Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Mangrove production and carbon sinks: A revision of global budget estimates

Mangrove forests are highly productive but globally threatened coastal ecosystems, whose role in the carbon budget of the coastal zone has long been debated. Here we provide a comprehensive synthesis of the available data on carbon fluxes in mangrove ecosystems. A reassessment of global mangrove primary production from the literature results in a conservative estimate of ∼218 ± 72 Tg C a−1. When using the best available estimates of various carbon sinks (organic carbon export, sediment burial, and mineralization), it appears that >50% of the carbon fixed by mangrove vegetation is unaccounted for. This unaccounted carbon sink is conservatively estimated at ∼112 ± 85 Tg C a−1, equivalent in magnitude to ∼30–40% of the global riverine organic carbon input to the coastal zone. Our analysis suggests that mineralization is severely underestimated, and that the majority of carbon export from mangroves to adjacent waters occurs as dissolved inorganic carbon (DIC). CO2 efflux from sediments and creek waters and tidal export of DIC appear to be the major sinks. These processes are quantitatively comparable in magnitude to the unaccounted carbon sink in current budgets, but are not yet adequately constrained with the limited published data available so far.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Inventing indigenous religious belief and practice within the spaces of Ghanaian Pentecostalism : the Mame Wata healing churches of Half Assini

Goddesses in African religions are spirits that affect humans and demand reverence from them. They are also embodiments of ideas that African people have about women, their powers and their roles in society. This study focused on Mame Wata, a goddess in Half Assini, an Nzema-speaking coastal community in western Ghana. It sought to resolve a paradox, that is, the fact that, the goddess is at the center of a Pentecostalist tradition even though traditional Pentecostalism in Ghana views her as an agent of the devil. The study involved fieldwork in this community of the goddess's female worshippers led by Agyimah, a charismatic man, and an agent of the goddess. The study interpreted the goddess as a post-colonial invented symbol personifying both pre-colonial and emerging ideas about female power. Findings from the study also show that through Mame Wata the followers celebrate the spirituality of the female.

Jane Smiley's "A Thousand Acres": A Feminist Revision of "King Lear"

Jane Smiley retells the tale of “King Lear” through the perspective of one of the evil sisters, in her novel “A Thousand Acres”. While the literary canon places William Shakespeare and his plays at the top of the list, I disagree that the canon should denote what is considered “classic” and what would be disregarded. Jane Smiley's novel is not canonized, but why? Her feminist revision of “King Lear” answers why Goneril and Regan were so evil. I argue that “King Lear” (both the text and the play) does not provide the evidence of dysfunction that Smiley's novel exhibits. “A Thousand Acres” opens up questions about gender formation, issues that are misrepresented and occluded in Shakespeare's “King Lear”. By bringing the trauma of incest to the forefront of the novel, its reverse emotional structures allow the reader to obtain a new perspective to a complex four-century-old play.