Computational intelligence based intrusion detection systems for wireless communication and pervasive computing networks

The emerging trend of ubiquitous and pervasive computing aims at embedding everyday devices such as wrist-watches, smart phones, home video systems, autofocus cameras, intelligent vehicles, musical instruments, kitchen appliances etc. with microprocessors and imparts them with wireless communication capability. This advanced computing paradigm, also known as the Internet of Things or cyber-physical computing, leads internet and computing to appear everywhere and anywhere using any device and location. With maximum appreciation and due regards to the evolutionary arc, depth and scope of ceaseless internet utilities, it is equally necessary to envisage the security and data confidentiality challenges posed by the free and ubiquitous availability of internet. Wireless communication, by virtue of a plethora of networked devices, is severely prone to illegal use, unauthorized access, protocol tunneling, eavesdropping, and denial of service attacks as these devices are unknowingly exposed to illegal access from undefined locations. Amidst the rapidly expanding arena of cybercrime, banks, stock exchanges, business transactions and shopping firms such as Amazon and eBay are heavily dependent on internet. The freedom offered by wireless and 3G based internet communication and its open character has led to many incidences of abuse of technology. Unrestricted accessibility to internet has intensified the likelihood of sophisticated attacks, malicious intrusions and malware, capable of inflicting widespread damage on modern human life and economy. The classical intrusion detection systems have been found to be less equipped to handle the magnitude and complexity of wireless networks due to enormous user activities and constantly varying behavior patterns. This paper analyses the role of computational intelligence techniques to design adaptive and cognitive intrusion detection systems that can efficiently detect malicious network activities and proposes novel three-tier architecture for designing intelligent intrusion detection systems for wireless networks.