ecently, the explosive growth of wireless services and applications led to a shortage of radio spectrum. Since the Federal Communication Commission (FCC) approved unlicensed users to access the unused portion of the reserved spectrum (e.g. television channels) for wireless broadband services, various researchers have devoted much effort in designing cognitive radio networks (CRNs) to exploit this feature. CRNs are intelligent networks, which allow unlicensed users to use software radio for making the best use of the available/unused spectrum. While doing so, the unlicensed "cognitive" users should be transparent. In other words, they may not interfere with the primary users (i.e. the users for whom the system was originally designed for) in order to share the radio spectrum resource in CRNs such as those based on IEEE 802.22 wireless regional area network (WRAN) technology [1] . This radio spectrum sharing policy among the licensed and unlicensed users, however, opens up the possibility of various security threats. Indeed, a number of attacks have been studied in recent literature that target CRNs. Although some solutions have been presented to detect these attacks, to the best of our knowledge to date, a full-fledged intrusion detection system (IDS) has not yet been designed for combating the attacks against CRNs. The research work presented in [2] pioneered in addressing the need of IDS for CRNs as a second line of intrusion/attack detection in addition to the conventional cryptographic primitives for facilitating authentication and confidentiality. Even though the work in [2] defined some of the essential modules for designing an IDS for CRNs, it did not focus on specifying any lightweight detection algorithm. Having understood the lack of research work on the IDS-based defense for CRNs, we are motivated to design an effective IDS for deployment in the cognitive unlicensed users. Our proposed IDS uses cusumbased anomaly detection, which is lightweight and is able to discover previously unknown attacks with a significantly low detection latency. The remainder of the article is organized as follows. First, we survey a number of relevant research works on security threats against CRNs and some possible countermeasures. Then, we describe our considered CRN architecture and major security threats against the CRN. Next, we present our proposed IDS for the cognitive users of a CRN. Then, the performance of the proposed method is evaluated. Finally, the article is concluded. An introduction to the first wireless standard based upon CRNs is presented in the work by Cordeiro et al. [1] in 2006. The work demonstrated the prospect of CRN-based wireless communication by using the IEEE 802.22 WRAN technology. A detailed overview of the WRAN specifications, topology, service requirements, capacity, and applications were presented in the work. Most importantly, it specified the 802.22 system to comprise a fixed point-to-multipoint wireless air interface, in which a base station manages its own cell and all associated consumer premise equipment (CPE). Note that the word "cognitive/secondary users" replaced the term CPE in later literature. While Corderiro et al. provided the foundation for using the WRAN technology based on CRN, it was still an early time for their work to consider any of the WRAN/CRN security issues. R While cognitive radio networks (CRNs) present a promising solution to solve the scarcity of the radio spectrum, they are still susceptible to security threats. Until now, only a few researchers considered the use of intrusion detection systems (IDSs) to combat these threats against CRNs. In this article we describe a CRN based on IEEE wireless regional area network (WRAN) and describe some of the security threats against it. For the secondary users in the CRN to quickly detect whether they are being attacked, a simple yet effective IDS is then presented. Our proposal uses non-parametric cumulative sum (cusum) as the change point detection algorithm to discover the abnormal behavior due to attacks. Our proposed IDS adopts an anomaly detection approach and it profiles the CRN system parameters through a learning phase. So, our proposal is also able to detect new types of attacks. As an example, we present the case of detection of a jamming attack, which was not known to the IDS beforehand. The proposed IDS is evaluated through computer based simulations, and the simulation results clearly indicate the effectiveness of our proposal.
Another major threat, intrusion detection system REF is prevented.
Intrusion detection system (IDS) for combating attacks against cognitive radio networks
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