Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats. Discover novel research into the uses of machine-learning principles and algorithms to detect and prevent phishing attacks Help your business or organization avoid costly damage from phishing sources Gain insight into machine-learning strategies for facing a variety of information security threats
Mobile Wireless Ad Hoc Networks (MANET) is non-centralized wireless networks that can be formulated without the need for any pre-existing infrastructure in which each node can act as a router. It must discover its local neighbours and through them it will communicate to nodes that are out of its transmission range. Various features like open medium, dynamic topology, lack of clear lines of defence, makes MANET vulnerable to security attacks. Ad hoc On-demand Distance Vector routing (AODV) is one of the best and popular routing algorithms. AODV is severely affected by well-known black hole attack in which a malicious node injects a faked route reply message that it has a fresh route to destination. In this book, MANET performance against single black hole attack has compared with its performance against multiple black hole attacks by using Intrusion Detection System (IDSAODV) routing protocol (Dokurer, 2006). The result are analysed using NS-2.35, through various network parameter bases: total drop packets, end to end delay, packet delivery ratio and routing request overhead. The results indicate IDSAODV solution method which is presented for single black hole attack before, can be used effectively for decreasing total drop packets and improving packet delivery ratio against multiple black hole attacks, also. But, the method doesn't have significant effect for improving end to end delay and routing request overhead.
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