Network Intrusion Detection Using Multiclass Support Vector Machine

Intrusion detection is a topic of interest in current scenario. Statistical IDS overcomes many pitfalls present in signature based IDS. Statistical IDS uses models such as NB, C4.5 etc for classification to detect Intrusions. Multiclass Support Vector Machine is able to perform multiclass classification. This paper shows the performance of MSVM (1-versus-1, 1-versusmany and Error Correcting Output Coding (ECOC)) and it’s variants for statistical NBIDS. This paper explores the performance of MSVM for various categories of attack

Graph Kernels via Functional Embedding

We propose a representation of graph as a functional object derived from the power iteration of the underlying adjacency matrix. The proposed functional representation is a graph invariant, i.e., the functional remains unchanged under any reordering of the vertices. This property eliminates the difficulty of handling exponentially many isomorphic forms. Bhattacharyya kernel constructed between these functionals significantly outperforms the state-of-the-art graph kernels on 3 out of the 4 standard benchmark graph classification datasets, demonstrating the superiority of our approach. The proposed methodology is simple and runs in time linear in the number of edges, which makes our kernel more efficient and scalable compared to many widely adopted graph kernels with running time cubic in the number of vertices

Autoregressive Kernels For Time Series

We propose in this work a new family of kernels for variable-length time series. Our work builds upon the vector autoregressive (VAR) model for multivariate stochastic processes: given a multivariate time series x, we consider the likelihood function p_{\theta}(x) of different parameters \theta in the VAR model as features to describe x. To compare two time series x and x', we form the product of their features p_{\theta}(x) p_{\theta}(x') which is integrated out w.r.t \theta using a matrix normal-inverse Wishart prior. Among other properties, this kernel can be easily computed when the dimension d of the time series is much larger than the lengths of the considered time series x and x'. It can also be generalized to time series taking values in arbitrary state spaces, as long as the state space itself is endowed with a kernel \kappa. In that case, the kernel between x and x' is a a function of the Gram matrices produced by \kappa on observations and subsequences of observations enumerated in x and x'. We describe a computationally efficient implementation of this generalization that uses low-rank matrix factorization techniques. These kernels are compared to other known kernels using a set of benchmark classification tasks carried out with support vector machines

A Kernel Two-sample Test for Dynamical Systems

Evaluating whether data streams were generated by the same distribution is at the heart of many machine learning problems, e.g. to detect changes. This is particularly relevant for data generated by dynamical systems since they are essential for many real-world processes in biomedical, economic, or engineering systems. While kernel two-sample tests are powerful for comparing independent and identically distributed random variables, no established method exists for comparing dynamical systems. The key problem is the critical independence assumption, which is inherently violated in dynamical systems. We propose a novel two-sample test for dynamical systems by addressing three core challenges: we (i) introduce a novel notion of mixing that captures autocorrelations in a relevant metric, (ii) propose an efficient way to estimate the speed of mixing purely from data, and (iii) integrate these into established kernel-two sample tests. The result is a data-driven method for comparison of dynamical systems that is easy to use in practice and comes with sound theoretical guarantees. In an example application to anomaly detection from human walking data, we show that the test readily applies without the need for feature engineering, heuristics, and human expert knowledge