Current Search: Lugo-Cordero, Hector (x)
View All Items
- Title
- Enhancing Cognitive Algorithms for Optimal Performance of Adaptive Networks.
- Creator
-
Lugo-Cordero, Hector, Guha, Ratan, Wu, Annie, Stanley, Kenneth, University of Central Florida
- Abstract / Description
-
This research proposes to enhance some Evolutionary Algorithms in order to obtain optimal and adaptive network configurations. Due to the richness in technologies, low cost, and application usages, we consider Heterogeneous Wireless Mesh Networks. In particular, we evaluate the domains of Network Deployment, Smart Grids/Homes, and Intrusion Detection Systems. Having an adaptive network as one of the goals, we consider a robust noise tolerant methodology that can quickly react to changes in...
Show moreThis research proposes to enhance some Evolutionary Algorithms in order to obtain optimal and adaptive network configurations. Due to the richness in technologies, low cost, and application usages, we consider Heterogeneous Wireless Mesh Networks. In particular, we evaluate the domains of Network Deployment, Smart Grids/Homes, and Intrusion Detection Systems. Having an adaptive network as one of the goals, we consider a robust noise tolerant methodology that can quickly react to changes in the environment. Furthermore, the diversity of the performance objectives considered (e.g., power, coverage, anonymity, etc.) makes the objective function non-continuous and therefore not have a derivative. For these reasons, we enhance Particle Swarm Optimization (PSO) algorithm with elements that aid in exploring for better configurations to obtain optimal and sub-optimal configurations. According to results, the enhanced PSO promotes population diversity, leading to more unique optimal configurations for adapting to dynamic environments. The gradual complexification process demonstrated simpler optimal solutions than those obtained via trial and error without the enhancements.Configurations obtained by the modified PSO are further tuned in real-time upon environment changes. Such tuning occurs with a Fuzzy Logic Controller (FLC) which models human decision making by monitoring certain events in the algorithm. Example of such events include diversity and quality of solution in the environment. The FLC is able to adapt the enhanced PSO to changes in the environment, causing more exploration or exploitation as needed.By adding a Probabilistic Neural Network (PNN) classifier, the enhanced PSO is again used as a filter to aid in intrusion detection classification. This approach reduces miss classifications by consulting neighbors for classification in case of ambiguous samples. The performance of ambiguous votes via PSO filtering shows an improvement in classification, causing the simple classifier perform better the commonly used classifiers.
Show less - Date Issued
- 2018
- Identifier
- CFE0007046, ucf:52003
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007046
- Title
- Pervasive Secure Content Delivery Networks Implementation.
- Creator
-
Lugo-Cordero, Hector, Guha, Ratan, Stanley, Kenneth, Chatterjee, Mainak, Wu, Annie, Lu, Kejie, University of Central Florida
- Abstract / Description
-
Over the years, communication networks have been shifting their focus from providing connectivity in a client/server model to providing a service or content. This shift has led to topic areas like Service-Oriented Architecture (SOA), Heterogeneous Wireless Mesh Networks, and Ubiquitous Computing. Furthermore, probably the broadest of these areas which embarks all is the Internet of Things (IoT). The IoT is defined as an Internet where all physical entities (e.g., vehicles, appliances, smart...
Show moreOver the years, communication networks have been shifting their focus from providing connectivity in a client/server model to providing a service or content. This shift has led to topic areas like Service-Oriented Architecture (SOA), Heterogeneous Wireless Mesh Networks, and Ubiquitous Computing. Furthermore, probably the broadest of these areas which embarks all is the Internet of Things (IoT). The IoT is defined as an Internet where all physical entities (e.g., vehicles, appliances, smart phones, smart homes, computers, etc.), which we interact daily are connected and exchanging data among themselves and users. The IoT has become a global goal for companies, researchers, and users alike due to its different implementation and functional benefits: performance efficiency, coverage, economic and health. Due to the variety of devices which connect to it, it is expected that the IoT is composed of multiple technologies interacting together, to deliver a service. This technologies interactions renders an important challenge that must be overcome: how to communicate these technologies effectively and securely? The answer to this question is vital for a successful deployment of IoT and achievement of all the potential benefits that the IoT promises.This thesis proposes a SOA approach at the Network Layer to be able to integrate all technologies involved, in a transparent manner. The proposed set of solutions is composed of primarily the secure implementation of a unifying routing algorithm and a layered messaging model to standardizecommunication of all devices. Security is targeted to address the three main security concerns (i.e., confidentiality, integrity, and availability), with pervasive schemes that can be employed for any kind of device on the client, backbone, and server side. The implementation of such schemes is achieved by standard current security mechanisms (e.g., encryption), in combination with novel context and intelligent checks that detect compromised devices. Moreover, a decentralized content processing design is presented. In such design, content processing is handled at the client side, allowing server machines to serve more content, while being more reliable and capable of processing complete security checks on data and client integrity.
Show less - Date Issued
- 2017
- Identifier
- CFE0006620, ucf:51268
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006620