2 research outputs found

    Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

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    Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together

    Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso

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    © Springer International Publishing AG 2017. Performing predictions using a non-linear support vector machine (SVM) can be too expensive in some large-scale scenarios. In the non-linear case, the complexity of storing and using the classifier is determined by the number of support vectors, which is often a significant fraction of the training data. This is a major limitation in applications where the model needs to be evaluated many times to accomplish a task, such as those arising in computer vision and web search ranking. We propose an efficient algorithm to compute sparse approximations of a non-linear SVM, i.e., to reduce the number of support vectors in the model. The algorithm is based on the solution of a Lasso problem in the feature space induced by the kernel. Importantly, this formulation does not require access to the entire training set, can be solved very efficiently and involves significantly less parameter tuning than alternative approaches. We present experiments on well-known datasets to demonstrate our claims and make our implementation publicly available.status: publishe
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