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SESSION-BASED INTRUSION DETECTION SYSTEM TO MAP ANOMALOUS NETWORK TRAFFIC
Title
SESSION-BASED INTRUSION DETECTION SYSTEM TO MAP ANOMALOUS NETWORK TRAFFIC.
Creator
Caulkins, Bruce, Wang, Morgan, University of Central Florida
Abstract / Description
Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal – firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well...
Show moreComputer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal – firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomaly-based ID systems determine what is normal traffic within a network and reports abnormal traffic behavior. This paper will describe a methodology towards developing a more-robust Intrusion Detection System through the use of data-mining techniques and anomaly detection. These data-mining techniques will dynamically model what a normal network should look like and reduce the false positive and false negative alarm rates in the process. We will use classification-tree techniques to accurately predict probable attack sessions. Overall, our goal is to model network traffic into network sessions and identify those network sessions that have a high-probability of being an attack and can be labeled as a "suspect session." Subsequently, we will use these techniques inclusive of signature detection methods, as they will be used in concert with known signatures and patterns in order to present a better model for detection and protection of networks and systems.
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Date Issued
2005
Identifier
CFE0000906, ucf:46762
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000906
DATA MINING METHODS FOR MALWARE DETECTION
Title
DATA MINING METHODS FOR MALWARE DETECTION.
Creator
Siddiqui, Muazzam, Wang, Morgan, University of Central Florida
Abstract / Description
This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to...
Show moreThis research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares.
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Date Issued
2008
Identifier
CFE0002303, ucf:47870
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002303
rf power amplifier and oscillator design for reliability and variability
Title
rf power amplifier and oscillator design for reliability and variability.
Creator
Chen, Shuyu, Yuan, Jiann-Shiun, Sundaram, Kalpathy, Shen, Zheng, Gong, Xun, Wang, Morgan, University of Central Florida
Abstract / Description
CMOS RF circuit design has been an ever-lasting research field. It gained so much attention since RF circuits have high mobility and wide band efficiency, while CMOS technology has the advantage of low cost and better capability of integration. At the same time, IC circuits never stopped scaling down for the recent many decades. Reliability issues with RF circuits have become more and more severe with device scaling down: reliability effects such as gate oxide break down, hot carrier...
Show moreCMOS RF circuit design has been an ever-lasting research field. It gained so much attention since RF circuits have high mobility and wide band efficiency, while CMOS technology has the advantage of low cost and better capability of integration. At the same time, IC circuits never stopped scaling down for the recent many decades. Reliability issues with RF circuits have become more and more severe with device scaling down: reliability effects such as gate oxide break down, hot carrier injection, negative bias temperature instability, have been amplified as the device size shrinks. Process variability issues also become more predominant as the feature size decreases. With these insights provided, reliability and variability evaluations on typical RF circuits and possible compensation techniques are highly desirable.In this work, a class E power amplifier is designed and laid out using TSMC 0.18 (&)#181;m RF technology and the chip was fabricated. Oxide stress and hot electron tests were carried out at elevated supply voltage, fresh measurement results were compared with different stress conditions after 10 hours. Test results matched very well with mixed mode circuit simulations, proved that hot carrier effects degrades PA performances like output power, power efficiency, etc. Self- heating effects were examined on a class AB power amplifier since PA has high power operations. Device temperature simulation was done both in DC and mixed mode level. Different gate biasing techniques were analyzed and their abilities to compensate output power were compared. A simple gate biasing circuit turned out to be efficient to compensate self-heating effects under different localized heating situations. Process variation was studied on a classic Colpitts oscillator using Monte-Carlo simulation. Phase noise was examined since it is a key parameter in oscillator. Phase noise was modeled using analytical equations and supported by good match between MATLAB results and ADS simulation. An adaptive body biasing circuit was proposed to eliminate process variation. Results from probability density function simulation demonstrated its capability to relieve process variation on phase noise. Standard deviation of phase noise with adaptive body bias is much less than the one without compensation. Finally, a robust, adaptive design technique using PLL as on-chip sensor to reduce Process, Voltage, Temperature (P.V.T.) variations and other aging effects on RF PA was evaluated. The frequency and phase of ring oscillator need to be adjusted to follow the frequency and phase of input in PLL no matter how the working condition varies. As a result, the control signal of ring oscillator has to fluctuate according to the working condition, reflecting the P.V.T changes. RF circuits suffer from similar P.V.T. variations. The control signal of PLL is introduced to RF circuits and converted to the adaptive tuning voltage for substrate bias. Simulation results illustrate that the PA output power under different variations is more flat than the one with no compensation. Analytical equations show good support to what has been observed.
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Date Issued
2013
Identifier
CFE0004664, ucf:49894
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004664