A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures. These probabilities are subsequently used to compute the similarity score between a sequence and a pHMM graph. The Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these probabilities to optimize and compute similarity scores. However, the Baum-Welch algorithm is computationally intensive, and existing solutions offer either software-only or hardware-only approaches with fixed pHMM designs. We identify an urgent need for a flexible, high-performance, and energy-efficient HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs. We introduce ApHMM, the first flexible acceleration framework designed to significantly reduce both computational and energy overheads associated with the Baum-Welch algorithm for pHMMs. ApHMM tackles the major inefficiencies in the Baum-Welch algorithm by 1) designing flexible hardware to accommodate various pHMM designs, 2) exploiting predictable data dependency patterns through on-chip memory with memoization techniques, 3) rapidly filtering out negligible computations using a hardware-based filter, and 4) minimizing redundant computations. ApHMM achieves substantial speedups of 15.55x - 260.03x, 1.83x - 5.34x, and 27.97x when compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations in three key bioinformatics applications: 1) error correction, 2) protein family search, and 3) multiple sequence alignment, by 1.29x - 59.94x, 1.03x - 1.75x, and 1.03x - 1.95x, respectively, while improving their energy efficiency by 64.24x - 115.46x, 1.75x, 1.96x.Comment: Accepted to ACM TAC

Matched filter stochastic background characterization for hyperspectral target detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different scientific fields, can be used to locate spectral targets by modeling scene background as either structured geometric) with a set of endmembers (basis vectors) or as unstructured stochastic) with a covariance matrix. In unstructured background research, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This thesis will establish a strong theoretical foundation, describing the necessary preprocessing of hyperspectral imagery, deriving the spectral matched filter, and capturing current methods of unstructured background characterization. The extensive experimentation will allow for a comparative evaluation of several current unstructured background characterization methods as well as some new methods which improve stochastic modeling of the background. The results will show that consistent improvements over the scene-wide statistics can be achieved through spatial or spectral subsetting, and analysis of the results provides insight into the tradespaces of matching the interference, background multivariate normality and target exclusion for these techniques

Developing SQuIRE to map the landscape of interspersed repeat expression

Transposable elements (TEs) are interspersed repeat sequences that make up much of the human genome. Their expression has been implicated in development and disease. However, RNA-seq of TE transcripts results in ambiguous multi-mapping reads that are difficult to quantify. Past approaches to TE RNA-seq analysis have excluded these reads, aligned the reads to interspersed repeat consensus sequences, or aggregated RNA expression to subfamilies shared by similar TE copies. Such approaches have lost either quantitative accuracy or the genomic context necessary to understand TE transcription and its effects. As a result, repetitive sequence contributions to transcriptomes are not well understood. Here, we present Software for Quantifying Interspersed Repeat Expression (SQuIRE), to date the first and only RNA-seq analysis pipeline that provides a quantitative and locus-specific picture of interspersed repeat RNA expression. We demonstrate that SQuIRE is an accurate and powerful tool that can be used for a variety of species. Using SQuIRE on a variety of cell and tissue types in human and mouse data, we found that only a small percentage of TEs are transcribed, and that differential expression of TEs includes transcription of longer TE-containing mRNAs and lncRNAs. Our findings illustrate the importance of studying TE transcription with locus-level resolution. SQuIRE can be downloaded at (github.com/wyang17/SQuIRE)

Human interaction with digital ink : legibility measurement and structural analysis

Literature suggests that it is possible to design and implement pen-based computer interfaces that resemble the use of pen and paper. These interfaces appear to allow users freedom in expressing ideas and seem to be familiar and easy to use. Different ideas have been put forward concerning this type of interface, however despite the commonality of aims and problems faced, there does not appear to be a common approach to their design and implementation. This thesis aims to progress the development of pen-based computer interfaces that resemble the use of pen and paper. To do this, a conceptual model is proposed for interfaces that enable interaction with "digital ink". This conceptual model is used to organize and analyse the broad range of literature related to pen-based interfaces, and to identify topics that are not sufficiently addressed by published research. Two issues highlighted by the model: digital ink legibility and digital ink structuring, are then investigated. In the first investigation, methods are devised to objectively and subjectively measure the legibility of handwritten script. These methods are then piloted in experiments that vary the horizontal rendering resolution of handwritten script displayed on a computer screen. Script legibility is shown to decrease with rendering resolution, after it drops below a threshold value. In the second investigation, the clustering of digital ink strokes into words is addressed. A method of rating the accuracy of clustering algorithms is proposed: the percentage of words spoiled. The clustering error rate is found to vary among different writers, for a clustering algorithm using the geometric features of both ink strokes, and the gaps between them. The work contributes a conceptual interface model, methods of measuring digital ink legibility, and techniques for investigating stroke clustering features, to the field of digital ink interaction research

Innovation Of Petrophysical And Geomechanical Experiment Methodologies: The Application Of 3D Printing Technology

The petrophysical and geomechanical properties of rocks link the geology origin with engineering practice, which serves as the fundamental of various disciplinaries associated with subsurface porous media, including civil engineering, underground water, geological exploration, and petroleum engineering. The research methodologies can be mainly divided into three aspects: theoretical modelling, numerical simulation, and experiments, in which the last approach plays a critical role that can support, validate, calibrate, or even refute a hypothesis. Only replying on repeatable trials and consolidate analysis of precise results can the experiments be successful and convincing, though uncertainties, due to multiple factors, need to be scrutinized and controlled. The challenges also existed in the characterization and measurements of rock properties as a result of heterogeneity and anisotropy as well as the inevitable impact of experimental operation. 3D printing, a cutting-edge technology, was introduced and utilized in the study that is supposed to be capable of controlling the mineralogy, microstructure, physical properties of physical rock replicas and further benefit the petrophysical and geomechanical experimental methodologies. My PhD research project attempted to answer the questions from the standpoint of petrophysicisits and geomechanics scientist: Can 3D printed rocks replicate natural rocks in terms of microstructure, petrophysical and geomechanical properties? If not, by any means can we improve the quality of replicas to mimic the common rock types? Which 3D printing method is best suitable for our research purposes? How could it be applied in the conventional experiments and integrated with theoretical calculation or numerical simulation? Three main types of printing materials and techniques (gypsum, silica sand, resin) were characterized first individually, which demonstrated varying microstructure, anisotropy, petrophysical and geomechanical properties. Post-processing effect was examined on the 3D printed gypsum rocks that show impact differences on nanoscale and microscale pore structures. Through comparison, resin, the material used in stereolithography technology, best suits the reconstruction of intricate pore network that aims to complement digital rock physics and ultimately be applied in petrophysical research. Gypsum material, however, has been proved as the best candidate for geomechanical research spanning from reference samples to upscaling methods validation. Currently, a practical approach of utilizing 3D printing in petroleum geoscience is taking advantages of the characteristics we focus on the research while disregarding the other properties, by which a suitable 3D printing material and technique can emerge