Biblio

In this paper we propose Mastino, a novel defense system to detect malware download events. A download event is a 3-tuple that identifies the action of downloading a file from a URL that was triggered by a client (machine). Mastino utilizes global situation awareness and continuously monitors various network- and system-level events of the clients' machines across the Internet and provides real time classification of both files and URLs to the clients upon submission of a new, unknown file or URL to the system. To enable detection of the download events, Mastino builds a large download graph that captures the subtle relationships among the entities of download events, i.e. files, URLs, and machines. We implemented a prototype version of Mastino and evaluated it in a large-scale real-world deployment. Our experimental evaluation shows that Mastino can accurately classify malware download events with an average of 95.5% true positive (TP), while incurring less than 0.5% false positives (FP). In addition, we show the Mastino can classify a new download event as either benign or malware in just a fraction of a second, and is therefore suitable as a real time defense system.

Based on Storm, a distributed, reliable, fault-tolerant real-time data stream processing system, we propose a recognition system of web intrusion detection. The system is based on machine learning, feature selection algorithm by TF-IDF(Term Frequency–Inverse Document Frequency) and the optimised cosine similarity algorithm, at low false positive rate and a higher detection rate of attacks and malicious behavior in real-time to protect the security of user data. From comparative analysis of experiments we find that the system for intrusion recognition rate and false positive rate has improved to some extent, it can be better to complete the intrusion detection work.

Implementation attacks and more specifically Power Analysis (PA) (the dominant type of side channel attack) and fault injection (FA) attacks constitute a pragmatic hazard for scalar multiplication, the main operation behind Elliptic Curve Cryptography. There exists a wide variety of countermeasures attempting to thwart such attacks that, however, few of them explore the potential of alternative number systems like the Residue Number System (RNS). In this paper, we explore the potential of RNS as an PA-FA countermeasure and propose an PA-FA resistant scalar multiplication algorithm and provide an extensive security analysis against the most effective PA-FA techniques. We argue through a security analysis that combining traditional PA-FA countermeasures with lightweight RNS countermeasures can provide strong PA-FA resistance.

Control plane distribution on Software Defined Networking enhances security, performance and scalability of the network. In this paper, we propose an efficient architecture for distribution of controllers. The main contributions of the proposed architecture are: i) A controller distributed areas to ensure security, performance and scalability of the network; ii) A single database maintained by a designated controller to provide consistency to the control plane; iii) An optimized heuristic for locating controllers to reduce latency in the control plane; iv) A resilient mechanism of choosing the designated controller to ensure the proper functioning of the network, even when there are failures. A prototype of the proposal was implemented and the placement heuristic was analyzed in real topologies. The results show that connectivity is maintained even in failure scenarios. Finally, we show that the placement optimization reduces the average latency of controllers. Our proposed heuristic achieves a fair distribution of controllers and outperforms the network resilience of other heuristics up to two times better.

Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.

Cascading failure is an intrinsic threat of power grid to cause enormous cost of society, and it is very challenging to be analyzed. The risk of cascading failure depends both on its probability and the severity of consequence. It is impossible to analyze all of the intrinsic attacks, only the critical and high probability initial events should be found to estimate the risk of cascading failure efficiently. To recognize the critical and high probability events, a cascading failure analysis model for power transmission grid is established based on complex network theory (CNT) in this paper. The risk coefficient of transmission line considering the betweenness, load rate and changeable outage probability is proposed to determine the initial events of power grid. The development tendency of cascading failure is determined by the network topology, the power flow and boundary conditions. The indicators of expected percentage of load loss and line cut are used to estimate the risk of cascading failure caused by the given initial malfunction of power grid. Simulation results from the IEEE RTS-79 test system show that the risk of cascading failure has close relations with the risk coefficient of transmission lines. The value of risk coefficient could be useful to make vulnerability assessment and to design specific action to reduce the topological weakness and the risk of cascading failure of power grid.

Nowadays, the principle of image mining plays a vital role in various areas of our life, where numerous frameworks based on image mining are proposed for object recognition, object tracking, sensing images and medical image diagnosis. Nevertheless, the research in the image authentication based on image mining is still confined. Therefore, this paper comes to present an efficient engagement between the frequent pattern mining and digital watermarking to contribute significantly in the authentication of images transmitted via public networks. The proposed framework exploits some robust features of image to extract the frequent patterns in the image data. The maximal relevant patterns are used to discriminate between the textured and smooth blocks within the image, where the texture blocks are more appropriate to embed the secret data than smooth blocks. The experiment's result proves the efficiency of the proposed framework in terms of stabilization and robustness against different kind of attacks. The results are interesting and remarkable to preserve the image authentication.

Consensus algorithms provide strategies to solve problems in a distributed system with the added constraint that data can only be shared between adjacent computing nodes. We find these algorithms in applications for wireless and sensor networks, spectrum sensing for cognitive radio, even for some IoT services. However, consensus-based applications are not resilient to compromised nodes sending falsified data to their neighbors, i.e. they can be the target of Byzantine attacks. Several solutions have been proposed in the literature inspired from reputation based systems, outlier detection or model-based fault detection techniques in process control. We have reviewed some of these solutions, and propose two mitigation techniques to protect the consensus-based Network Intrusion Detection System in [1]. We analyze several implementation issues such as computational overhead, fine tuning of the solution parameters, impacts on the convergence of the consensus phase, accuracy of the intrusion detection system.

Consensus algorithms provide strategies to solve problems in a distributed system with the added constraint that data can only be shared between adjacent computing nodes. We find these algorithms in applications for wireless and sensor networks, spectrum sensing for cognitive radio, even for some IoT services. However, consensus-based applications are not resilient to compromised nodes sending falsified data to their neighbors, i.e. they can be the target of Byzantine attacks. Several solutions have been proposed in the literature inspired from reputation based systems, outlier detection or model-based fault detection techniques in process control. We have reviewed some of these solutions, and propose two mitigation techniques to protect the consensus-based Network Intrusion Detection System in [1]. We analyze several implementation issues such as computational overhead, fine tuning of the solution parameters, impacts on the convergence of the consensus phase, accuracy of the intrusion detection system.

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.

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.

The emerging trends of volatile distributed energy resources and micro-grids are putting pressure on electrical power system infrastructure. This pressure is motivating the integration of digital technology and advanced power-industry practices to improve the management of distributed electricity generation, transmission, and distribution, thereby creating a web of systems. Unlike legacy power system infrastructure, however, this emerging next-generation smart grid should be context-aware and adaptive to enable the creation of applications needed to enhance grid robustness and efficiency. This paper describes key factors that are driving the architecture of smart grids and describes orchestration middleware needed to make the infrastructure resilient. We use an example of adaptive protection logic in smart grid substations as a use case to motivate the need for contextawareness and adaptivity.

As web applications is becoming more prominent due to the ubiquity of web services, web applications have become main targets for attackers. In order to steal or leak sensitive user data managed by web applications, attackers exploit a wide range of input validation vulnerabilities such as SQL injection, path traversal (or directory traversal), cross-site scripting (XSS), etc. This paper propose a technique that can verify input values of Java-based web applications using static bytecode instrumentation and runtime input validation. The technique searches for target methods or object constructors in compiled Java class files, and statically inserts bytecode modules. At runtime, the instrumented bytecode modules validate input values of the targets, and take countermeasure against malicious inputs. The proposed technique can mitigate the input validation vulnerabilities in Java-based web applications without source codes. To evaluate the effectiveness of the proposed technique, experiments are carried out with an insecure web application maintained by OWASP WebGoat Project. The experimental results show that the proposed technique successfully mitigates input validation vulnerabilities such as SQL injection and path traversal.

Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel's software guard extensions (SGX) [15]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user's input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, heath analysis, image processing and machine translation.

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.

Content-based routing (CBR) is a powerful model that supports scalable asynchronous communication among large sets of geographically distributed nodes. Yet, preserving privacy represents a major limitation for the wide adoption of CBR, notably when the routers are located in public clouds. Indeed, a CBR router must see the content of the messages sent by data producers, as well as the filters (or subscriptions) registered by data consumers. This represents a major deterrent for companies for which data is a key asset, as for instance in the case of financial markets or to conduct sensitive business-to-business transactions. While there exists some techniques for privacy-preserving computation, they are either prohibitively slow or too limited to be usable in real systems. In this paper, we follow a different strategy by taking advantage of trusted hardware extensions that have just been introduced in off-the-shelf processors and provide a trusted execution environment. We exploit Intel's new software guard extensions (SGX) to implement a CBR engine in a secure enclave. Thanks to the hardware-based trusted execution environment (TEE), the compute-intensive CBR operations can operate on decrypted data shielded by the enclave and leverage efficient matching algorithms. Extensive experimental evaluation shows that SGX adds only limited overhead to insecure plaintext matching outside secure enclaves while providing much better performance and more powerful filtering capabilities than alternative software-only solutions. To the best of our knowledge, this work is the first to demonstrate the practical benefits of SGX for privacy-preserving CBR.

The emerging Information-Centric Networking (ICN) paradigm is expected to facilitate content sharing among users. ICN will make it easy for users to appoint storage nodes, in various network locations, perhaps owned or controlled by them, where shared content can be stored and disseminated from. These storage nodes should be (somewhat) trusted since not only they have (some level of) access to user shared content, but they should also properly enforce access control. Traditional forms of encryption introduce significant overhead when it comes to sharing content with large and dynamic groups of users. To this end, proxy re-encryption provides a convenient solution. In this paper, we use Identity-Based Proxy Re-Encryption (IB-PRE) to provide confidentiality and access control for content items shared over ICN, realizing secure content distribution among dynamic sets of users. In contrast to similar IB-PRE based solutions, our design allows each user to generate the system parameters and the secret keys required by the underlay encryption scheme using their own \textbackslashemph\Private Key Generator\, therefore, our approach does not suffer from the key escrow problem. Moreover, our design further relaxes the trust requirements on the storage nodes by preventing them from sharing usable content with unauthorized users. Finally, our scheme does not require out-of-band secret key distribution.

The pervasive presence of interconnected objects enables new communication paradigms where devices can easily reach each other while interacting within their environment. The so-called Internet of Things (IoT) represents the integration of several computing and communications systems aiming at facilitating the interaction between these devices. Arduino is one of the most popular platforms used to prototype new IoT devices due to its open, flexible and easy-to-use architecture. Ardunio Yun is a dual board microcontroller that supports a Linux distribution and it is currently one of the most versatile and powerful Arduino systems. This feature positions Arduino Yun as a popular platform for developers, but it also introduces unique infection vectors from the security viewpoint. In this work, we present a security analysis of Arduino Yun. We show that Arduino Yun is vulnerable to a number of attacks and we implement a proof of concept capable of exploiting some of them.

Providing recommendations on social systems has been in the spotlight of both academics and industry for some time already. Social network giants like Facebook, LinkedIn, Myspace, etc., are eager to find the silver bullet of recommendation. These applications permit clients to shape a few certain social networks through their day-by-day social cooperative communications. In the meantime, today's online experience depends progressively on social association. One of the main concerns in social network is establishing a successful business plan to make more profit from the social network. Doing a business on every platform needs a good business plan with some important solutions such as advertise the products or services of other companies which would be a kind of marketing for those external businesses. In this study a philosophy of a system speaking to of a comprehensive structure of advertisement recommender system for social networks will be presented. The framework uses a semantic logic to provide the recommended products and this capability can differentiate the recommender part of the framework from classical recommender methods. Briefly, the framework proposed in this study has been designed in a form that can generate advertisement recommendations in a simplified and effective way for social network users.