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A media‐access‐control (MAC) table of switches is used to store the MAC addresses of stations in a local area network (LAN) segment to enable frame forwarding. Each incoming frame is broadcast to all switch ports through a switch backplane when an MAC address is not registered in the MAC table. If an address is registered, the switch forwards the frame to the port connected to the destination host. An MAC table overflow (MTO) vulnerability attack causes the MAC table of all switches to overflow in an LAN segment, and all incoming frames are broadcast to every port in the switch. The attack degrades switch‐based LANs (each port of a switch comprises an individual operating domain and switch bandwidth) to bus‐based LANs (all ports are bounded to one operating domain and share a bandwidth similarly to a hub), causing information leakages and reducing the effective bandwidth; a virtual LAN configuration can reduce but not eliminate the associated damage. This paper presents the security effect of an MTO vulnerability attack, and a novel per‐port‐based MAC table design is proposed to solve this type of vulnerability. The experimental results indicate that the mechanism of the proposed design eliminates the damage caused by such attacks. Copyright © 2014 John Wiley & Sons, Ltd.

Advancements in the field of information technology have resulted in an increase in the speed and amount of data generated. This has resulted in traditional association rules algorithms, such as the Apriori and Frequent Pattern Growth (FPGrowth) algorithms, no longer being able to rapidly explore valuable knowledge in big data. Nowadays, parallel computing with technologies such as MapReduce is commonly used to reduce execution times. Because the FP-Growth algorithm uses an FPTree to mine itemsets, decomposing its structure into subtasks is difficult . We developed a method that combines the Apriori and FP-Growth algorithms with MapReduce to rectify this problem. In experiments conducted, we varied the block size of the Mapper to achieve execution performance better than those of the Apriori and FP-Growth algorithms.

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