Biblio

Cross site scripting (XSS) attack is one of the attacks on the web. It brings session hijack with HTTP cookies, information collection with fake HTML input form and phishing with dummy sites. As a countermeasure of XSS attack, machine learning has attracted a lot of attention. There are existing researches in which SVM, Random Forest and SCW are used for the detection of the attack. However, in the researches, there are problems that the size of data set is too small or unbalanced, and that preprocessing method for vectorization of strings causes misclassification. The highest accuracy of the classification was 98% in existing researches. Therefore, in this paper, we improved the preprocessing method for vectorization by using word2vec to find the frequency of appearance and co-occurrence of the words in XSS attack scripts. Moreover, we also used a large data set to decrease the deviation of the data. Furthermore, we evaluated the classification results with two procedures. One is an inappropriate procedure which some researchers tend to select by mistake. The other is an appropriate procedure which can be applied to an attack detection filter in the real environment.

Cryptographic primitives are subject to diverging imperatives. Functional correctness and auditability pushes for the use of a high-level programming language. Performance and the threat of timing attacks push for using no more abstract than an assembler to exploit (or avoid!) the micro-architectural features of a given machine. We believe that a suitable programming language can reconcile both views and actually improve on the state of the art of both. Usuba is an opinionated dataflow programming language in which block ciphers become so simple as to be ``obviously correct'' and whose types document and enforce valid parallelization strategies at the granularity of individual bits. Its optimizing compiler, Usubac, produces high-throughput, constant-time implementations performing on par with hand-tuned reference implementations. The cornerstone of our approach is a systematization and generalization of bitslicing, an implementation trick frequently used by cryptographers.

This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time. In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manually-optimized expert codes that are considered to be among the top contenders in their fields.