Large industrial systems that combine services and applications, have become targets for cyber criminals and are challenging from the security, monitoring and auditing perspectives. Security log analysis is a key step for uncovering anomalies, detecting intrusion, and enabling incident response. The constant increase of link speeds, threats and users, produce large volumes of log data and become increasingly difficult to analyse on a Central Processing Unit (CPU). This paper presents a massively parallel Graphics Processing Unit (GPU) Log Processing (GLoP) library and can also be used for Deep Packet Inspection (DPI), using a prefix matching technique, harvesting the full power of off-the-shelf technologies. GLoP implements two different algorithm using different GPU memory and is compared against CPU counterpart implementations. The library can be used for processing nodes with single or multiple GPUs as well as GPU cloud farms. The results show throughput of 20 Gbps and demonstrate that modern GPUs can be utilised to increase the operational speed of large scale log processing scenarios, saving precious time before and after an intrusion has occurred.
MapReduce is an efficient distributed computing model for large-scale data processing. However, single-node performance is gradually to be the bottleneck in compute-intensive jobs. This paper presents an approach of MapReduce improvement with GPU acceleration, which is implemented by Hadoop and OpenCL. Different from other implementations, it targets at general and inexpensive hardware platform, and it is seamless-integrated with Apache Hadoop, a most widely used MapReduce framework. As a heterogeneous multi-machine and multicore architecture, it aims at both data- and compute-intensive applications. An almost 2 times performance improvement has been validated, without any farther optimization. For the virus signature matching time of traditional the eigenvalue is too long, which can’t meet the need of the information security, a method of fast virus signature matching on the GPU is compelled. The system uses the GPU as a fast filter to quickly identify possible virus signatures for thousands of data objects in parallel. The performance of their library suggests that the GPU is now a viable platform for cost-effective, high-performance network security processing. And it shows that the computing speed of the eigenvalue based on GPU is obviously higher than the eigenvalue based on CPU by the experiment. An algorithm is presented which finds all occurrences of one given string within another, in running time proportional to the sum of the lengths of the strings. The constant of proportionality is low enough to make this algorithm of practical use, and the procedure can also be extended to deal with some more general pattern-matching problems. A theoretical application of the algorithm shows that the set of concatenations of even palindromes, i.e., the language @math , can be recognized in linear time. Other algorithms which run even faster on the average are also considered. Graphics Processing Units (GPUs) have become the focus of much interest with the scientific community lately due to their highly parallel computing capabilities, and cost effectiveness. They have evolved from simple graphic rendering devices to extremely complex parallel processors, used in a plethora of scientific areas. This paper outlines experimental results of a comparison between GPUs and general purpose CPUs for exact pattern matching. Specifically, a comparison is conducted for the Knuth-Morris-Pratt algorithm using different string sizes, alphabet sizes and introduces different techniques such as loop unrolling, and shared memory using the Compute Unified Device Architecture framework. Empirical results demonstrate a 29 fold increase in processing speed where GPUs are used instead of CPUs.