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Effective Worm Detection for Various Scan Techniques 1 Effective Worm Detection for Various Scan Techniques Jianhong Xia, Sarma Vangala, Jiang Wu and Lixin Gao Department of Electrical and Computer Engineering University of Massachusetts at Amherst Amherst, MA 01003 fjxia, svangala, jiawu, [email protected] Kevin Kwiat Air Force Research Lab Information Directorate 525 Brooks Road, Rome, NY 13441 [email protected] Abstract— In recent years, the threats and damages vulnerable machines in the Internet within several hours caused by active worms have become more and more or even a few minutes. The Slammer worm [16] is an serious. In order to reduce the loss caused by fast- example of such worms that spread to all potential targets spreading active worms, an effective detection mechanism in less than 10 minutes. In order to find vulnerable to quickly detect worms is desired. In this paper, we first hosts, a worm can scan the entire IPv4 address space explore various scan strategies used by worms on finding vulnerable hosts. We show that targeted worms spread randomly. Such a mechanism of finding vulnerable hosts much faster than random scan worms. We then present a is called random scan. In this paper, we analyze various generic worm detection architecture to monitor malicious scan techniques that help worms spread faster than a worm activities. We propose and evaluate our detection random scan worm does. In particular, a worm that scans mechanism called Victim Number Based Algorithm. We IP address space selectively, for example, the only IP show that our detection algorithm is effective and able addresses used in the Internet, can spread faster than to detect worm events before 2% of vulnerable hosts a worm that randomly scans the entire IPv4 address are infected for most scenarios. Furthermore, in order to space. Slapper ([4], [8]) is an example of such worms reduce false alarms, we propose an integrated approach that have already caused considerable damage to the using multiple parameters as indicators to detect worm events. The results suggest that our integrated approach Internet. These methods reduce the time wasted on can differentiate worm attacks from DDoS attacks and scanning unallocated IP addresses and can dramatically benign scans. increase the spreading speed of worms. They are easy to implement and pose the most imminent threats to the Internet. I. INTRODUCTION In this paper, we first present and analyze the spread of Computer worms are self-propagating malicious codes worms that use various scan techniques. We then propose that spread across networks by exploiting security flaws and evaluate our worm detection algorithm, Victim Num- without human intervention. The threats of these worms ber Based Algorithm. We show that our algorithm can have kept increasing, leading to increasing management detect various worm scans before 2% of the vulnerable and maintenance costs. Recent studies ([5], [28], [22] hosts are infected for most scenarios. Since events such have shown that active worms could infect almost all as DDoS attacks or port scans may cause false alarms, we propose an integrated algorithm to reduce false Part of this work has been published in the 11th Annual Network and Distributed System Security Symposium (NDSS’04) [26]. This alarms using multiple parameters to differentiate worm work is supported in part by NSF Grant ANI-0208116, Alfred Sloan scans from DDoS attacks. These parameters include the Fellowship and Air Force Research Lab. Any opinions, findings, and number of destination addresses scanned and the traffic conclusions or recommendations expressed in this material are those volume observed in the detection networks. of the authors and do not necessarily reflect the views of National Science Foundation, Alfred Sloan Foundation or Air Force Research This paper is organized as follows. Section II intro- Lab. duces related work on worm modeling and detection. 2 Section III describes various possible scan techniques computer sends a SYN packet to new addresses. If so, and the impact of these techniques on worm spreading the packet will be delayed for a short period of time. speed. In particular, we discuss the class of random scan- These two methods can reduce the impact of false alarms ning worms including selective random scan and routable because they adopt the strategy of delaying connections scan. We also introduce a new worm scan method instead of dropping packets. Another example of delay- called divide-conquer scan, and analyze its spreading ing worm spread is the LaBrea tool [15] designed by speed. Section IV presents a generic worm detection Liston. This technique reduces the worm spreading speed architecture. Section V describes our Victim Number by holding TCP sessions with worm victims for a period Based Algorithm for worm detection. Section VI eval- of time. Spitzner [20] introduced the idea of Honeypots, uates the performance of our algorithm using traffic which are hosts that pretend to own a number of IP traces. To reduce false alarms, we propose an integrated addresses and passively monitor packets sent to them. By algorithm for worm detection using multiple parameters analyzing the scan packets, Honeypots can detect worm as indicators in Section VII. We summarize the paper in attacks and generate the signatures to fight back worm Section VIII. attacks. To detect worm attacks on enterprise networks, Che- ung [6] proposed an activity-graph based detection al- II. BACKGROUND AND RELATED WORK gorithm that uses the scan activity-graph inferred from There are a number of studies on analyzing worms and the traffic, in which the senders and receivers of packets their propagation. Weaver [23] proposed a fast spreading are the vertices and the relations among them are the worm, called the Warhol worm, that uses various scan edges. This method assumes that the activity graph can methods to find vulnerable hosts. Staniford et al. [22] be large for a very short period of time during worm systematically introduced the threats of worms and ana- attacks. Recent proposals for scan detection on enterprise lyzed some well-known worms. In addition, smarter scan networks include the works by Jung et al. ([12], [13]) methods such as localized subnet, hitlist and permutation and Staniford et al. ([24], [21]). Jung et al. [13] presented scans were discussed. These methods focus on improving an algorithm for portscan detection on enterprise net- the efficiency of the worm scan process. works using reverse sequential hypothesis testing. In this Several models were used to analyze the spreading algorithm, a random walk based mechanism observes of worms. Kephart and White [14] introduced an epi- the number of unsuccessful scans being sent out of the demiological model to measure the dynamics of virus enterprise network by a particular host. Hosts that do population. Zou et al. [28] proposed a two-factor worm not receive acknowledgements for a large number of model considering the factors on human countermea- connection attempts are contained by using a rate based sures and congestion caused by worm scan traffic. Chen blocking technique. This method is further augmented et al. [5] proposed a discrete time model which adopts and implemented by a hardware system in [24]. Staniford parameters such as scan rate, patching rate and death et al. [24] showed that their system is able to contain rate. By analyzing the factors that influence the spread of malicious activity before a fixed number of scans are sent worms, these models give insight into containing worm out of the enterprise network. Gu et al. [10] discussed propagation effectively. worm containment in local network using a small address Accurate detection and quick defense are always diffi- space. Dagon et al. [7] discuss a multi-parameter based cult problems for unprecedented worm attacks. There are worm detection mechanism using honeypots. a number of detection methods using Internet traffic mea- In terms of worm detection in the global Internet, surements to detect worms. Based on the locations where Zou et al. [27] explored the possibility of monitoring decisions are made on worm attacks, these methods can Internet traffic with a small address space to predict be classified into three categories, i.e., detect worms at the worm propagation in the Internet. They use Kalman end hosts, detect worms on enterprise networks, and filter based technique to detect the existence of worm detect worms in the global Internet. scans. However, Kalman filter based approach is found As an end host, a single computer can apply various to be sensitive to the selection of parameters such as strategies to detect and respond to worm attacks. So- monitoring time intervals [10]. mayaji et al. [19] proposed a method on worm detection Since the number of infected machines increases dra- and defense that uses a system call sequence database matically and exhibits the trend of exponential growth, to compare with new sequences. If a mismatch is found, we are able to monitor the change on the number of then the sequence is delayed. Williamson [25] proposed infected machines to detect worms. We call the infected a method, called Virus Throttle, that checks whether a machines observed in our monitoring networks victims. 3 5 x 10 5 where N is the total number of vulnerable machines in Weaver 4.5 AAWP the Internet; ­ is number of the addresses that a worm performs random scan; s is the scan rate (the number 4 of scan packets sent out by an infected machine per 3.5 time tick) and ni is the number of infected machines 3 up to time tick i. These notations are used consistently 2.5 throughout this paper. In Equation (1), the first term 2 on the right hand side denotes the number of infected 1.5 machines alive at the end of time tick i. The term, N¡ni, denotes the number of vulnerable machines not infected 1 Number of infected machines 1 sni by time tick i.
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