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- Title
- Application level intrusion detection using a sequence learning algorithm.
- Creator
- Dong, Yuhong., Florida Atlantic University, Hsu, Sam, Rajput, Saeed, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
An un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed...
Show moreAn un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed approach, a hash table is used to store a sparse data matrix in triple data format that is collected from a web transition log instead of an n-by-n dimension matrix. Furthermore, in GSLA, the sequence learning matrix can be dynamically changed according to a different volume of data sets. Therefore, this approach is more efficient, easy to manipulate, and saves memory space. To validate the effectiveness of the algorithm, extensive simulations have been conducted by applying the GSLA algorithm to the homework submission system at our computer science and engineering department. The performance of GSLA is evaluated and compared with traditional Markov Model (MM) and K-means algorithms. Specifically, three major experiments have been done: (1) A small data set is collected as a sample data, and is applied to GSLA, MM, and K-means algorithms to illustrate the operation of the proposed algorithm and demonstrate the detection of abnormal behaviors. (2) The Random Walk-Through sampling method is used to generate a larger sample data set, and the resultant anomaly score is classified into several clusters in order to visualize and demonstrate the normal and abnormal behaviors with K-means and GSLA algorithms. (3) Multiple professors' data sets are collected and used to build the normal profiles, and the ANOVA method is used to test the significant difference among professors' normal profiles. The GSLA algorithm can be made as a module and plugged into the IDS as an anomaly detection system.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/12220
- Subject Headings
- Data mining, Parallel processing (Electronic computers), Computer algorithms, Computer security, Pattern recognition systems
- Format
- Document (PDF)
- Title
- Event detection in surveillance video.
- Creator
- Castellanos Jimenez, Ricardo Augusto., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video...
Show moreDigital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video sequences. This thesis presents a surveillance system based on optical flow and background subtraction concepts to detect events based on a motion analysis, using an event probability zone definition. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction to a predefined condition or running in the scene, with precision greater than 50% and recall greater than 80%.
Show less - Date Issued
- 2010
- PURL
- http://purl.flvc.org/FAU/1870694
- Subject Headings
- Computer systems, Security measures, Image processing, Digital techniques, Imaging systems, Mathematical models, Pattern recognition systems, Computer vision, Digital video
- Format
- Document (PDF)
- Title
- Patterns for secure interactions in social networks in Web 2.0.
- Creator
- Marin, Carolina, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A social network is a structure of individuals and organizations, which are connected by one or more types of interdependency, such as friendship, affinity, common interests or knowledge. Social networks use Web 2.0 technology, which is mostly based on a service-oriented architecture. We are studying patterns for social networks in this environment. A pattern is an encapsulated solution to a software problem in a given context, secure threats are possible in this context. We present a...
Show moreA social network is a structure of individuals and organizations, which are connected by one or more types of interdependency, such as friendship, affinity, common interests or knowledge. Social networks use Web 2.0 technology, which is mostly based on a service-oriented architecture. We are studying patterns for social networks in this environment. A pattern is an encapsulated solution to a software problem in a given context, secure threats are possible in this context. We present a collection of patterns associated with the most important aspects of social networks, with emphasis on controlling the actions of the users of these networks.
Show less - Date Issued
- 2012
- PURL
- http://purl.flvc.org/FAU/3342203
- Subject Headings
- Web 2.0, Computer network architectures, Online social networks, Security measures, Social media, Pattern recognition systems
- Format
- Document (PDF)