Today's anomaly-based network intrusion detection systems (IDSs) are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching the problem of detecting these attacks using multi-layered feed forward neural network (MLFFNN) IDSs. The scope of the paper focused on a review of the literature from primarily 2008 to the present found in peer-review and scholarly journals. A key word search was used to compare and contrast the literature to find strengths, weaknesses and gaps. The significance of the research found that further work is needed to improve the performance and convergence rates of MLFFNN IDSs. This literature review contributes to the area of intrusion detection by looking at the effects of architecture, algorithms, and input data on the performance and convergence rates of MLFFNN IDSs.
Advanced persistent threats (APTs) have become a big problem for computer systems. Databases are vulnerable to these threats and can give attackers access to an organizations sensitive data. Oracle databases are at greater risk due to their heavy use as back-ends to corporate applications such as enterprise resource planning software. This chapter will describe a methodology for finding APTs hiding or operating deep within an Oracle database system. Using an understanding of Oracle normal operations provides a baseline to assist in discovering APT behavior. Incorporating these and other techniques such as database activity monitoring, machine learning, neural networks and honeypots/tokens can create a database intrusion detection system capable of finding these threats.
Advanced persistent threats (APTs) have become a big problem for computer systems. Databases are vulnerable to these threats and can give attackers access to an organizations sensitive data. Oracle databases are at greater risk due to their heavy use as back-ends to corporate applications such as enterprise resource planning software. This paper will describe a methodology for finding APTs that may be hiding or operating deep within an Oracle database system. Using a deep understanding of Oracle normal operations provides a baseline to assist in discovering APT behavior. Incorporating these into a database intrusion detection system can raise the ability for finding these threats.
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