Development of a rhino anti-poaching model for game farms and nature reserves in the Free State Province of South Africa

Thesis (Doctor of Technology in Agriculture) -- Central University of Technology, Free State, 2018During the last ten years, the rhino populations of South Africa have suffered under an intense poaching onslaught. This onslaught has moved to the Free State Province of South Africa and there is a justified concern that its rhino populations are at risk. To protect the rhinos in the Province a need exists to manage the risk of poaching through a practical rhino anti-poaching model, which can assist to protect them effectively. This Rhino Anti-Poaching Model can determine and predict a poaching risk, can identify weaknesses, can assist to address problem areas, and will enable efficient monitoring. It is imperative to know which rhino species occurred in the Free State Province in historical times, to ensure that the correct rhino species is protected against the risk of poaching and that they be kept in a suitable habitat. Nine farms in the Free Sate have rhino as a prefix in their names, which may be an indication that rhino did occur in the Province. Various historical photos and literature give a retrospective view, and show that there was insufficient browsing vegetation for black rhinos to survive. The occurrence of open grassland however suggests that white rhinos did occur in those specific areas during the time of the first pioneers. During this study period a count of rhinos was done, and currently there are 669 white rhinos and 11 black rhinos in the Province, thus a total of 680 rhinos in the Free State Province. The South African Constitution mandated the State to enforce measures that will ensure adequate environmental protection for the benefit of future generations. The South African government promulgated a myriad of new environmental legislation. Several international agreements were also introduced as enforcement tools to regulate rhinos. The enforcement measures are noble, but out of balance, with many inspectors, but too few law enforcement officials. It also over-regulates and ensnare officials in minor issues. A brief discussion on the latest technological innovations gives insight to the purpose of its development and effectiveness in combating rhino poaching. There are currently no technical or strategic solutions to save rhinos from poaching, therefore a combination of techniques is needed. Although self-manufactured unconventional devices seem to be more efficient to deceive poachers, a combination with the latest technological equipment and conventional strategies might be the best solution to counter rhino poaching. Free State rhino farmers own 90% of the Province’s rhinos. However, these rhino farmers lack the appropriate security measures required to adequately protect their rhinos. A questionnaire was developed and data were collected, providing statistics on the current stance of security measures on rhino sites in the Province. It showed that 80% of rhino poaching occurs in rhino camps bordering public roads, 69% of the rhino sites located within 20km from the nearest town reported poaching, and 77% of large rhino camps are prone to poaching incidents. 57% of the respondents experienced rhino poaching on their sites. Through the questionnaire, it was gathered that rhino farmers in the Province are not vested in the concept of using trained rhino security. A Production Loss Formula was constructed that calculates the production loss of a poached rhino. This formula reflects the reality experienced by the breeder. It indicated that the Province lost almost R300 million due to the poaching of 60 rhinos and that a poached breeding bull scores a higher production loss than all other gender groups. The Production Loss Formula also indicated that the State had a larger mean amount of loss compared to the rhino farmers, despite the lower bull per cow ratio owned by the State. As part of the Rhino Anti-Poaching Model, a spreadsheet Formula was developed to calculate the total poaching risk percentage of each rhino site in the Province. Subsequently, an average rhino-poaching risk of almost 65% was obtained. After a rectification of the risks, the average poaching risk was reduced to nine percent and 10 sites scored below 50% (versus the current four). The statistical analysis indicates that the most important predictors for number of rhinos poached in the Free State Province were the Rhino Camp and Rhino Population categories

Bankrupting DoS Attackers

To defend against denial-of-service (DoS) attacks, we employ a technique called resource burning (RB). RB is the verifiable expenditure of a resource, such as computational power, required from clients before receiving service from the server. To the best of our knowledge, we present the first DoS defense algorithms where the algorithmic cost -- the cost to both the server and the honest clients -- is bounded as a function of the attacker's cost. We model an omniscient, Byzantine attacker, and a server with access to an estimator that estimates the number of jobs from honest clients in any time interval. We examine two communication models: an idealized zero-latency model and a partially synchronous model. Notably, our algorithms for both models have asymptotically lower costs than the attacker's, as the attacker's costs grow large. Both algorithms use a simple rule to set required RB fees per job. We assume no prior knowledge of the number of jobs, the adversary's costs, or even the estimator's accuracy. However, these quantities parameterize the algorithms' costs. We also prove a lower bound on the cost of any randomized algorithm. This lower bound shows that our algorithms achieve asymptotically tight costs as the number of jobs grows unbounded, whenever the estimator output is accurate to within a constant factor