Current Search:   Calvert, Chad (x)

View All Items

  • CSV Spreadsheet
(1 - 2 of 2)
DATA COLLECTION FRAMEWORK AND MACHINE LEARNING ALGORITHMS FOR THE ANALYSIS OF CYBER SECURITY ATTACKS
Title
DATA COLLECTION FRAMEWORK AND MACHINE LEARNING ALGORITHMS FOR THE ANALYSIS OF CYBER SECURITY ATTACKS.
Creator
Calvert, Chad, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors...
Show moreThe integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as compared to normal traffic. This skewed distribution between normal and attack data can lead to high levels of class imbalance. Machine learning techniques can be used to aid in attack detection, but large levels of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results.
Show less
Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013289
Subject Headings
Machine learning, Algorithms, Anomaly detection (Computer security), Intrusion detection systems (Computer security), Big data
Format
Document (PDF)
integrated component selection framework for system level design
Title
An integrated component selection framework for system level design.
Creator
Calvert, Chad., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The increasing system design complexity is negatively impacting the overall system design productivity by increasing the cost and time of product development. One key to overcoming these challenges is exploiting Component Based Engineering practices. However it is a challenge to select an optimum component from a component library that will satisfy all system functional and non-functional requirements, due to varying performance parameters and quality of service requirements. In this thesis...
Show moreThe increasing system design complexity is negatively impacting the overall system design productivity by increasing the cost and time of product development. One key to overcoming these challenges is exploiting Component Based Engineering practices. However it is a challenge to select an optimum component from a component library that will satisfy all system functional and non-functional requirements, due to varying performance parameters and quality of service requirements. In this thesis we propose an integrated framework for component selection. The framework is a two phase approach that includes a system modeling and analysis phase and a component selection phase. Three component selection algorithms have been implemented for selecting components for a Network on Chip architecture. Two algorithms are based on a standard greedy method, with one being enhanced to produce more intelligent behavior. The third algorithm is based on simulated annealing. Further, a prototype was developed to evaluate the proposed framework and compare the performance of all the algorithms.
Show less
Date Issued
2009
PURL
http://purl.flvc.org/FAU/368608
Subject Headings
High performance computing, Computer architecture, Engineering design, Data processing, Computer-aided design
Format
Document (PDF)