach to analyzing the security of a cyber-physical system (CPS) is proposed, where the behavior of a physical plant and its controller are captured in approximate models, and their interaction is rigorously checked to discover potential attacks that involve a varying number of compromised sensors and actuators. As a preliminary study, this approach has been applied to a fully functional water treatment testbed constructed at the Singapore University of Technology and Design. The analysis revealed previously unknown attacks that were confirmed to pose serious threats to the safety of the testbed, and suggests a number of research challenges and opportunities for applying a similar type of formal analysis to cyber-physical security.

This paper presents an approach to a closed-class authorship attribution (AA) problem. It is based on language modeling for classification and called modified language modeling. Modified language modeling aims to offer a solution for AA problem by Combinations of both bigram words weighting and Unigram words weighting. It makes the relation between unseen text and training documents clearer with giving extra reward of training documents; training document including bigram word as well as unigram words. Moreover, IDF value multiplied by related word probability has been used, instead of removing stop words which are provided by Stop words list. we evaluate Experimental results by four approaches; unigram, bigram, trigram and modified language modeling by using two Persian poem corpora as WMPR-AA2016-A Dataset and WMPR-AA2016-B Dataset. Results show that modified language modeling attributes authors better than other approaches. The result on WMPR-AA2016-B, which is bigger dataset, is much better than another dataset for all approaches. This may indicate that if adequate data is provided to train language modeling the modified language modeling can be a good solution to AA problem.

In today's enterprise networks, there are many ways for a determined attacker to obtain a foothold, bypass current protection technologies, and attack the intended target. Over several years we have developed the Self-shielding Dynamic Network Architecture (SDNA) technology, which prevents an attacker from targeting, entering, or spreading through an enterprise network by adding dynamics that present a changing view of the network over space and time. SDNA was developed with the support of government sponsored research and development and corporate internal resources. The SDNA technology was purchased by Cryptonite, LLC in 2015 and has been developed into a robust product offering called Cryptonite NXT. In this paper, we describe the journey and lessons learned along the course of feasibility demonstration, technology development, security testing, productization, and deployment in a production network.

Distributed denial-of-service attacks are an increasing problem facing web applications, for which many defense techniques have been proposed, including several moving-target strategies. These strategies typically work by relocating targeted services over time, increasing uncertainty for the attacker, while trying not to disrupt legitimate users or incur excessive costs. Prior work has not shown, however, whether and how a rational defender would choose a moving-target method against an adaptive attacker, and under what conditions. We formulate a denial-of-service scenario as a two-player game, and solve a restricted-strategy version of the game using the methods of empirical game-theoretic analysis. Using agent-based simulation, we evaluate the performance of strategies from prior literature under a variety of attacks and environmental conditions. We find evidence for the strategic stability of various proposed strategies, such as proactive server movement, delayed attack timing, and suspected insider blocking, along with guidelines for when each is likely to be most effective.

The MP4 files has become to most used video media file available, and will mostly likely remain at the top for some time to come. This makes MP4 files an interesting candidate for steganography. With its size and structure, it offers a challenge to steganography developers. While some attempts have been made to create a truly covert file, few are as successful as Martin Fiedler's TCSteg. TCSteg allows users to hide a TrueCrypt hidden volume in an MP4 file. The structure of the file makes it difficult to identify that a volume exists. In our analysis of TCSteg, we will show how Fielder's code works and how we may be able to detect the existence of steganography. We will then implement these methods in hope that other steganography analysis can use them to determine if an MP4 file is a carrier file. Finally, we will address the future of MP4 steganography.

The popularity of Cloud computing has considerably increased during the last years. The increase of Cloud users and their interactions with the Cloud infrastructure raise the risk of resources faults. Such a problem can lead to a bad reputation of the Cloud environment which slows down the evolution of this technology. To address this issue, the dynamic and the complex architecture of the Cloud should be taken into account. Indeed, this architecture requires that resources protection and healing must be transparent and without external intervention. Unlike previous work, we suggest integrating the fundamental aspects of autonomic computing in the Cloud to deal with the self-healing of Cloud resources. Starting from the high degree of match between autonomic computing systems and multiagent systems, we propose to take advantage from the autonomous behaviour of agent technology to create an intelligent Cloud that supports autonomic aspects. Our proposed solution is a multi-agent system which interacts with the Cloud infrastructure to analyze the resources state and execute Checkpoint/Replication strategy or migration technique to solve the problem of failed resources.

A class of cyber-attacks called False Data Injection attacks that target measurement data used for state estimation in the power grid are currently under study by the research community. These attacks modify sensor readings obtained from meters with the aim of misleading the control center into taking ill-advised response action. It has been shown that an attacker with knowledge of the network topology can craft an attack that bypasses existing bad data detection schemes (largely based on residual generation) employed in the power grid. We propose a multi-agent system for detecting false data injection attacks against state estimation. The multi-agent system is composed of software implemented agents created for each substation. The agents facilitate the exchange of information including measurement data and state variables among substations. We demonstrate that the information exchanged among substations, even untrusted, enables agents cooperatively detect disparities between local state variables at the substation and global state variables computed by the state estimator. We show that a false data injection attack that passes bad data detection for the entire system does not pass bad data detection for each agent.

Online conversations, such as blogs, provide rich amount of information and opinions about popular queries. Given a query, traditional blog sites return a set of conversations often consisting of thousands of comments with complex thread structure. Since the interfaces of these blog sites do not provide any overview of the data, it becomes very difficult for the user to explore and analyze such a large amount of conversational data. In this paper, we present MultiConVis, a visual text analytics system designed to support the exploration of a collection of online conversations. Our system tightly integrates NLP techniques for topic modeling and sentiment analysis with information visualizations, by considering the unique characteristics of online conversations. The resulting interface supports the user exploration, starting from a possibly large set of conversations, then narrowing down to the subset of conversations, and eventually drilling-down to the set of comments of one conversation. Our evaluations through case studies with domain experts and a formal user study with regular blog readers illustrate the potential benefits of our approach, when compared to a traditional blog reading interface.

India being digitized through digital India, the most basic unique identity for each individual is biometrics. Since India is the second most populous nation, the database that has to be maintained is surplus. Shielding those information by using the present techniques has been questioned. This contravene problem can be overcome by using cryptographic algorithms in accumulation to biometrics. Hence proposed system is developed by combining multimodal biometric (Fingerprint, Retina, Finger vein) with cryptographic algorithm with Genuine Acceptance Rate of 94%, False Acceptance Rate of 1.46%, and False Rejection Rate of 1.07%.

Microfluidics is an interdisciplinary science focusing on the development of devices and systems that process low volumes of fluid for applications such as high throughput DNA sequencing, immunoassays, and entire Labs-on-Chip platforms. Microfluidic diagnostic technology enables these advances by facilitating the miniaturization and integration of complex biochemical processing through a microfluidic biochip [1]. This approach tightly couples the biochemical operations, sensing system, control algorithm, and droplet-based biochip. During the process the status of a droplet is monitored in real-time to detect operational errors. If an error has occurred, the control algorithm dynamically reconfigures to allow recovery and rescheduling of on-chip operations. During this recovery procedure the droplet that is the source of the error is discarded to prevent the propagation of the error and the operation is repeated. Threats to the operation of the microfluidics biochip include (1) integrity: an attack can modify control electrodes to corrupt the diagnosis, and (2) privacy: what can a user/operator deduce about the diagnosis? It is challenging to describe both these aspects using existing models; as Figure 1 depicts there are multiple security domains, Unidirectional information flows shown in black indicate undesirable flows, the bidirectional black arrows indicate desirable, but possibly corrupted, information flows, and the unidirectional red arrows indicate undesirable information flows. As with Stuxnet, a bidirectional, deducible information flow is needed between the monitoring security domain and internal security domain (biochip) [2]. Simultaneously, the attacker and the operators should receive a nondeducible information flow. Likewise, the red attack arrows should be deducible to the internal domain. Our current security research direction uses the novel approach of Multiple Security Domain Nondeducibility [2] to explore the vulnerabilities of exploiting this error recovery process through information flow leakages and leads to protection of the system through desirable information flows.

Security attacks often exploit flaws that are not anticipated in an abstract design, but are introduced inadvertently when high-level interactions in the design are mapped to low-level behaviors in the supporting platform. This paper proposes a multi-representational approach to security analysis, where models capturing distinct (but possibly overlapping) views of a system are automatically composed in order to enable an end-to-end analysis. This approach allows the designer to incrementally explore the impact of design decisions on security, and discover attacks that span multiple layers of the system. This paper describes Poirot, a prototype implementation of the approach, and reports on our experience on applying Poirot to detect previously unknown security flaws in publicly deployed systems.

Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the side-channel signals of 3D printer and then hack the IP information? To answer this, we perform an end-to-end study on exploring smartphone-based side-channel attacks against 3D printers. Specifically, we formulate the problem of the IP side-channel attack in 3D printing. Then, we investigate the possible acoustic and magnetic side-channel attacks using the smartphone built-in sensors. Moreover, we explore a magnetic-enhanced side-channel attack model to accurately deduce the vital directional operations of 3D printer. Experimental results show that by exploiting the side-channel signals collected by smartphones, we can successfully reconstruct the physical prints and their G-code with Mean Tendency Error of 5.87% on regular designs and 9.67% on complex designs, respectively. Our study demonstrates this new and practical smartphone-based side channel attack on compromising IP information during 3D printing.

The security game is a basic model for resource allocation in adversarial environments. Here there are two players, a defender and an attacker. The defender wants to allocate her limited resources to defend critical targets and the attacker seeks his most favorable target to attack. In the past decade, there has been a surge of research interest in analyzing and solving security games that are motivated by applications from various domains. Remarkably, these models and their game-theoretic solutions have led to real-world deployments in use by major security agencies like the LAX airport, the US Coast Guard and Federal Air Marshal Service, as well as non-governmental organizations. Among all these research and applications, equilibrium computation serves as a foundation. This paper examines security games from a theoretical perspective and provides a unified view of various security game models. In particular, each security game can be characterized by a set system E which consists of the defender's pure strategies; The defender's best response problem can be viewed as a combinatorial optimization problem over E. Our framework captures most of the basic security game models in the literature, including all the deployed systems; The set system E arising from various domains encodes standard combinatorial problems like bipartite matching, maximum coverage, min-cost flow, packing problems, etc. Our main result shows that equilibrium computation in security games is essentially a combinatorial problem. In particular, we prove that, for any set system \$E\$, the following problems can be reduced to each other in polynomial time: (0) combinatorial optimization over E; (1) computing the minimax equilibrium for zero-sum security games over E; (2) computing the strong Stackelberg equilibrium for security games over E; (3) computing the best or worst (for the defender) Nash equilibrium for security games over E. Therefore, the hardness [polynomial solvability] of any of these problems implies the hardness [polynomial solvability] of all the others. Here, by "games over E" we mean the class of security games with arbitrary payoff structures, but a fixed set E of defender pure strategies. This shows that the complexity of a security game is essentially determined by the set system E. We view drawing these connections as an important conceptual contribution of this paper.

Large scale applications in data centers are composed of computers connected with a network. Traditional network switches cannot be flexibly controlled. Then, application developer cannot optimize network elements' behavior for improving application performance. On the other hand, Deeply Programmable Network (DPN) switches can completely analyze packet payloads and be profoundly programmed. In this paper, we focus on processing a part of application functions in network elements for improving application performance based on Deep Packet Inspection (DPI), i.e. analyzing packet payload, using DPN switches. We assume some applications as targets and implement some of functions of applications in network switches. We then present the comparison of performances with and without out method, and show that our method can significantly increase application performance.

This paper establishes a new framework for electrical cyber-physical systems (ECPSs). The communication network is designed by the characteristics of a power grid. The interdependent relationship of communication networks and power grids is described by data-uploading channels and commands-downloading channels. Control strategies (such as load shedding and relay protection) are extended to this new framework for analyzing the performance of ECPSs under several attack scenarios. The fragility of ECPSs under cyber attacks (DoS attack and false data injection attack) and the effectiveness of relay protection policies are verified by experimental results.

As the amount of spatial data gets bigger, organizations realized that it is cheaper and more flexible to keep their data on the Cloud rather than to establish and maintain in-house huge data centers. Though this saves a lot for IT costs, organizations are still concerned about the privacy and security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the data set, which is impractical. In this paper, we propose a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. The proposed technique suggests a new index structure to support answering range query over encrypted data set. The proposed indexing scheme is based on the Z-curve. The paper describes a distributed algorithm for answering range queries over spatial data stored on the Cloud. We carried many simulation experiments to measure the performance of the proposed scheme. The experimental results show that the proposed scheme outperforms the most recent schemes by Kim et al. in terms of data redundancy.

SMS (Short Messaging Service) is a text messaging service for mobile users to exchange short text messages. It is also widely used to provide SMS-powered services (e.g., mobile banking). With the rapid deployment of all-IP 4G mobile networks, the underlying technology of SMS evolves from the legacy circuit-switched network to the IMS (IP Multimedia Subsystem) system over packet-switched network. In this work, we study the insecurity of the IMS-based SMS. We uncover its security vulnerabilities and exploit them to devise four SMS attacks: silent SMS abuse, SMS spoofing, SMS client DoS, and SMS spamming. We further discover that those SMS threats can propagate towards SMS-powered services, thereby leading to three malicious attacks: social network account hijacking, unauthorized donation, and unauthorized subscription. Our analysis reveals that the problems stem from the loose security regulations among mobile phones, carrier networks, and SMS-powered services. We finally propose remedies to the identified security issues.

Finding security vulnerabilities in the source code as early as possible is becoming more and more essential. In this respect, vulnerability prediction models have the potential to help the security assurance activities by identifying code locations that deserve the most attention. In this paper, we investigate whether prediction models behave like milk (i.e., they turn with time) or wine (i.e., the improve with time) when used to predict future vulnerabilities. Our findings indicate that the recall values are largely in favor of predictors based on older versions. However, the better recall comes at the price of much higher file inspection ratio values.

In this paper, we analyze the performance and cost trade-off from selecting two representations of nodes when implementing the Aho-Corasick algorithm. This algorithm can be used for pattern matching in network-based intrusion detection systems such as Snort. Our analysis uses the Snort 2.9.7 rules set, which contains almost 26k patterns. Our methodology consists of code profiling and analysis, followed by the selection of a parameter to maximize a metric that combines clock cycles count and memory usage. The parameter determines which of two types of nodes is selected for each trie node. We show that it is possible to select the parameter to optimize the metric, which results in an improvement by up to 12× compared with the single node-type case.