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Online banking has become increasingly important to the profitability of financial institutions as well as adding convenience for their customers. As the number of customers using online banking increases, online banking systems are becoming more desirable targets for criminals to attack. To maintain their customers' trust and confidence in the security of their online bank accounts, financial institutions must identify how attackers compromise accounts and develop methods to protect them. Attack trees and protection trees are a cost effective way to do this. Attack trees highlight the weaknesses in a system and protection trees provide a methodical means of mitigating these weaknesses. In this paper, a notional online banking system is analyzed and protection solutions are proposed for varying budgets.

In the search for a robust and efficient algorithm to be used for computer virus detection, we have developed an artificial immune system genetic algorithm (REALGO) based on the human immune system's use of reverse transcription ribonucleic acid (RNA). The REALGO algorithm provides memory such that during a complex search the algorithm can revert back to and attempt to mutate in a different "direction" in order to escape local minima. In lieu of non-existing virus generic templates, validation is addressed by using an appropriate variety of function optimizations with landscapes believed to be similar to that ofvirus detection. It is empirically shown that the REALGO algorithm finds "better" solutions than other evolutionary strategies in four out of eight test functions and finds equally "good" solutions in the remaining four optimization problems.

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