Adaptive Anomaly / Intrusion Detection and Mitigation Systems for High-speed Wireless Networks

Traffic anomalies and attacks are commonplace in today’s networks, and identifying them rapidly and accurately is critical for large network operators. It was estimated that malicious code (viruses, worms and Trojan horses) caused over $28 billion in economic losses in 2003, and will grow to over $75 billion in economic losses by 2007 [11]. Meanwhile, the broadband wireless networks, represented by the emerged IEEE 802.16 standards, is gaining its popularity and will be seamlessly integrated into the current Internet. Such high-speed wireless network may even be connected to the regional backbone directly to construct the wireless metropolitan-area network (MAN). Thus such wireless network will be exposed to all the attacks, viruses and worms in the current Internet. Like the 802.11 wireless LAN, 802.16 network is facing scrutiny on its security mechanisms. Driven by customers’ security awareness and demand, intrusion detection and security management tools become essential part of current 802.11 product offering suite. Although there are a lot of existing 802.11 intrusion detection products, they mostly target to detect denial-of-service attacks caused by the WEP authentication vulnerability, e.g., Airespace [6]. Existing IDSs for wired network have various shortcomings when they are applied to broadband wireless MAN as discussed below [5, 10, 13–15, 17, 20]. Furthermore, there is little intrusion detection research done tailored to 802.16 and beyond 3G. First, they are mostly host-based and not scalable to high-speed networks. However, nowadays rapid propagation of viruses/worms can infect most vulnerable machines in the Internet in only ten minutes [12], and even 30 seconds with advanced techniques [18]. Thus it is crucial to identify such outbreaks in their early phases, which can only be possibly achieve d by detection at the base stations or routers provided by the network infrastructures instead of at end hosts [23]. In fact, it is very hard to implement certain detection techniques, such as those for port scanning, at end hosts. The end hosts, especially for wireless devices, may have various weak computational/power limits to do advanced detection. Thus to augment the wireless MAN infrastructure with accurate intrusion detection and mitigation system can significantly attract the subscribers. However, the existing schemes are not scalable to the link speeds and number of flows for high-speed 802.16 wireless MAN as illustrated below. Second, they are mostly signature-based and unable to adaptively recognize flow-level unknown attacks. Most of them can only detect known attacks with signatures, but not unknown new attacks. Also, attackers can easily evade such IDSs by garbling signatures. Statistical IDSs are therefore proposed to detect anomalous behaviors [2, 7, 8, 21, 22]. To enable accurate detection and attack mitigation, the detection needs to be executed at flow level. Given a spoofed TCP SYN flooding attack sending 40-byte packet streams to a 802.16 network which can provide up to 134Mbps bandwidth, each packet may be considered as a flow, and even to record 10-minute traffic in memory for analysis will take more than 4GB. Thus existing flow-level schemes themselves are vulnerable to attacks [7, 13, 14]. In addition, the statistical parameters are often manually set, and hard to adapt to the traffic pattern changes. However, wireless networks often have transient connections which makes it a big challenge to differentiate collisions, interference, and real attacks. Third, they cannot differentiate malicious events from the unintentional anomalies. Such unintentional anomalies may be caused by network element (e.g.base stations, routers or links) faults. The statistical IDS cannot tell it from malicious attacks, and thus have high false positive rates.