Intrusion Detection Using Variable-Length Audit Trail Patterns

BackgroundWith the advent of Next-Generation Sequencing technologies (NGS), a large amount of short read data has been generated. If a reference genome is not available, the assembly of a template sequence is usually challenging because of repeats and the short length of reads. When NGS reads cannot be mapped onto a reference genome alignment-based methods are not applicable. However it is still possible to study the evolutionary relationship of unassembled genomes based on NGS data.ResultsWe present a parameter-free alignment-free method, called , based on variable-length patterns, for the direct comparison of sets of NGS reads. We define a similarity measure using variable-length patterns, as well as reverses and reverse-complements, along with their statistical and syntactical properties. We evaluate several alignment-free statistics on the comparison of NGS reads coming from simulated and real genomes. In almost all simulations our method outperforms all other statistics. The performance gain becomes more evident when real genomes are used.ConclusionThe new alignment-free statistic is highly successful in discriminating related genomes based on NGS reads data. In almost all experiments, it outperforms traditional alignment-free statistics that are based on fixed length patterns.

We propose a novel method for estimating speech rate based on the durations of similar patterns as a first step in determining the relation between various speaking styles used in everyday conversation and speaker intentions or attitudes. Whereas most methods of determining speaking rate require manually obtained label information or linguistic knowledge, the proposed method uses patterns of speech-sound sequences that occur relatively frequently in dialogue speech, as detected from the speech waveform information alone. For use as an index of speaking rate, the method calculates the z-score of each pattern duration, relative to the distribution of the respective pattern groups. The method uses speech recognition to provide a rough classification of the speech sounds, i.e., as a phonetic typewriter, but without requiring accuracy of recognition in any meaningful linguistic terms. From a large body of natural dialogue speech data, it divides the label sequences obtained from the recognizer/classifier into variable length patterns according to maximum likelihood, and classifies all speech segments having the same pattern as a group. The validity of speech rate detection was evaluated.

Objectives: To design a new framework to efficiently parallelize the steps of VLASPD algorithm using a hybridized apriori and fp-growth on GPU; to implement the existing and proposed framework in CUDA;to improve the performance factors like computational time, memory and CPU utilization.Methods/Statistical Analysis: This paper proposes the acceleration of Protein-Protein Interactions (PPIs) prediction on Graphics Processing Units(GPUs). A GPU can provide more processing cores and computational power in the same cost as a CPU.Findings: The frequently occurring patterns in the protein sequences can be used for PPIs prediction.The moving of the approaches from fixed length to variable length lead to computational complexity but also is found to be advantageous.Applications/Improvements:Sequence biology is since being researched by various computer engineers, the GPUs can be employed for predicting various sequence interactions like DNA-Proteins, etc. Since the GPU runs the parallel code efficiently, the methodology can be further improved if efficiently parallelized.

The process of component security testing can produce massive amounts of monitor logs. Current approaches to detect implicit security exceptions (those which cannot be identified by visual inspection alone) compare correct execution sequences with fixed patterns mined from the execution of sequential patterns in the monitor logs. However, this is not efficient and is not suitable for mining large monitor logs. To enable effective mining of implicit security exceptions from large monitor logs, this paper proposes a method based on improved variable-length sequential pattern mining. The proposed method first mines the variable-length sequential patterns from correct execution sequences and from actual execution sequences, thus reducing the number of patterns. The sequential patterns are then detected using the Sunday string-searching algorithm. We conducted an experimental study based on this method, the results of which show that the proposed method can efficiently detect the implicit security exceptions of components.

This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the-art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein–protein interactions directly from sequences. The web service is available at and a mirror site at .

Most of the existing algorithms for mining frequent patterns could produce lots of projected databases and short patterns which could increase the time and memory cost of mining. In order to overcome such shortcoming, a fast and efficient algorithm named FBPM for mining frequent patterns in biological sequence is proposed. We first present the concept of primary pattern, and then use prefix tree for mining frequent primary patterns. A pattern growth approach is also presented to mine all the frequent patterns without producing large amount of irrelevant patterns. Our experimental results show that FBPM not only improves the performance but also achieves effective mining results.

The goal of motif discovery is to detect novel, unknown, and important signals from biology sequences. In most models, the importance of a motif is equal to the sum of the similarity of every single position. In 2006, Song et al. introduced Aggregated Related Column Score (ARCS) measure which includes correlation information to the evaluation of motif importance. The paper showed that the ARCS measure is superior to other measures. Due to the complicated nature of the ARCS motif model, we cannot directly apply existing sequential motif discovery methods to find motifs with high ARCS values. This article presents a novel mining algorithm, ARCS-Motif, to discover related sequential motifs in biological sequences. ARCS-Motif is applied to 400 PROSITE datasets and compared with five alternative methods (CONSENSUS, Gibbs sampler, MEME, SPLASH and DIALIGN-TX). ARCS-Motif outperforms all the methods in accuracy, and most of the methods in efficiency. Although SPLASH has better efficiency than ARCS-Motif, ARCS-Motif has much better accuracy than SPLASH. On average, ARCS-Motif is able to produce the motifs which are at least 10% better than the best of the alternative methods. Among the 400 PROSITE datasets, ARCS-Motif produces the best motifs for more than 200 families. Other than SPLASH, the execution time of ARCS-Motif is less than a third of that of the fastest alternative method and its execution time grows at the slowest rate with respect to the number of sequences and the average sequence among all methods.

Nowadays, Wireless Sensor Network (WSN) have created tremendous prominence because this evident benefits while comparing to the conventional cable network are simple in distribution, users mobility over the area of network coverage and ease in new user’s connection. With sudden increase of communications in digital technologies are depressed through traffic of network data. The usage demand in an internet has grown daily use for the present cyber world have influences the requirement of network security. The WSN security can be supported through the strategies of attack and defense using Machine Learning (ML) which perform automated in learning from and adapt to the characteristics of wireless communication which are firm in capturing using the features and model of hand-crafted. However, several security attacks have classified as cyber ones and the attack types are interested in the system tentative as well as spoils the data instead of data stealing. Hence, the paper focuses on Intrusion Detection System (IDS) has done through Stacked Autoencoder (SAE) with Artificial Neural Network (ANN) in sequential patterns method is produced improved security in DoS attacks. Moreover, the dataset of security type for DoS in WSN has mainly focused in identifying attacks, malicious node elimination and transmission data security

In this paper we present a frequent sequence pattern mining-based algorithm used for network intrusion detection, which is an application and extension of the SPADE algorithm. It is based on the idea that much behavior on the network appears as sequences of activities, according to the sequence patterns we computed, we can construct the intrusion rule base and legal action rule base, then we can detect known and novel intrusion activities by rule matching. In addition, when the system is running, we use an incremental sequence pattern mining algorithm to complement the rule library in order to avoid re-executing the algorithm on the entire dataset, thereby reducing execution time. The experimental results indicate that this algorithm is efficient enough to meet the needs for active detection of intrusion. Compared with most existing methods used in commercial systems which are built using purely knowledge engineering approaches, our algorithm is more intelligent and adaptive.

Focusing on the users is more interested in the sequential patterns that accord with the well completion business process habit overall in the web access mode of well completion mobile platform. This paper proposed a weighted sequential pattern mining algorithm based on the well completion business process. With analyzing the business process models and web access log of well completion, confirm the business dependency strength calculation model of well completion as the sequence weight, at the same time, using the technology of k-minimum weighted support in the weighted mining to improve the PrefixSpan algorithm. The algorithm discards a lot of access sequence that dissatisfied the needs of the business process in the weighted mining, avoids the happening of the candidate combination explosion effectively. Experiments showed that the algorithm can rapidly excavate meaningful well completion sequential patterns.

Mitochondrial proteins of Plasmodium falciparum (MPPF) are an important target for anti-malarial drugs, but their identification through manual experimentation is costly, and in turn, their related drugs production by pharmaceutical institutions involves a prolonged time duration. Therefore, it is highly desirable for pharmaceutical companies to develop computationally automated and reliable approach to identify proteins precisely, resulting in appropriate drug production in a timely manner. In this direction, several computationally intelligent techniques are developed to extract local features from biological sequences using machine learning methods followed by various classifiers to discriminate the nature of proteins. Unfortunately, these techniques demonstrate poor performance while capturing contextual features from sequence patterns, yielding non-representative classifiers. In this paper, we proposed a sequence-based framework to extract deep and representative features that are trust-worthy for Plasmodium mitochondrial proteins identification. The backbone of the proposed framework is MPPF identification-net (MPPFI-Net), that is based on a convolutional neural network (CNN) with multilayer bi-directional long short-term memory (MBD-LSTM). MPPIF-Net inputs protein sequences, passes through various convolution and pooling layers to optimally extract learned features. We pass these features into our sequence learning mechanism, MBD-LSTM, that is particularly trained to classify them into their relevant classes. Our proposed model is experimentally evaluated on newly prepared dataset PF2095 and two existing benchmark datasets i.e., PF175 and MPD using the holdout method. The proposed method achieved 97.6%, 97.1%, and 99.5% testing accuracy on PF2095, PF175, and MPD datasets, respectively, which outperformed the state-of-the-art approaches.

We present here a heuristic method toward predicting the expression specificity in the transcriptional process, which is known to be regulated in large part by promoter sequences, by observing the appearance of conserved sequence patterns in a group of known promoters, such as for housekeeping or tissue-specific genes. Statistically conserved patterns were automatically extracted from a set of unaligned sequences up to 200 bp upstream of the transcription initiation site, by a standard procedure using the Markov chain and binomial distribution models. Furthermore, to obtain signal sequences of optimal lengths we devised a method that combines the multiple alignment and the analysis of the information content (or relative entropy). Groups of related promoters were compiled from the EPD eukaryotic promoter database and the EMBL nucleic acid sequence database. Each promoter was examined for its specificity by linear discriminant analysis to test the validity of the extracted patterns. Our method could correctly discriminate 77.6% of the housekeeping gene promoters and 62.9% of the liver promoters.

This unit illustrates how to use MEME to discover motifs in a group of related nucleotide or peptide sequences. A MEME motif is a sequence pattern that occurs repeatedly in one or more sequences in the input group. MEME can be used to discover novel patterns because it bases its discoveries only on the input sequences, not on any prior knowledge (such as databases of known motifs). The input to MEME is a set of unaligned sequences of the same type (peptide or nucleotide). For each motif it discovers, MEME reports the occurrences (sites), consensus sequence, and the level of conservation (information content) at each position in the pattern. MEME also produces block diagrams showing where all of the discovered motifs occur in the training set sequences. MEME's hypertext (HTML) output also contains buttons that allow for the convenient use of the motifs in other searches.

In this work we created a sequence model that goes beyond simple linear patterns to model a specific type of higher-order relationship possible in biological sequences. Particularly, we seek models that can account for partially overlaid and interleaved patterns in biological sequences. Our proposed context-switching model (cswHMM) is designed as a variable-order hidden Markov model (HMM) with a specific structure that allows switching control between two or more sub-models.Tests of this approach suggest that a combination of HMMs for protein sequence analysis, such as pattern mining based HMMs or profile HMMs, with the context-switching approach can improve the descriptive ability and performance of the models.

Sequential pattern mining and temporal association rule mining techniques are established techniques to mine insights into the data collected by smart home environments and ambient living systems. Local process models are a recent technique that uses constructs from the field of business process modeling to represent frequent patterns that go beyond sequential patterns and can additionally express rich ordering relations that include concurrent execution, choices, and repetition. To gain insight into the behavior within smart home environments using local process models, it is vital to focus on patterns where the activities that are described by the pattern are truly related. We show on real-life smart home data that existing techniques for mining local process models fail to do so, and address this problem by proposing novel techniques for constraint-based local process model mining, focusing on event gap constraints and time gap constraints. We observe that in order to get an accurate support count of a local process model, it is sufficient to only consider specific parts of the datasets instead of the full dataset, which allows to speed up the counting of the support of a pattern. We provide a novel algorithm to extract those relevant parts of the data for support counting. We evaluate our approach on a collection of real-life smart home data sets. We show that more insightful local process models can be mined when applying such constraints, and show the novel algorithm allows to mine them efficiently.