HTTP Request - SQL Query Mapping Scheme for Malicious SQL Query Detection in Multitier Web Applications

Yeongung Seo, Seungyoung Park

http://doi.org/

The continuously growing internet service requirements has resulted in a multitier system structure consisting of web server and database (DB) server. In this multitier structure, the existing intrusion detection system (IDS) detects known attacks by matching misused traffic patterns or signatures. However, malicious change to the contents at DB server through hypertext transfer protocol (HTTP) requests at the DB server cannot be detected by the IDS at the DB server’s end, since the DB server processes structured query language (SQL) without knowing the associated HTTP, while the web server cannot identify the response associated with the attacker’s SQL query. To detect these types of attacks, the malicious user is tracked using knowledge on interaction between HTTP request and SQL query. However, this is a practical challenge because system’s source code analysis and its application logic needs to be understood completely. In this study, we proposed a scheme to find the HTTP request associated with a given SQL query using only system log files. We first generated an HTTP request-SQL query map from system log files alone. Subsequently, the HTTP request associated with a given SQL query was identified among a set of HTTP requests using this map. Computer simulations indicated that the proposed scheme finds the HTTP request associated with a given SQL query with 94% accuracy.

A Function-characteristic Aware Thread-mapping Strategy for an SEDA-based Message Processor in Multi-core Environments

Heeeun Kang, Sungyong Park, Younjeong Lee, Seungbae Jee

http://doi.org/

A message processor is server software that receives various message formats from clients, creates the corresponding threads to process them, and lastly delivers the results to the destination. Considering that each function of an SEDA-based message processor has its own characteristics such as CPU-bound or IO-bound, this paper proposes a thread-mapping strategy called "FC-TM" (function-characteristic aware thread mapping) that schedules the threads to the cores based on the function characteristics in multi-core environments. This paper assumes that message-processor functions are static in the sense that they are pre-defined when the message processor is built; therefore, we profile each function in advance and map each thread to a core using the information in order to maximize the throughput. The benchmarking results show that the throughput increased by up to a maximum of 72 % compared with the previous studies when the ratio of the IO-bound functions to the CPU-bound functions exceeds a certain percentage.

Re-anonymization Technique for Dynamic Data Using Decision Tree Based Machine Learning

Young Ki Kim, Choong Seon Hong

http://doi.org/

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.

Requirements Elicitation and Specification Method for the Development of Adaptive User Interface

Kibeom Park, Seok-Won Lee

http://doi.org/

Many studies have addressed ‘Adaptive User Interface (AUI)’, wherein the user interface changes in accordance with the situation and the environment of each user at runtime. Nevertheless, previous papers for AUI barely reflected the viewpoints from requirements engineering field, since most of them focused on proposing the architecture and design. In this study, we outline AUI with the perspective of requirements engineering and propose the requirements elicitation and specification method based on concepts which have been researched in the area of self-adaptive system. Step by step, we first redefine and reinterpret the well-known concepts of self-adaptive software, after which the AUI requirements are elicited and specified. Finally, we illustrate a case study, which demonstrates the effectiveness of our method.

Korean Semantic Role Labeling using Stacked Bidirectional LSTM-CRFs

Jangseong Bae, Changki Lee

http://doi.org/

Syntactic information represents the dependency relation between predicates and arguments, and it is helpful for improving the performance of Semantic Role Labeling systems. However, syntax analysis can cause computational overhead and inherit incorrect syntactic information. To solve this problem, we exclude syntactic information and use only morpheme information to construct Semantic Role Labeling systems. In this study, we propose an end-to-end SRL system that only uses morpheme information with Stacked Bidirectional LSTM-CRFs model by extending the LSTM RNN that is suitable for sequence labeling problem. Our experimental results show that our proposed model has better performance, as compare to other models.

A Pedestrian Detection Method using Deep Neural Network

Su Ho Song, Hun Beom Hyeon, Hyun Lee

http://doi.org/

Pedestrian detection, an important component of autonomous driving and driving assistant system, has been extensively studied for many years. In particular, image based pedestrian detection methods such as Hierarchical classifier or HOG and, deep models such as ConvNet are well studied. The evaluation score has increased by the various methods. However, pedestrian detection requires high sensitivity to errors, since small error can lead to life or death problems. Consequently, further reduction in pedestrian detection error rate of autonomous systems is required. We proposed a new method to detect pedestrians and reduce the error rate by using the Faster R-CNN with new developed pedestrian training data sets. Finally, we compared the proposed method with the previous models, in order to show the improvement of our method.

Question Answering Optimization via Temporal Representation and Data Augmentation of Dynamic Memory Networks

Dong-Sig Han, Chung-Yeon Lee, Byoung-Tak Zhang

http://doi.org/

The research area for solving question answering (QA) problems using artificial intelligence models is in a methodological transition period, and one such architecture, the dynamic memory network (DMN), is drawing attention for two key attributes: its attention mechanism defined by neural network operations and its modular architecture imitating cognition processes during QA of human. In this paper, we increased accuracy of the inferred answers, by adapting an automatic data augmentation method for lacking amount of training data, and by improving the ability of time perception. The experimental results showed that in the 1K-bAbI tasks, the modified DMN achieves 89.21% accuracy and passes twelve tasks which is 13.58% higher with passing four more tasks, as compared with one implementation of DMN. Additionally, DMN’s word embedding vectors form strong clusters after training. Moreover, the number of episodic passes and that of supporting facts shows direct correlation, which affects the performance significantly.

Sequence-to-sequence based Morphological Analysis and Part-Of-Speech Tagging for Korean Language with Convolutional Features

Jianri Li, EuiHyeon Lee, Jong-Hyeok Lee

http://doi.org/

Traditional Korean morphological analysis and POS tagging methods usually consist of two steps: 1 Generat hypotheses of all possible combinations of morphemes for given input, 2 Perform POS tagging search optimal result. require additional resource dictionaries and step could error to the step. In this paper, we tried to solve this problem end-to-end fashion using sequence-to-sequence model convolutional features. Experiment results Sejong corpus sour approach achieved 97.15% F1-score on morpheme level, 95.33% and 60.62% precision on word and sentence level, respectively; s96.91% F1-score on morpheme level, 95.40% and 60.62% precision on word and sentence level, respectively.

Combining Multiple Strategies for Sleeping Bandits with Stochastic Rewards and Availability

Sanghee Choi, Hyeong Soo Chang

http://doi.org/

This paper considers the problem of combining multiple strategies for solving sleeping bandit problems with stochastic rewards and stochastic availability. It also proposes an algorithm, called sleepComb(Φ), the idea of which is to select an appropriate strategy for each time step based on ϵ_t -probabilistic switching. ϵ_t -probabilistic switching is used in a well-known parameter-based heuristic ϵ_t -greedy strategy. The algorithm also converges to the “best” strategy properly defined on the sleeping bandit problem. In the experimental results, it is shown that sleepComb(Φ) has convergence, and it converges to the “best” strategy rapidly compared to other combining algorithms. Also, we can see that it chooses the “best” strategy more frequently.

Finding the Minimum MBRs Embedding K Points

Keonwoo Kim, Younghoon Kim

http://doi.org/

There has been a recent spate in the usage of mobile device equipped GPS sensors, such as smart phones. This trend enables the posting of geo-tagged messages (i.e., multimedia messages with GPS locations) on social media such as Twitter and Facebook, and the volume of such spatial data is rapidly growing. However, the relationships between the location and content of messages are not always explicitly shown in such geo-tagged messages. Thus, the need arises to reorganize search results to find the relationship between keywords and the spatial distribution of messages. We find the smallest minimum bounding rectangle (MBR) that embedding k or more points in order to find the most dense rectangle of data, and it can be usefully used in the location search system. In this paper, we suggest efficient algorithms to discover a group of 2-Dimensional spatial data with a close distance, such as MBR. The efficiency of our proposed algorithms with synthetic and real data sets is confirmed experimentally.

A Bottom-up Algorithm to Find the Densest Subgraphs Based on MapReduce

Woonghee Lee, Younghoon Kim

http://doi.org/

Finding the densest subgraphs from social networks, such that people in the subgraph are in a particular community or have common interests, has been a recurring problem in numerous studies undertaken. However, these algorithms focused only on finding the single densest subgraph. We suggest a heuristic algorithm of the bottom-up type, which finds the densest subgraph by increasing its size from a given starting node, with the repeated addition of adjacent nodes with the maximum degree. Furthermore, since this approach matches well with parallel processing, we further implement a parallel algorithm on the MapReduce framework. In experiments using various graph data, we confirmed that the proposed algorithm finds the densest subgraphs in fewer steps, as compared to other related studies. It also scales efficiently for many given starting nodes.

Squall: A Real-time Big Data Processing Framework based on TMO Model for Real-time Events and Micro-batch Processing

http://doi.org/

Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this paper, we propose a Squall framework using Time-triggered Message-triggered Object (TMO) technology, a model that is widely used for processing real-time big data. Moreover, we provide a description of Squall framework and its operations under a single node. TMO is an object model that supports the non-regular real-time processing method for certain conditions as well as regular periodic processing for certain amount of time. A Squall framework can support the real-time event stream of big data and micro-batch processing with outstanding performances, as compared to Apache storm and Spark Streaming. However, additional development for processing real-time stream under multiple nodes that is common under most frameworks is needed. In conclusion, the advantages of a TMO model can overcome the drawbacks of Apache storm or Spark Streaming in the processing of real-time big data. The TMO model has potential as a useful model in real-time big data processing.

A Video Quality Adaptation Algorithm to Improve QoE for HTTP Adaptive Streaming Service

Myoungwoo Kim, Kwangsue Chung

http://doi.org/

HTTP adaptive streaming has recently emerged to handle the rapidly growing traffic and to provide high quality multimedia contents. To improve the QoE (Quality of Experience) for HTTP adaptive streaming service, the average video bitrate should be maximized, and the video switching frequency (difference of bitrate between adjacent segments) and video stalling events need to be minimized. The recently proposed quality adaptation algorithms for HTTP adaptive streaming do not provide high QoE, since detailed QoE factors such as video switching frequency and bitrate difference of adjacent segments, are not considered. In this paper, we propose a SQA (Smooth Quality Adaptation) algorithm to improve the user QoE. The proposed algorithm provides the smoothed QoE, such that it minimizes the unnecessary video switching events by maintaining the quality in a certain period, thus minimizing the bitrate difference of adjacent segments. Through simulation, we confirm that the proposed algorithm reduces the unnecessary switching events, and prevents the sudden decrease in video quality.

A Real-time Multicasting Protocol using Time Deadline in Wireless Sensor Networks

Cheonyong Kim, Taehun Yang, Sangdae Kim, Hyunchong Cho, Sang-Ha Kim

http://doi.org/

Real-time multicasting is a packet transmission scheme ensuring that multiple destinations receive a packet within the desired time line. In wireless sensor networks, a packet can be delivered to a limited distance under a given deadline, since the end-to-end delay tends to be proportional to the end-to-end physical distance. Existing real-time multicasting protocols select the distance between the source and the furthest destination as the distance limitation and construct a multicasting tree guaranteeing delivery paths to each destination within the distance limitation. However, the protocols might lead to real-time delivery failures and energy efficiency degradation due to the fixed distance limitation. In this study, we proposed a real-time multicasting protocol using time deadline. The proposed protocol obtains the maximum transmittable distance with a given time deadline and subsequently constructs a multicasting tree using the maximum transmittable distance. The form of the multicasting tree varies according to the given time deadline to trade off the energy efficiency against the real-time delivery success ratio. The simulation results showed that the proposed scheme is superior to the existing protocols in terms of energy efficiency and real-time delivery success ratio under various time deadlines.