Your SlideShare is downloading. ×
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
2011   modeling and detection of camouflaging worm
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

2011 modeling and detection of camouflaging worm

932

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
932
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
16
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 1 Modeling and Detection of Camouflaging Worm Wei Yu, Xun Wang, Prasad Calyam, Dong Xuan, and Wei Zhao Abstract—Active worms pose major security threats to the Internet. This is due to the ability of active worms to propagate in an automated fashion as they continuously compromise computers on the Internet. Active worms evolve during their propagation and thus pose great challenges to defend against them. In this paper, we investigate a new class of active worms, referred to as Camouflaging Worm (C-Worm in short). The C-Worm is different from traditional worms because of its ability to intelligently manipulate its scan traffic volume over time. Thereby, the C-Worm camouflages its propagation from existing worm detection systems based on analyzing the propagation traffic generated by worms. We analyze characteristics of the C-Worm and conduct a comprehensive comparison between its traffic and non-worm traffic (background traffic). We observe that these two types of traffic are barely distinguishable in the time domain. However, their distinction is clear in the frequency domain, due to the recurring manipulative nature of the C-Worm. Motivated by our observations, we design a novel spectrum-based scheme to detect the C-Worm. Our scheme uses the Power Spectral Density (PSD) distribution of the scan traffic volume and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from background traffic. Using a comprehensive set of detection metrics and real-world traces as background traffic, we conduct extensive performance evaluations on our proposed spectrum-based detection scheme. The performance data clearly demonstrates that our scheme can effectively detect the C-Worm propagation. Furthermore, we show the generality of our spectrum-based scheme in effectively detecting not only the C-Worm, but traditional worms as well. Index Terms—Worm, Camouflage, Anomaly Detection. ✦ 1 I NTRODUCTION attacks of unprecedented scale [10]. For an adversary, super- An active worm refers to a malicious software program that botnets would also be extremely versatile and resistant to propagates itself on the Internet to infect other computers. The countermeasures. propagation of the worm is based on exploiting vulnerabilities Due to the substantial damage caused by worms in the of computers on the Internet. Many real-world worms have past years, there have been significant efforts on developing caused notable damage on the Internet. These worms include detection and defense mechanisms against worms. A network- “Code-Red” worm in 2001 [1], “Slammer” worm in 2003 [2], based worm detection system plays a major role by moni- and “Witty”/“Sasser” worms in 2004 [3]. Many active worms toring, collecting, and analyzing the scan traffic (messages to are used to infect a large number of computers and recruit identify vulnerable computers) generated during worm attacks. them as bots or zombies, which are networked together to form In this system, the detection is commonly based on the self- botnets [4]. These botnets can be used to: (a) launch massive propagating behavior of worms that can be described as Distributed Denial-of-Service (DDoS) attacks that disrupt the follows: after a worm-infected computer identifies and infects Internet utilities [5], (b) access confidential information that a vulnerable computer on the Internet, this newly infected can be misused [6] through large scale traffic sniffing, key computer1 will automatically and continuously scan several IP logging, identity theft etc, (c) destroy data that has a high addresses to identify and infect other vulnerable computers. As monetary value [7], and (d) distribute large-scale unsolicited such, numerous existing detection schemes are based on a tacit advertisement emails (as spam) or software (as malware). assumption that each worm-infected computer keeps scanning There is evidence showing that infected computers are being the Internet and propagates itself at the highest possible speed. rented out as “Botnets” for creating an entire black-market Furthermore, it has been shown that the worm scan traffic industry for renting, trading, and managing “owned” com- volume and the number of worm-infected computers exhibit puters, leading to economic incentives for attackers [4], [8], exponentially increasing patterns [2], [11], [12], [13], [14]. [9]. Researchers also showed possibility of “super-botnets,” Nevertheless, the attackers are crafting attack strategies that networks of independent botnets that can be coordinated for intend to defeat existing worm detection systems. In particular, ‘stealth’ is one attack strategy used by a recently-discovered • Wei Yu is with the Department of Computer and Information Sciences, active worm called “Atak” worm [15] and the “self-stopping” Towson University, Towson, MD 21252. E-mail: wyu@towson.edu. worm [16] circumvent detection by hibernating (i.e., stop • Xun Wang is with Cisco Systems Inc, San Jose, CA 95134. propagating) with a pre-determined period. Worm might also Email: xunwang.osu@gmail.com • Prasad Calyam is with OARnet, The Ohio State University, Columbus, OH use the evasive scan [17] and traffic morphing technique to 43210. Email: pcalyam@oar.net. hide the detection [18]. • Dong Xuan is with the Dept. of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210. Email: xuan@cse.ohio- This worm attempts to remain hidden by sleeping (suspend- state.edu. ing scans) when it suspects it is under detection. Worms that • Wei Zhao is with Department of Computer and Information Science, University of Macau, Macau, China. E-mail: weizhao@umac.mo. 1. In this paper, we interchangeably use the terms - worm-infected computer and worm instance. Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.Digital Object Indentifier 10.1109/TDSC.2010.13 1545-5971/10/$26.00 © 2010 IEEE
  • 2. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 2 adopt such smart attack strategies could exhibit overall scan Furthermore, we demonstrate the effectiveness of our traffic patterns different from those of traditional worms. Since spectrum-based detection scheme in comparison with existing the existing worm detection schemes will not be able to detect worm detection schemes. We define several new metrics. such scan traffic patterns, it is very important to understand Maximal Infection Ratio (MIR) is the one to quantify the such smart-worms and develop new countermeasures to defend infection damage caused by a worm before being detected. against them. Other metrics include Detection Time (DT) and Detection In this paper, we conduct a systematic study on a new Rate (DR). Our evaluation data clearly demonstrate that our class of such smart-worms denoted as Camouflaging Worm (C- spectrum-based detection scheme achieves much better detec- Worm in short). The C-Worm has a self-propagating behavior tion performance against the C-Worm propagation compared similar to traditional worms, i.e., it intends to rapidly infect with existing detection schemes. Our evaluation also shows as many vulnerable computers as possible. However, the C- that our spectrum-based detection scheme is general enough Worm is quite different from traditional worms in which it to be used for effective detection of traditional worms as well. camouflages any noticeable trends in the number of infected The remainder of the paper is organized as follows. In computers over time. The camouflage is achieved by manip- Section 2, we introduce the background and review the related ulating the scan traffic volume of worm-infected computers. work. In Section 3, we introduce the propagation model of Such a manipulation of the scan traffic volume prevents exhibi- the C-Worm. We present our spectrum-based detection scheme tion of any exponentially increasing trends or even crossing of against the C-Worm in Section 4. The performance evaluation thresholds that are tracked by existing detection schemes [19], results of our spectrum-based detection scheme is provided in [20], [21]. We note that the propagation controlling nature Section 5. We conclude this paper in Section 6. of the C-Worm (and similar smart-worms, such as “Atak”) cause a slow down in the propagation speed. However, by carefully controlling its scan rate, the C-Worm can: (a) still 2 BACKGROUND AND R ELATED WORK achieve its ultimate goal of infecting as many computers as 2.1 Active Worms possible before being detected, and (b) position itself to launch subsequent attacks [4], [5], [6], [7]. Active worms are similar to biological viruses in terms of their We comprehensively analyze the propagation model of the infectous and self-propagating nature. They identify vulnerable C-Worm and corresponding scan traffic in both time and computers, infect them and the worm-infected computers frequency domains. We observe that although the C-Worm propagate the infection further to other vulnerable computers. scan traffic shows no noticeable trends in the time domain, In order to understand worm behavior, we first need to model it demonstrates a distinct pattern in the frequency domain. it. With this understanding, effective detection and defense Specifically, there is an obvious concentration within a narrow schemes could be developed to mitigate the impact of the range of frequencies. This concentration within a narrow worms. For this reason, tremendous research effort has focused range of frequencies is inevitable since the C-Worm adapts on this area [12], [24], [14], [25], [16]. to the dynamics of the Internet in a recurring manner for Active worms use various scan mechanisms to propagate manipulating and controlling its overall scan traffic volume. themselves efficiently. The basic form of active worms can be The above recurring manipulations involve steady increase, categorized as having the Pure Random Scan (PRS) nature. In followed by a decrease in the scan traffic volume, such that the PRS form, a worm-infected computer continuously scans the changes do not manifest as any trends in the time domain a set of random Internet IP addresses to find new vulnerable or such that the scan traffic volume does not cross thresholds computers. Other worms propagate themselves more effec- that could reveal the C-Worm propagation. tively than PRS worms using various methods, e.g., network Based on the above observation, we adopt frequency domain port scanning, email, file sharing, Peer-to-Peer (P2P) networks, analysis techniques and develop a detection scheme against and Instant Messaging (IM) [26], [27]. In addition, worms use wide-spreading of the C-Worm. Particularly, we develop a different scan strategies during different stages of propagation. novel spectrum-based detection scheme that uses the Power In order to increase propagation efficiency, they use a local Spectral Density (PSD) distribution of scan traffic volume in network or hitlist to infect previously identified vulnerable the frequency domain and its corresponding Spectral Flatness computers at the initial stage of propagation [12], [28]. They Measure (SFM) to distinguish the C-Worm traffic from non- may also use DNS, network topology and routing information worm traffic (background traffic). Our frequency domain anal- to identify active computers instead of randomly scanning IP ysis studies use the real-world Internet traffic traces (Shield addresses [11], [21], [27], [29]. They split the target IP address logs dataset) provided by SANs Internet Storm Center (ISC) space during propagation in order to avoid duplicate scans [22], [23]2 . Our results reveal that non-worm traffic (e.g., [21]. Li et al. [30] studied a divide-conquer scanning technique port-scan traffic for port 80, 135 and 8080) has relatively that could potentially spread faster and stealthier than a larger SFM values for their PSD distributions. Whereas, the traditional random-scanning worm. Ha et al. [31] formulated C-Worm traffic shows comparatively smaller SFM value for the problem of finding a fast and resilient propagation topology its respective PSD distribution. and propagation schedule for Flash worms. Yang et al. [32] studied the worm propagation over the sensor networks. 2. ISC monitors and collects port-scan traffic data from around 1 million IP addresses spanning several thousands of organizations in different geograph- Different from the above worms, which attempt to accelerate ical regions. the propagation with new scan schemes, the Camouflaging Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 3. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 3 Worm (C-Worm) studied in this paper aims to elude the de- rules for detecting the worm propagation. For example, tection by the worm defense system during worm propagation. Venkataraman et al. and Wu et al. in [20], [21] proposed Closely related, but orthogonal to our work, are the evolved schemes to examine statistics of scan traffic volume, Zou et active worms that are polymorphic [33], [34] in nature. Poly- al. presented a trend-based detection scheme to examine the morphic worms are able to change their binary representation exponential increase pattern of scan traffic [19], Lakhina et al. or signature as part of their propagation process. This can in [40] proposed schemes to examine other features of scan be achieved with self-encryption mechanisms or semantics- traffic, such as the distribution of destination addresses. Other preserving code manipulation techniques. The C-Worm also works study worms that attempt to take on new patterns to shares some similarity with stealthy port-scan attacks. Such avoid detection [39]. attacks try to find out available services in a target system, Besides the above detection schemes that are based on while avoiding detection [35], [36]. It is accomplished by the global scan traffic monitor by detecting traffic anoma- decreasing the port scan rate, hiding the origin of attackers, lous behavior, there are other worm detection and defense etc. Due to the nature of self-propagation, the C-Worm must schemes such as sequential hypothesis testing for detecting use more complex mechanisms to manipulate the scan traffic worm-infected computers [44], payload-based worm signature volume over time in order to avoid detection. detection [34], [45]. In addition, Cai et al. in [46] presented both theoretical modeling and experimental results on a col- laborative worm signature generation system that employs 2.2 Worm Detection distributed fingerprint filtering and aggregation and multiple Worm detection has been intensively studied in the past and edge networks. Dantu et al. in [47] presented a state-space can be generally classified into two categories: “host-based” feedback control model that detects and control the spread detection and “network-based” detection. Host-based detection of these viruses or worms by measuring the velocity of the systems detect worms by monitoring, collecting, and analyzing number of new connections an infected computer makes. worm behaviors on end-hosts. Since worms are malicious Despite the different approaches described above, we believe programs that execute on these computers, analyzing the that detecting widely scanning anomaly behavior continues to behavior of worm executables plays an important role in host- be a useful weapon against worms, and that in practice multi- based detection systems. Many detection schemes fall under faceted defence has advantages. this category [37], [38]. In contrast, network-based detection systems detect worms primarily by monitoring, collecting, 3 M ODELING OF THE C-WORM and analyzing the scan traffic (messages to identify vulner- able computers) generated by worm attacks. Many detection 3.1 C-Worm schemes fall under this category [19], [20], [21], [39], [40]. The C-Worm camouflages its propagation by controlling scan Ideally, security vulnerabilities must be prevented to begin traffic volume during its propagation. The simplest way to with, a problem which must addressed by the programming manipulate scan traffic volume is to randomly change the language community. However, while vulnerabilities exist and number of worm instances conducting port-scans. pose threats of large-scale damage, it is critical to also focus As other alternatives, a worm attacker may use an open-loop on network-based detection, as this paper does, to detect wide- control (non-feedback) mechanism by choosing a randomized spreading worms. and time related pattern for the scanning and infection in order In order to rapidly and accurately detect Internet-wide to avoid being detected. Nevertheless, the open-loop control large scale propagation of active worms, it is imperative to approach raises some issues of the invisibility of the attack. monitor and analyze the traffic in multiple locations over First, as we know, worm propagation over the Internet can the Internet to detect suspicious traffic generated by worms. be considered a dynamic system. When an attacker launches The widely adopted worm detection framework consists of worm propagation, it is vey challenging for the attacker to multiple distributed monitors and a worm detection center know the accurate parameters for worm propagation dynamics that controls the former [23], [41]. This framework is well over the Internet. Given the inaccurate knowledge of worm adopted and similar to other existing worm detection systems, propagation over the Internet, the open-loop control system such as the Cyber center for disease controller [11], Internet will not be able to stabilize the scan traffic. This is a known motion sensor [42], SANS ISC (Internet Storm Center) [23], result from control system theory [48]. Consequently, the Internet sink [41], and network telescope [43]. The monitors overall worm scan traffic volume in the open-loop control are distributed across the Internet and can be deployed at end- system will expose a much higher probability to show an hosts, router, or firewalls etc. Each monitor passively records increasing trend with the progress of worm propagation. As irregular port-scan traffic, such as connection attempts to a more and more computers get infected, they, in turn, take range of void IP addresses (IP addresses not being used) and part in scanning other computers. Hence, we consider the C- restricted service ports. Periodically, the monitors send traffic worm as a worst case attacking scenario that uses a closed- logs to the detection center. The detection center analyzes the loop control for regulating the propagation speed based on the traffic logs and determines whether or not there are suspicious feedback propagation status. scans to restricted ports or to invalid IP addresses. In order to effectively evade detection, the overall scan Network-based detection schemes commonly analyze the traffic for the C-Worm should be comparatively slow and collected scanning traffic data by applying certain decision variant enough to not show any notable increasing trends over Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 4. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 4 time. On the other hand, a very slow propagation of the C- B has been infected. Through validating such marks during Worm is also not desirable, since it delays rapid infection the propagation, a C-Worm infected computer can estimate damage to the Internet. Hence, the C-Worm needs to adjust its M (t). Appendix A discusses one alternative how the C- propagation so that it is neither too fast to be easily detected, ¯ Worm could estimate M (t) to obtain M (t) as the propagation nor too slow to delay rapid damage on the Internet. proceeds. There are other approaches to achieve this goal, such To regulate the C-Worm scan traffic volume, we introduce as incorporating the Peer-to-Peer techniques to disseminate a control parameter called attack probability P (t) for each information through secured IRC channels [49], [50]. worm-infected computer. P (t) is the probability that a C- Worm instance participates in the worm propagation (i.e., 3.2 Propagation Model of the C-Worm scans and infects other computers) at time t. Our C-Worm model with the control parameter P (t) is generic. P (t) = 1 To analyze the C-Worm, we adopt the epidemic dynamic represents the cases for traditional worms, where all worm model for disease propagation, which has been extensively instances actively participate in the propagation. For the C- used for worm propagation modeling [2], [12]. Based on Worm, P (t) needs not be a constant value and can be set as existing results [2], [12], this model matches the dynamics a time varying function. of real worm propagation over the Internet quite well. For this In order to achieve its camouflaging behavior, the C-Worm reason, similar to other publications, we adopt this model in needs to obtain an appropriate P (t) to manipulate its scan our paper as well. Since our investigated C-Worm is a novel traffic. Specifically, the C-Worm will regulate its overall scan attack, we modified the original Epidemic dynamic formula traffic volume such that: (a) it is similar to non-worm scan to model the propagation of the C-Worm by introducing the traffic in terms of the scan traffic volume over time, (b) it P (t) - the attack probability that a worm-infected computer does not exhibit any notable trends, such as an exponentially participates in worm propagation at time t. We note that there increasing pattern or any mono-increasing pattern even when is a wide scope to notably improve our modified model in the number of infected hosts increases (exponentially) over the future to reflect several characteristics that are relevant in time, and (c) the average value of the overall scan traffic real-world practice. volume is sufficient to make the C-Worm propagate fast Particularly, the epidemic dynamic model assumes that any enough to cause rapid damage on the Internet3. given computer is in one of the following states: immune, We assume that a worm attacker intends to manipulate vulnerable, or infected. An immune computer is one that scan traffic volume so that the number of worm instances cannot be infected by a worm; a vulnerable computer is one participating in the worm propagation follow a random distri- that has the potential of being infected by a worm; an infected ¯ ¯ bution with mean MC . This MC can be regulated in a random computer is one that has been infected by a worm. The simple fashion during worm propagation in order to camouflage the epidemic model for a finite population of traditional PRS propagation of C-Worm. Correspondingly, the worm instances worms can be expressed as4 , need to adjust their attack probability P (t) in order to ensure dM (t) = β · M (t) · [N − M (t)], (1) that the total number of worm instances launching the scans dt ¯ is approximately MC . where M (t) is the number of infected computers at time t; ¯ To regulate MC , it is obvious that P (t) must be decreased N (= T · P1 · P2 ) is the number of vulnerable computers on over time since M (t) keeps increasing during the worm the Internet; T is the total number of IP addresses on the propagation. We can express P (t) using a simple function Internet; P1 is the ratio of the total number of computers on the ¯ ¯ as follows: P (t) = min( M(t) , 1), where M (t) represents the MC ¯ Internet over T ; P2 is the ratio of total number of vulnerable estimation of M (t) at time t. From the above expression, we computers on the Internet over the total number of computers ¯ know that the C-Worm needs to obtain the value of M (t) (as on the Internet; β = S/V is called the pairwise infection rate close to M (t) as possible) in order to generate an effective [51]; S is the scan rate defined as the number of scans that P (t). Here, we discuss one approach for the C-Worm to an infected computer can launch in a given time interval. We estimate M (t). The basic idea is as follows: A C-Worm could assume that at t = 0, there are M (0) computers being initially estimate the percentage of computers that have already been infected and N − M (0) computers being susceptible to further infected over the total number of IP addresses as well as M (t), worm infection. through checking a scan attempt as a new hit (i.e., hitting The C-Worm has a different propagation model compared an uninfected vulnerable computer) or a duplicate hit (i.e., to traditional PRS worms because of its P (t) parameter. hitting an already infected vulnerable computer). This method Consequently, Formula (1) needs to be rewritten as, requires each worm instance (i.e., infected computer) to be dM (t) marked indicating that this computer has been infected. Thus, = β · M (t) · P (t) · [N − M (t)]. (2) when a worm instance (for example, computer A) scans one dt ¯ ¯ infected computer (for example, computer B), then computer A Recall that P (t) = M (t) , M (t) is the estimation of M (t) at MC ¯ will detect such a mark, thereby becoming aware that computer ¯ time t, and assuming that M (t) = (1 + ) · M (t), where is 3. Note that if chooses P (t) below a certain (very low) level, other 4. We would like to remark that we use the PRS worms to compare C- human-scale countermeasures (e.g., signature-based virus detection, machine Worm performance, but our work can be easily extended to compare with quarantine) may become effective to disrupt the propagation. other worm scan techniques, such as hitlist. Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 5. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 5 Num ber of Detected Scanning Hosts on Cam ouflaging Worm the estimation error, the Formula (2) can be rewritten as, 100 ¯ # of Detected Scanning Hosts dM (t) β · MC 90 = · [N − M (t)]. (3) 80 dt 1 + (t) 70 60 50 With Formula (3), we can derive the propagation model 40 ¯ β·MC for the C-Worm as M (t) = N − e− 1+ (t)·t (N − M (0)), 30 20 where M (0) is the number of infected computers at time 10 0 0. Assume that the worm detection system can monitor Pm 200 2000 4000 6000 8000 9000 10000 11000 12000 13000 14000 (Pm ∈ [0, 1]) of the whole Internet IP address space. Without Time (min) PRS C-Worm 1 C-Worm 2 C-Worm 3 loss of generality, the probability that at least one scan from a worm-infected computer (it generates S scans in unit time Fig. 1. Observed infected instance number for the C- on average) will be observed by the detection system is Worm and PRS worm 1 − (1 − Pm )P (t)·S . We define that MA (t) is the number of Infection Ratio on Camouflaging Worms worm instances that have been observed by the worm detection 1 system at time t, then there are M (t) − MA (t) unobserved 0.9 0.8 infected instances at time t. At the worm propagation early 0.7 stage, M (t) − MA (t) M (t). The expected number of newly 0.6 IR 0.5 observed infected instances at t + δ (where δ is the interval 0.4 0.3 of monitoring) is (M (t) − MA (t)) · [1 − (1 − Pm )P (t)·S ] 0.2 0.1 M (i)[1 − (1 − Pm )P (t)·S ]. Thus, we have MA (t + δ) = 0 200 2000 4000 5000 7000 8100 9100 10200 11800 14000 MA (t)+M (t)[1−(1−Pm )P (t)·S ]. Using simple mathematical Time (min) manipulations, the number of worm instances observed by the PRS C-Worm 1 C-Worm 2 C-Worm 3 worm detection system at time t is, Fig. 2. Infected ratio for the C-Worm and PRS-Worm Pm · MC¯ MA (t) = P (t) · M (t) · Pm = . (4) 1 + (t) For the C-Worm, the trend of observed number of worm instances over time (MA (t)) (defined in Formula (4)) is much 3.3 Effectiveness of the C-Worm different from that of the traditional PRS worm as shown in Fig. 2. This clearly demonstrates how the C-Worm success- We now demonstrate the effectiveness of the C-Worm in evad- fully camouflages its increase in the number of worm instances ing worm detection through controlling P (t). Given random (MA (t)) and avoids detection by worm detection systems ¯ selection of Mc , we generate three C-Worm attacks (viz., C- that expect exponential increases in worm instance numbers Worm 1, C-Worm 2 and C-Worm 3) that are characterized during large-scale worm propagation. Fig. 3 shows the number by different selections of mean and variance magnitudes of scanning computers from normal non-worm port-scanning ¯ for MC . In our simulations, we assume that the scan rate traffic (background traffic) for several well-known ports, (i.e., of the traditional PRS worm follow a normal distribution 25, 53, 135, and 8080) obtained over several months by the Sn = N (40, 40) (note that if the scan rate generated by above ISC. Comparing Fig. 3 with Fig. 1, we can observe that it is distribution is less than 0 , we set the scan rate as 0). We also hard to distinguish the C-Worm port traffic from background set the total number of vulnerable computers on the Internet port-scanning traffic in the time domain. as 360,000, which is the total number of infected computers From above Figs. 1 and 2, we also observe that the C- in “Code-Red” worm incident [1]. Worm is still able to maintain a certain magnitude of scan Fig. 1 shows the observed number of worm-infected com- traffic so as to cause significant infection on the Internet. As puters over time for the PRS worm and the above three C- a note regarding the speed of C-Worm propagation, we can Worm attacks. Fig. 2 shows the infection ratio for the PRS observe from Fig. 1 that the C-Worm takes approximately 10 worm and the above three C-Worm attacks. These simulations days to infect 75% of total vulnerable hosts in comparison are for a worm detection system discussed in Section 2.2 that with the 3.3 days taken by a PRS worm5 . Hence, the C-Worm covers a 220 IPv4 address space on the Internet. The reason for could potentially adjust its propagation speed such that it is choosing 220 IP addresses as the coverage space of the worm still effective in causing wide-spreading propagation, while detection system is due to the fact that the SANs Internet avoiding being detected by the worm detection schemes. Storm Center (ISC), a representative ITM system, has similar We discussed the “Atak” worm in Section I and mentioned coverage space [23]. In the ITM systems, a large number of that it is similar to the C-Worm since it tries to avoid being monitors are commonly deployed all over the Internet and detected, when it suspects that it is being detected by anti- each monitor collects the traffic directed to a small set of IP worm software. However, it differs from the C-Worm in its address spaces which are not commonly used (also called dark behavior. The “Atak” worm attempts to hide only during IP addresses). Therefore, the address space of ITM system is times it suspects its propagation will be detected by anti-worm not a narrow range address space, rather a large number of small chunks of addresses randomly spread across the global 5. Our simulated PRS worm has less scan rate (mean value of 40) than IP address space. “Code-Red” (mean value of 358). Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 6. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 6 Scanning Traffic Volume: port 25 Scanning Traffic Volume: port 53 2500 500 control transfers into signals (traps) and inserting dummy Number of Scans 2000 400 control transfers and “junk” instructions after the signals. Number of Scans 1500 300 The resulting code can significantly reduce the chance to be 1000 200 detected. Recent studies also showed that existing commercial 500 100 anti-worm detection systems fail to detect brand new worms 0 0 500 1000 1500 2000 2500 0 0 500 1000 1500 2000 2500 and can also be easily circumvented by worms that use simple Time (unit − 20 min) Time (unit − 20 min) Scanning Traffic Volume: port 135 Scanning Traffic Volume: port 8080 mutation techniques to manipulate their payload [58]. 3500 3000 3000 Although in this paper we only demonstrate effectiveness 2500 of the C-Worm against existing traffic volume-based detection Number of Scans) Number of Scans 2500 2000 2000 1500 schemes, the design principle of the C-Worm can be extended 1500 1000 1000 to defeat other newly developed detection schemes, such 500 500 as destination distribution-based detection [39], [40]. In the 0 0 0 500 1000 1500 Time (unit − 20 min) 2000 2500 0 500 1000 1500 Time (unit − 20 min) 2000 2500 following, we discuss this preliminary concept. Recall that the Fig. 3. Observed infected instance number for back- attack target distribution based schemes analyze the distribu- ground scanning reported by ISC tion of attack targets (the scanned destination IP addresses) as basic detection data to capture the fundamental features of worm propagation, i.e., they continuously scan different software. Whereas, the C-Worm proactively camouflages itself targets, which is not the expected behavior of non-worm at all times. In addition, the “Self-stopping” worm attempts to scan traffic. However, our initial investigation shows that the hide by co-ordinating with its members to halt propagation worm attacker is still able to defeat such a countermeasure activity only after the vulnerable population is subverted [16]. via manipulating the attack target distribution. For example, This behavior leaves enough evidence for worm detection the attacker may launch a portion of scan traffic bound for systems to recognize its propagation. The C-Worm, on the some IP addresses monitored by ITM system. Recall that other hand, hides itself even during its propagation and thus those dedicated IP addresses monitored by ITM system can keeps the worm detection schemes completely unaware of its be obtained via probing attacks or other means [59], [60], propagation. The C-Worm also has some similarity in spirit [61]. with polymorphic worms that manipulate the byte stream of Using port 135 reported by SANs ISC as an example, we worm payload in order to avoid the detection of signature analyze the traces and obtain the traffic target distribution in a (payload)-based detection scheme [33], [34]. The manipulation window lasting 10 mins. Following existing work [39], [40], of worm payload can be achieved by various mechanisms: (a) we use entropy as the metric to measure the attack target interleaving meaningful instructions with NOP (no operation), distribution. Fig. 4 shows the Probability Density Function (b) using different instructions to achieve the same results, (c) (PDF) of background traffic’s entropy values. We also simulate shuffling the register set in each worm propagation program the worm propagation traffic, which allocates a portion of code copy, and (d) using cryptography mechanisms to change scan traffic bound for IP addresses monitored by the ITM worm payload signature with every infection attempt [33], system. Following this, we obtain the PDF of the entropy [34]. In contrast, the C-Worm tries to manipulate the scan value for combined traffic including both worm propagation traffic pattern to avoid detection. and background traffic. From Fig. 4, we know that when the attacker uses a portion of attack traffic to manipulate the target distribution, the entropy-based detection scheme 3.4 Discussion can degrade significantly. For example, when the attacker In this paper, we focus on a new class of worms, referred to as uses 10% traffic to manipulate the traffic’s entropy value, the the camouflaging worm (C-Worm). The C-Worm adapts their false positive rate of entropy-based detection scheme is 14%. propagation traffic patterns in order to reduce the probability When the attacker uses 30% traffic to manipulate the traffic’s of detection, and to eventually infect more computers. The entropy value, the false positive rate becomes 40%. Hence, C-Worm is different from polymorphic worms that delib- in order to preserve the performance, entropy-based detection erately change their payload signatures during propagation scheme needs to evolve correspondingly and integrate with [34], [52]. For example, MetaPHOR [53] and Zmist [54] other detection schemes. We will perform a more detailed worms intensively metamorphose their payload signature to study of this aspect in our future work. hide themselves from detection schemes that rely on expensive packet payload analysis. Bethencourt et al. [55] studied the 4 D ETECTING THE C-WORM worm which employs private information retrieval techniques 4.1 Design Rationale to find and retrieve specific pieces of sensitive information In this section, we develop a novel spectrum-based detection from compromised computers while hiding its search criteria. scheme. Recall that the C-Worm goes undetected by detection Sharif et al. [56] presented an obfuscation-based technique that schemes that try to determine the worm propagation only in automatically conceals specific condition dependent malicious the time domain. Our detection scheme captures the distinct behavior from virus detectors that have no prior knowledge of pattern of the C-Worm in the frequency domain, and thereby program inputs. Popov et al. [57] investigated a technique that has the potential of effectively detecting the C-Worm propa- allows the worm programs to be obfuscated by changing many gation. Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 7. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 7 PDF of C-Worm SFM 100 90 80 Probability Density 70 60 50 40 30 20 10 0 0 0.04 0.08 0.11 0.15 0.19 0.23 0.27 0.3 0.34 0.38 0.42 0.48 0.72 0.96 SFM Value Fig. 5. PDF of SFM on C-Worm traffic Fig. 4. Manipulation of attack target distribution entropy PDF of Norm al Non-worm Scanning Traffic 70 In order to identify the C-Worm propagation in the fre- 60 quency domain, we use the distribution of Power Spectral Den- 50 Probability Density sity (PSD) and its corresponding Spectral Flatness Measure 40 (SFM) of the scan traffic. Particularly, PSD describes how the 30 power of a time series is distributed in the frequency domain. 20 10 Mathematically, it is defined as the Fourier transform of the 0 auto-correlation of a time series. In our case, the time series 0 0.15 0.3 0.4 0.43 0.47 0.5 0.54 0.57 0.61 0.64 0.68 0.71 0.75 0.8 0.96 corresponds to the changes in the number of worm instances SFM Value that actively conduct scans over time. The SFM of PSD is Fig. 6. PDF of SFM on normal non-worm traffic defined as the ratio of geometric mean to arithmetic mean of the coefficients of PSD. The range of SFM values is [0, 1] and a larger SFM value implies flatter PSD distribution and followed by a decrease in the scan traffic volume. vice versa. Notice that the frequency domain analysis will require To illustrate SFM values of both the C-Worm and normal more samples in comparison with the time domain analysis, non-worm scan traffic, we plot the Probability Density Func- since the frequency domain analysis technique such as the tion (PDF) of SFM for both C-Worm and normal non-worm Fourier transform, needs to derive power spectrum amplitude scan traffic as shown in Fig. 5 and Fig. 6, respectively. The for different frequencies. In order to generate the accurate normal non-worm scan traffic data shown in Fig. 6 is based spectrum amplitude for relatively high frequencies, a high on real-world traces collected by the ISC 6 . Note that we granularity of data sampling will be required. In our case, we only show the data for port 8080 as an example, and other rely on Internet threat monitoring (ITM) systems to collect ports show similar observations. From this figure, we know traffic traces from monitors (motion sensors) in a timely that the SFM value for normal non-worm traffic is very small manner. As a matter of fact, other existing detection schemes (e.g., SFM ∈ (0.02, 0.04) has much higher density compared based on the scan traffic rate [20], variance [21] or trend [19] with other magnitudes). The C-Worm data shown in Fig. 5 is will also demand a high sampling frequency for ITM systems based on 800 C-Worms attacks generated by varying attack in order to accurately detect worm attacks. Enabling the ITM parameters defined in Section 3 such as P (t) and Mc (t). system with timely data collection will benefit worm detection From this figure, we know that the SFM value of the C-Worm in real-time. attacks is high (e.g., SFM ∈ 0.5, 0.6 has high density). From 4.2 Spectrum-based Detection Scheme the above two figures, we can observe that there is a clear demarcation range of SFM ∈ (0.3, 0.38) between the C-Worm We now present the details of our spectrum-based detection and normal non-worm scan traffic. As such, the SFM can be scheme. Similar to other detection schemes [19], [21], we use used to sensitively detect the C-Worm scan traffic. a “destination count” as the number of the unique destination IP addresses targeted by launched scans during worm propaga- The large SFM values of normal non-worm scan traffic tion. To understand how the destination count data is obtained, can be explained as follows. The normal non-worm scan we recall that an ITM system collects logs from distributed traffic does not tend to concentrate at any particular frequency monitors across the Internet. On a side note, Internet Threat since its random dynamics is not caused by any recurring Monitoring (ITM) systems are a widely deployed facility to phenomenon. The small value of SFM can be reasoned by detect, analyze, and characterize dangerous Internet threats the fact that the power of C-Worm scan traffic is within a such as worms. In general, an ITM system consists of one narrow-band frequency range. Such concentration within a centralized data center and a number of monitors distributed narrow range of frequencies is unavoidable since the C-Worm across the Internet. Each monitor records traffic that addressed adapts to the dynamics of the Internet in a recurring manner to a range of IP addresses (which are not commonly used IP for manipulating the overall scan traffic volume. In reality, address also called the dark IP addresses) and periodically the above recurring manipulations involve steady increase sends the traffic logs to the data center. The data center then 6. The traces used in this paper contain log files which have over 100 analyzes the collected traffic LOGS and publishes reports (e.g., million records and the total size exceeds 40 GB. statistics of monitored traffic) to ITM system users. Therefore Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 8. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 8 the baseline traffic in our study is scan traffic. With reports in expressed as, a sampling window Ws , the source count X(t) is obtained by n 1 [ k=1 S(fk )] n counting the unique source IP addresses in received logs. SF M = 1 n , (7) To conduct spectrum analysis, we consider a detection n k=1 S(fk ) sliding window Wd in the worm detection system. Wd consists where S(fk ) is an PSD coefficient for the PSD obtained from of q (> 1) continuous detection sampling windows and each the results in Formula (6). SFM is a widely existing measure sampling window lasts Ws . The detection sampling window for discriminating frequencies in various applications such is the unit time interval to sample the detection data (e.g., the as voiced frame detection in speech recognition [63], [64]. destination count). Hence, at time i, within a sliding window In general, small values of SFM imply the concentration of Wd , there are q samples denoted by (X(i − q − 1), X(i − data at narrow frequency spectrum ranges. Note that the C- q − 2), . . . , X(i)), where X(i − j − 1) (j ∈ (1, q)) is the j-th Worm has unpreventable recurring behavior in its scan traffic; destination count from time i − j − 1 to i − j. consequently its SFM values are comparatively smaller than In our spectrum-based detection scheme, the distribution of the SFM values of normal non-worm scan traffic. To be useful PSD and its corresponding SFM are used to distinguish the C- in detecting C-Worms, we introduce a sliding window to Worm scan traffic from the non-worm scan traffic. Recall that capture a noticeably higher concentrations at a small range of the definition of PSD distribution and its corresponding SFM spectrum. When such noticeably concentration is recognized, are introduced in Section 4.1. In our worm detection scheme, we derive the SFM within a wider frequency range. From the detection data (e.g., destination counter), is further pro- Fig. 5, we can observe that the SFM value for the C-Worm is cessed in order to obtain its PSD and SFM. In the following, very small (e.g., with a mean value of approximately 0.075). we detail how the PSD and SFM are determined during the A formal analysis of SFM for the C-Worm is presented in the processing of the detection data. Appendix B. 4.2.1 Power Spectral Density (PSD) 4.2.3 Detection Decision Rule To obtain the PSD distribution for worm detection data, We now describe the method of applying an appropriate we need to transform data from the time domain into the detection rule to detect C-Worm propagation. As the SFM frequency domain. To do so, we use a random process value can be used to sensitively distinguish the C-Worm X(t), t ∈ [0, n] to model the worm detection data. Assuming and normal non-worm scan traffic, the worm detection is X(t) is the source count in time period [t − 1, t] (t ∈ [1, n]), performed by comparing the SFM with a predefined threshold we define the auto-correlation of X(t) by Tr . If the SFM value is smaller than a predefined threshold RX (L) = E[X(t)X(t + L)]. (5) Tr , then a C-Worm propagation alert is generated. The value of the threshold Tr used by the C-Worm detection can be In Formula (5), RX (L) is the correlation of worm detection fittingly set based on the knowledge of statistical distribution data in an interval L. If a recurring behavior exists, a Fourier (e.g., PDF) of SFM values that correspond to the non-worm transform of the auto-correlation function of RX (L) can reveal scan traffic. Notice that the Tr value for the non-worm traffic such behavior. Thus, the PSD function (also represented by can be derived by analyzing the historical data provided by SX (f ); where f refers to frequency) of the scan traffic data SANs Internet Storm Center (ISC). In the worm detection is determined using the Discrete Fourier Transform (DFT) of systems, monitors collect port-scan traffic to certain area of its auto-correlation function as follows, dark IP addresses and periodically reports scan traffic log to N −1 the data center. Then the data center aggregates the data from ψ(RX [L], K) = (RX [L]) · e−j2πKn/N , (6) different monitors on the same port and publishes the data. n=0 Based on the historical data for different ports, we can build where K = 0, 1, . . . , N − 1. the statistical profiles of port-scan traffic on different ports and then derive the Tr value for the non-worm traffic. Based on As the PSD inherently captures any recurring pattern in the the continuous reported data, the value of Tr will be tuned frequency domain, the PSD function shows a comparatively and adaptively used to carry out worm detection. even distribution across a wide spectrum range for the normal non-worm scan traffic. The PSD of C-Worm scan traffic shows If we can obtain the PDF of SFM values for the C- spikes or noticeably higher concentrations at a certain range Worm through comprehensive simulations and even real-world of the spectrum. profiled data in the future, the optimal threshold can be obtained by applying the Bayes classification [65]. If the PDF of SFM values for the C-Worm is not available, based on the 4.2.2 Spectral Flatness Measure (SFM) PDF of SFM values of the normal non-worm scan traffic, we We measure the flatness of P SD to distinguish the scan traffic can set an appropriate Tr value. For example, the Tr value of the C-Worm from the normal non-worm scan traffic. For can be determined by the Chebyshev inequality [65] in order this, we introduce the Spectral Flatness Measure (SFM), which to obtain a reasonable false positive rate for worm detection. can capture anomaly behavior in certain range of frequencies. Hence in Section 5, we evaluate our spectrum-based detection The SFM is defined as the ratio of the geometric mean to the scheme against the C-Worm on two cases: (a) the PDF of SFM arithmetic mean of the PSD coefficients [62], [63]. It can be values are known for both the normal non-worm scan traffic Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 9. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 9 and the C-Worm scan traffic, (b) the PDF of SFM values is the detection speed of a detection scheme. M IR defines the only known for the normal non-worm scan traffic. ratio of an infected computer number over the total number In addition, our spectrum-based scheme is also generic for of vulnerable computers up to the moment when the worm detecting the PRS worms. This is due to the fact that propa- spreading is detected. It quantifies the damage caused by a gation traffic of PRS worms has an exponentially increasing worm before being detected. The objective of any detection pattern. Thus, in the propagation traffic of PRS worms, the scheme is to minimize the damage caused by a rapid worm PSD values in the low frequency range are much higher propagation. Hence, M IR and DT can be used to quantify compared with other frequency ranges. A formal analysis of the effectiveness of any worm detection scheme. The higher SFM for the PRS worm is presented in Appendix C. the values, the more effective the worm attack and the less Notice that even if the C-Worm monitors the port-scan effective the detection. In addition, we use two more metrics - traffic report, it will be hard for the C-Worm to make the Detection Rate (PD ) and False Positive Rate (PF ). The PD is SFM similar to the background traffic. This can be reasoned defined as the probability that a detection scheme can correctly by two factors. First, the low value of SFM is mainly caused by identify a worm attack. The PF is defined as the probability the closed-loop control nature of C-worm. The concentration that a detection scheme mistakenly identifies a non-existent within a narrow range of frequencies is unavoidable since the worm attack. C-Worm adapts to the dynamics of the Internet in a recurring manner for manipulating the overall scan traffic volume. Based 5.1.2 Simulation Setup on our analysis, the non-worm traffic on a port is rather random In our evaluation we considered both experiments with real- and its SFM has a flat pattern. That means that the non-worm world “non-worm” traffic and simulated c-worm traffic. To traffic on the port distributes similar power across different make our experiments reflect real-world practice, some key frequencies. Second, as we indicated in other responses, with- parameters that we used to generate C-worm traffic in our out introducing the closed-loop control, it will be difficult for simulation were based on previous results from a real-worm the attacker to hide the irregularity of worm propagation traffic incidence - “Code-Red” worm in 2001 [1]. Specifically, we in the time domain. When the worm attacks incorporate the set the total number of vulnerable computers on the Internet closed-loop control mechanism to camouflage their traffic, it as 360,000, which is the maximum number of computers will expose a relative small value of SFM. Hence, integrating which could be infected by “Code-Red” worm. Additionally, our spectrum-based detection with existing traffic rate-based we set the scan rate S (number of scans per minute) to anomaly detection in the time domain, we can force the worm be variable within a range, this allows us to emulate the attacker into a dilemma: if the worm attacker does not use the infected computers in different network environments. In our closed-loop control, the existing traffic rate-based detection evaluation, the scan rates are predetermined and follow a 2 2 scheme will be able to detect the worm; if the worm attacker Gaussian distribution S = N (Sm , Sσ ), where Sm and Sσ are adopt the closed-loop control, it will cause the relatively small in [(20, 70], similar to those used in [19]. In our evaluation, SFM due to the process of closed-loop control. This makes we merged the simulated C-worm attack traffic into replayed the worm attack to be detected by our spectrum-based scheme “non-worm” traffic traces and carried out evaluation study. along with other existing traffic-rate based detection schemes. We simulate the C-Worm attacks by varying the attack parameters, such as attack probability (P (t)) and the number 5 P ERFORMANCE E VALUATION ¯ of worm instances participating in the scan (MC ) defined in ¯ Section 3. The MC follows the Gaussian distribution N (m, σ) In this section, we report our evaluation results that illustrate the effectiveness of our spectrum-based detection scheme and are changed dynamically by the C-Worm during its against both the C-Worm and the PRS worm in comparison propagation. Particularly, for N (m, σ), m is randomly selected with existing representative detection schemes for detecting in (12000, 75000) and σ is randomly selected in (0.2, 100). wide-spreading worms. In addition, we also take into consid- We simulate different C-Worm attacks by varying the values eration destination distribution based detection schemes and of m and σ. The detection sampling window Ws is set to evaluate their performance against the C-Worm. 5 minutes and the detection sliding window Wd is set to be incremental from 80 min to 800 min. The incremental selection of Ws from a comparatively small window to a large 5.1 Evaluation Methodology window can adaptively reflect the worm scan traffic dynamics 5.1.1 Evaluation Metrics caused by the C-Worm propagation at various speeds. We In order to evaluate the performance of any given detection choose the setting of the detection sampling window to be scheme against the C-Worm, we use the following three short enough in order to provide enough sampling accuracy metrics listed in Table II. The first metric is the worm Infection as prescribed by Nyquist’s sampling theory. Also, we choose Ratio (IR), which is defined as the ratio of the number of the detection sliding window to be long enough to capture infected computers to the total number of vulnerable comput- adequate information for spectrum-based analysis [63]. ers, assuming there is no worm detection/defense system in In practice, since detection systems analyze port scan traffic place. The other two metrics are the Detection Time (DT ) blended with the non-worm scan traffic, we replay the real- and the Maximal Infection Ratio (M IR). DT is defined as world traces as non-worm scan traffic (background noise to the time taken to successfully detect a wide-spreading worm attack traffic) in our simulations. In particular, we used the from the moment the worm propagation starts. It quantifies ISC real-world trace (Shield logs dataset) from 01/01/2005 Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 10. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 10 TABLE 1 Evaluation Metrics Notation Definition Infection Ratio (IR) Ratio of worm-infection over time without the presence of detection/defense system Maximal Infection Ratio (M IR) Ratio of worm infection at the moment that worm is being detected Detection time (DT ) Time taken to successfully detect a wide-spreading worm from its birth to 01/15/2005. Note that SANs ISC, maintained by the SANs comparison between traffic volume based detection and traffic Institute, have gained popularity among the Internet security distribution based detection. community in recent years. ISC collects firewall and Intrusion detection system logs, which indicate port-scan trends from 5.2.1 Detection Performance for C-Worm Attacks approximately 2000 organizations that monitor up to 1 million Table 2 shows the detection results of different detection IP addresses. We choose the scan traffic logs for port 8080 as schemes against the C-Worm. The results have been averaged an example for profiling the non-worm scan traffic. over 500 C-Worm attacks. From this table, we can observe In order to provide the creditability of such data, we did the that existing detection schemes are not able to effectively following effort before using the data in our experiments. First, detect the C-Worm and their detection rate (PD ) values are we had the 15 days traces from 01/01/2005 to 01/15/2005 significantly lower in comparison with our spectrum-based provided by SANs ISC. We checked with the SANs website detection schemes (SPEC and SPEC(W)). For example, SPEC and found that there were no worm attack incidents within achieves the detection rate of 99%, which is at least 3-4 those 15 days. Second, we obtained the statistical profile of times more accurate than detection schemes such as VAR and traffic traces, including the mean value and standard deviation MEAN that achieve detection rate values of only 48% and of traffic rates. Based on the statistical profile, we set a 14%, respectively. threshold which is the summary of mean value and four times Our SPEC and SPEC(W) detection schemes also achieve that of the standard derivation, and filtered out some data good detection time (DT ) performance in addition to the high which had unusual large values. Third, we conducted our detection rate values indicated above. In contrast, the detection evaluation 15 times based on data randomly combined with time of existing detection schemes have relatively larger different dates. The results we showed in the paper are the values. As a consequence of the detection time values, we can mean values of experimental results from different rounds. see that the C-Worm propagation is effectively contained by SPEC and SPEC(W), as demonstrated by the lower values of 5.2 Performance of Detection Schemes maximal infection ratio (M IR) for the SPEC and SPEC(W). We evaluate our proposed spectrum-based detection scheme by Since the detection rate values for the existing detection comparing its performance with three existing representative schemes are relatively small, obtaining low values of M IR traffic volume-based detection schemes. The first scheme is for those schemes are not as significant as those for SPEC the volume mean-based (MEAN) detection scheme which uses and SPEC(W). Furthermore, we can notice that the detection mean of scan traffic to detect worm propagation [20]; the performance of the SPEC(W) is worse than the SPEC. This is second scheme is the trend-based (TREND) detection scheme because the SPEC(W) lacks off-line training knowledge for the which uses the increasing trend of scan traffic to detect worm C-Worm scan traffic. Nonetheless, the SPEC(W) still performs propagation [19]; and the third scheme is the victim number much better than existing detection schemes. variance based (VAR) detection scheme which uses the vari- ance of the scan traffic to detect worm propagation [21]. 5.2.2 Detection Performance for Traditional PRS Worms We define our spectrum-based detection scheme as SPEC. We evaluate the detection performance of different detection We evaluate two types of SPEC: one has no knowledge of schemes for traditional PRS worm attacks. The detection per- any C-Worm attacks or C-Worm scan traffic (denoted by formance results have been averaged over 500 PRS worm at- SPEC(W)) and the other has knowledge of C-Worm attacks tacks. We observe that both our SPEC and SPEC(W) schemes through an off-line training process (denoted by SPEC). For achieve 100% detection rate (PD ) while detecting traditional the off-line training, we use 1000 worm attacks that include PRS worms in comparison with the existing worm detection both the C-Worm (800 C-Worm attacks) and PRS worms schemes that have been specifically designed for detecting the (200 PRS worm attacks). For fairness, we set the detection traditional PRS worms. parameters for our SPEC scheme and the other three detection In view of emphasizing the relative performance of our schemes, so that all detection schemes achieve a similar false SPEC and SPEC(W) schemes with the existing worm de- positive rate (PF ) below 1%. tection schemes, we plot the M IR and DT results in Figs. In the following subsections, we first evaluate the perfor- 7 and 8 for different scan rates S. We can observe from mance of our spectrum-based detection scheme in the context these figures that the M IR and DT results of our spectrum- of detecting C-Worm attacks. We then evaluate the perfor- based scheme (shown only for SPEC(W)) are comparable mance of our spectrum-based detection scheme in the context or better than the existing worm detection schemes. For a of detecting traditional PRS worms, followed by performance mean scan rate of 70/min, our SPEC(W) scheme achieves Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 11. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 11 TABLE 2 Detection results for the C-Worm Schemes VAR TREND MEAN SPEC(W) SPEC Detection Rate (DR) 48% 0% 14% 96.4% 99.3% Maximal Infection Ratio (MIR) 14.4% 100% 7.5% 4.4% 2.8% Detection Time (DT) in minutes 2367 ∞ 1838 1707 1460 Maxim al Infection Ratio of of PRS Worm the C-Worm in comparison with existing representative de- 0.1 0.09 tection schemes. This paper lays the foundation for ongoing 0.08 studies of “smart” worms that intelligently adapt their propa- 0.07 0.06 gation patterns to reduce the effectiveness of countermeasures MIR 0.05 0.04 0.03 0.02 ACKNOWLEDGMENTS 0.01 0 We thank the anonymous reviewers for their invaluable feed- 20 30 40 Scan Rate 50 60 70 back. This work was supported in part by the US National VAR TREND MEAN SPEC(W) Science Foundation (NSF) under grants No. CNS-0916584, Fig. 7. Maximal Infection Ratio of detection schemes CAREER Award CCF-0546668, and the Army Research Of- against PRS worm fice (ARO) under grant No. AMSRD-ACC-R 50521-CI; by the National Science Foundation (NSF) under grants No. Detection Tim e of PRS Worm 0963973 and No. 0963979 and by the University of Macau, 2900 and Macao Science and Technology Development Foundation. Any opinions, findings, conclusions, and recommendations in 2500 this paper are those of the authors and do not necessarily reflect 2100 the views of the funding agencies. The authors would like DT 1700 to acknowledge Ms. Larisa Archer for her dedicated help to improve the paper. 1300 900 20 30 40 Scan Rate 50 60 70 R EFERENCES VAR TREND MEAN SPEC(W) [1] D. Moore, C. Shannon, and J. Brown, “Code-red: a case study on the Fig. 8. Detection Time of detection schemes against PRS spread and victims of an internet worm,” in Proceedings of the 2-th Internet Measurement Workshop (IMW), Marseille, France, November worm 2002. [2] D. Moore, V. Paxson, and S. Savage, “Inside the slammer worm,” in IEEE Magazine of Security and Privacy, July 2003. a detection time of 1024 mins, which is faster than that [3] CERT, CERT/CC advisories, http://www.cert.org/advisories/. of VAR and MEAN schemes, whose values are 1239 min [4] P. R. Roberts, Zotob Arrest Breaks Credit Card Fraud Ring, http: //www.eweek.com/article2/0,1895,1854162,00.asp. and 1161 min, respectively. For the same mean scan rate of [5] W32/MyDoom.B Virus, http://www.us-cert.gov/cas/techalerts/ 70/min, SPEC(W) achieves a maximal infection ratio of 0.03, TA04-028A.html. which is comparable to TREND’s M IR value and is less than [6] W32.Sircam.Worm@mm, http://www.symantec.com/avcenter/venc/data/ w32.sircam.worm@mm.html. 50% of the M IR value for the VAR and MEAN detection [7] Worm.ExploreZip, http://www.symantec.com/avcenter/venc/data/worm. schemes. The effectiveness of our spectrum-based scheme is explore.zip.html. based on the fact that traditional PRS worm scanning traffic [8] R. Naraine, Botnet Hunters Search for Command and Control Servers, http://www.eweek.com/article2/0,1759,1829347,00.asp. shows a constantly rapid increase. Thus, SFM values are [9] T. Sanders, Botnet operation controlled 1.5m PCs Largest zom- relatively small due to PSD concentration at the low frequency bie army ever created, http://www.vnunet.com/vnunet/news/2144375/ bands in the case of the traditional PRS worm scanning. botnet-operation-ruled-million, 2005. [10] R. Vogt, J. Aycock, and M. Jacobson, “Quorum sensing and self- stopping worms,” in Proceedings of 5th ACM Workshop on Recurring 6 F INAL R EMARKS [11] Malcode (WORM), Alexandria VA, October 2007. S. Staniford, V. Paxson, and N. Weaver, “How to own the internet in your In this paper, we studied a new class of smart-worm called C- spare time,” in Proceedings of the 11-th USENIX Security Symposium (SECURITY), San Francisco, CA, August 2002. Worm, which has the capability to camouflage its propagation [12] Z. S. Chen, L.X. Gao, and K. Kwiat, “Modeling the spread of and further avoid the detection. Our investigation showed that, active worms,” in Proceedings of the IEEE Conference on Computer although the C-Worm successfully camouflages its propagation Communications (INFOCOM), San Francisco, CA, March 2003. [13] M. Garetto, W. B. Gong, and D. Towsley, “Modeling malware spreading in the time domain, its camouflaging nature inevitably mani- dynamics,” in Proceedings of the IEEE Conference on Computer fests as a distinct pattern in the frequency domain. Based on Communications (INFOCOM), San Francisco, CA, March 2003. observation, we developed a novel spectrum-based detection [14] C. C. Zou, W. Gong, and D. Towsley, “Code-red worm propagation modeling and analysis,” in Proceedings of the 9-th ACM Conference scheme to detect the C-Worm. Our evaluation data showed that on Computer and Communication Security (CCS), Washington DC, our scheme achieved superior detection performance against November 2002. Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 12. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 12 [15] Zdnet, Smart worm lies low to evade detection, http://news.zdnet.co.uk/ [38] X. Wang, W. Yu, A. Champion, X. Fu, and D. Xuan, “Detecting internet/security/0,39020375,39160285,00.htm. worms via mining dynamic program execution,” in Proceedings of IEEE [16] J. Ma, G. M. Voelker, and S. Savage, “Self-stopping worms,” in Pro- International Conference on Security and Privacy in Communication ceedings of the ACM Workshop on Rapid Malcode (WORM), Washington Networks (SECURECOMM), Nice, France, September 2007. D.C, November 2005. [39] W. Yu, X. Wang, D. Xuan, and D. Lee, “Effective detection of active [17] Min Gyyng Kang, Juan Caballero, and Dawn Song, “Distributed evasive worms with varying scan rate,” in Proceedings of IEEE International scan techniques and countermeasuress,” in Proceedings of International Conference on Security and Privacy in Communication Networks (SE- Conference on Detection of Intrusions & Malware, and Vulnerability CURECOMM), Baltimore, MD, August 2006. Assessment (DIMVA), Lucerne, Switzerland, July 2007. [40] A. Lakhina, M. Crovella, and C. Diot, “Mining anomalies using traffic [18] Charles Wright, Scott Coull, and Fabian Monrose, “Traffic morphing: feature distribution,” in Proceedings of ACM SIGCOMM, Philadelphia, An efficient defense against statistical traffic analysis,” in Proceedings PA, August 2005. of the 15th IEEE Network and Distributed System Security Symposium [41] V. Yegneswaran, P. Barford, and D. Plonka, “On the design and (NDSS), San Diego, CA, Febrary 2008. utility of internet sinks for network abuse monitoring,” in Proceeding [19] C. Zou, W. B. Gong, D. Towsley, and L. X. Gao, “Monitoring of Symposium on Recent Advances in Intrusion Detection (RAID), and early detection for internet worms,” in Proceedings of the 10- Pittsburgh, PA, September 2003. th ACM Conference on Computer and Communication Security (CCS), [42] M. Bailey, E. Cooke, F. Jahanian, J. Nazario, and D. Watson, “The Washington DC, October 2003. internet motion sensor: A distributed blackhole monitoring system,” in [20] S. Venkataraman, D. Song, P. Gibbons, and A. Blum, “New streaming Proceedings of the 12-th IEEE Network and Distributed Systems Security algorithms for superspreader detection,” in Proceedings of the 12-th Symposium (NDSS), San Diego, CA, February 2005. IEEE Network and Distributed Systems Security Symposium (NDSS), [43] D. Moore, “Network telescopes: Observing small or distant security San Diego, CA, Febrary 2005. events,” in Invited Presentation at the 11th USENIX Security Symposium [21] J. Wu, S. Vangala, and L. X. Gao, “An effective architecture and (SECURITY)), San Francisco, CA, August 2002. algorithm for detecting worms with various scan techniques,” in [44] J. Jung, V. Paxson, A. W. Berger, and H. Balakrishnan, “Fast portscan Proceedings of the 11-th IEEE Network and Distributed System Security detection using sequential hypothesis testing,” in Proceedings of the Symposium (NDSS), San Diego, CA, Febrary 2004. 25-th IEEE Symposium on Security and Privacy (S&P), Oakland, CA, [22] Dshield.org, Distributed Intrusion Detection System, http://www.dshield. May 2004. org/, 2005. [45] H. Kim and B. Karp, “Autograph: Toward automated, distributed worm [23] SANS, Internet Storm Center, http://isc.sans.org/. signature detection,” in Proceedings of the 13-th USENIX Security [24] C. C. Zou, W. Gong, and D. Towsley, “Worm propagation modeling Symposium (SECURITY), San Diego, CA, August 2004. and analysis under dynamic quarantine defense,” in Proceedings of the [46] M. Cai, K. Hwang, J. Pan, and C. Papadopoulos, “Wormshield: Fast 1-th ACM CCS Workshop on Rapid Malcode (WORM), Washington DC, worm signature generation with distributed fingerprint aggregation,” October 2003. IEEE Transactions on Dependable and Secure Computing, vol. 4, no. 2, pp. 88–104, 2007. [25] C. C. Zou, D. Towsley, and W. Gong, “Modeling and simulation [47] R. Dantu, J. W. Cangussu, and S. Patwardhan, “Fast worm containment study of the propagation and defense of internet e-mail worm,” IEEE using feedback control,” IEEE Transactions on Dependable and Secure Transactions on Dependable and Secure Computing, vol. 4, no. 2, pp. Computing, vol. 4, no. 2, pp. 119–136, 2007. 105–118, 2007. [48] K. Ogata, MOdern Control Engineering, Pearson Prentice Hall, 2002. [26] C. Zou, Don Towsley, and Weibo Gong, “Email worm modeling [49] J. B. Grizzard, V. Sharma, C. Nunnery, B. B. Kang, and D. Dagon, and defense,” in Proceedings of the 13-th International Conference “Peer-to-peer botnets: Overview and case study,” in Proceedings of on Computer Communications and Networks (ICCCN), Chicago, IL, USENIX Workshop on Hot Topics in Understanding Botnets (HotBots), October 2004. Cambridge, MA, April 2007. [27] W. Yu, S. Chellappan C. Boyer, and D. Xuan, “Peer-to-peer system- [50] P. Wang, S. SParka, and C. Zou, “An advanced hybrid peer-to- based active worm attacks: Modeling and analysis,” in Proceedings of peer botnet,” in Proceedings of USENIX Workshop on Hot Topics in IEEE International Conference on Communication (ICC), Seoul, Korea, Understanding Botnets (HotBots), Cambridge, MA, April 2007. May 2005. [51] D. J. Daley and J. Gani, Epidemic Modeling: an Introduction, Cam- [28] Dynamic Graphs of the Nimda Worm, http://www.caida.org/dynamic/ bridge University Press, 1999. analysis/security/nimda. [52] D. Bruschi, L. Martignoni, and M. Monga, “Detecting self-mutating [29] S. Staniford, D. Moore, V. Paxson, and N. Weaver, “The top speed of malware using control flow graph matching,” in Proceedings of the flash worms,” in Proceedings of the 2-th ACM CCS Workshop on Rapid Conference on Detection of Intrusions and Malware & Vulnerability Malcode (WORM), Fairfax, VA, October 2004. Assessment (DIMVA), Berlin, Germany, July 2006. [30] Yubin Li, Zesheng Chen, and Chao Chen, “Understanding divide- [53] MetaPHOR, http://securityresponse.symantec.com/avcenter/venc/data/ conquer-scanning worms,” in Proceedings of International Performance w32.simile.html. Computing and Communications Conference (IPCCC), Austin, TX, [54] P. Ferrie and P. Sz¨ r. Zmist, Zmist opportunities, Virus Bullettin, http: o December 2008. //www.virusbtn.com. [31] D. Ha and H. Ngo, “On the trade-off between speed and resiliency [55] John Bethencourt, Dawn Song, and Brent Waters, “Analysis-resistant of flash worms and similar malcodes,” in Proceedings of 5th ACM malware,” in Proceedings of the 15th IEEE Network and Distributed Workshop on Recurring Malcode (WORM), Alexandria VA, October System Security Symposium (NDSS), San Diego, CA, Febrary 2008. 2007. [56] Monirul Sharif, Jonathon Giffin, Wenke Lee, and Andrea Lanzi, “Im- [32] Y. Yang, S. Zhu, and G. Cao, “Improving sensor network immunity peding malware analysis using conditional code obfuscation,” in under worm attacks: A software diversity approach,” in Proceedings Proceedings of the 15th IEEE Network and Distributed System Security of ACM International Symposium on Mobile Ad Hoc Networking and Symposium (NDSS), San Diego, CA, Febrary 2008. Computing (MobiHoc), Hong Kong, May 2008. [57] Igor V. Popov, Saumya K. Debray, and Gregory R. Andrews, “Binary [33] L. Martignoni D. Bruschi and M. Monga, “Detecting self-mutating obfuscation using signals,” in Proceedings of the 17th USENIX Security malware using control flow graph matching,” in Proceedings of the Symposium (SECURITY), San Jose, CA, July 2008. Conference on Detection of Intrusions and Malware and Vulnerability [58] M. Christodorescu and S. Jha, “Testing malware detectors,” in Assessment (DIMVA), Berlin, Germany, 2006 July. Proceedings of the 2004 ACM SIGSOFT International Symposium on [34] R. Perdisci, O. Kolesnikov, P. Fogla, M. Sharif, and W. Lee, “Polymor- Software Testing and Analysis (ISSTA), Boston, MA, July 2004. phic blending attacks,” in Proceedings of the 15-th USENIX Security [59] X. Wang, W. Yu, X. Fu, D. Xuan, and W. Zhao, “iloc: An invisible local- Symposium (SECURITY), Vancouver, B.C., August 2006. ization attack to internet threat monitoring systems,” in Proceedings of [35] Linux.com, Understanding Stealth Scans: Forewarned is Forearmed, the 27th IEEE International Conference on Computer Communications http://security.itworld.com/4363/LWD010321vcontrol3/page1.html. (INFOCOM) Mini-conference, Phoenix, AZ, April 2008. [36] Solar Designer, Designing and Attacking Port Scan Detection Tools, [60] J. Bethencourt, J. Frankin, and M. Vernon, “Mapping internet sensors http://www.phrack.org/phrack/53/P53-13. with probe response attacks,” in Proceedings of the 14-th USNIX [37] J. Z. Kolter and M. A. Maloof, “Learning to detect malicious executables Security Symposium, Baltimore, MD, July-August 2005. in the wild,” in Proceedings of the 10th ACM International Conference [61] Y. Shinoda, K. Ikai, and M. Itoh, “Vulnerabilities of passive internet on Knowledge Discovery and Data Mining (SIGKDD), Seattle, WA, threat monitors,” in Proceedings of the 14-th USNIX Security Sympo- August 2004. sium, Baltimore, MD, July-August 2005. Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 13. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 13 [62] S. Soundararajan and D. L. Wang, “A schema-based model for phonemic propagation status and can be integrated with other strategies restoration,” Tech. Report, OSU-CISRC-1/04-TR03, Department of to improve the accuracy of tracking worm propagation status. Computer Science and Engineering, The Ohio State University, January 2004. [63] N. S. Jayant and P. Noll, Digital Coding of Waveforms, Prentice-Hall, Appendix B: SFM of the C-Worm 1984. [64] R. E. Yantorno, K. R. Krishnamachari, J. M. Lovekin, D. S. Benincasa, We present a formal analysis of SFM for the C-Worm as and S. J. Wenndt, “The spectral autocorrelation peak valley ratio (sapvr) follows: Let the observation Z1 be given by Z1 = X1 + Y1 , - a usable speech measure employed as a co-channel detection system,” where X1 is the random variable representing the C-Worm in Proceedings of IEEE International Workshop on Intelligent Signal Processing (WISP), Budapest, Hungary, May 2001. scanning traffic (e.g., volume, destination counter) in one [65] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Second sampling window and Y1 is the random variable representing Edition, Elsevier Science, 2003. the background scanning traffic (e.g., volume, source counter) in one sampling window. We define X = X1 − E[X1 ], where A PPENDIX E[X1 ] is the mean value of X1 and Y = Y − E[Y1 ]. Thus, Appendix A: Estimation of M (t) we have Z = X + Y , where X and Y are independent zero- mean random variables. We assume that the total frequency is We now discuss how the C-Worm can estimate M (t) (number of worm instances or infected computers) during run-time (i.e, within the −W ≤ f ≤ W range. Based on the observations in Section 4.1, we approximately during C-Worm propagation). While there are many possible represent Y1 (t) by white Gaussian noise, which is widely ways to estimate M (t), we only discuss one approach the C- used in modeling wide-band noise in communication systems. Worm could use based on the limited network and computing Thus, Y can be approximately represented by a Gaussian white resources available during its propagation. There are other noise with zero mean and a variance of σ. Thus, in the total approaches, such as incorporating the Peer-to-Peer techniques frequency band limited within the range ∈ [−W ≤ f ≤ W ], to disseminate information through secured IRC channels [49], the PSD of Y is SY (f ) = σ shows that Y has a constant [50]. Actually, a worm could take advantage of the knowledge power spectrum and each frequency has the average power that an infection attempt was a new hit (reaching a previously value σ. uninfected vulnerable computer) and duplicate hit (reaching a Considering the fact that all C-Worm instances adopt a previously infected vulnerable computer). similar control mechanism strategy to manipulate the overall To estimate M (t), we use an approach that is similar in scanning traffic volume, we explained how a distinct trend nature to the approach used by the “self-stopping” worms can be noticed in the frequency domain, i.e., the trend being that do not require a global overlay control network [16] for a concentration in the scanning traffic frequency of the C- realizing their behavior in practice. We call our approach to Worm within a narrow range of frequencies. Assume that C- estimate M (t) as the Distributed Co-ordination method. In this Worm scanning traffic counter is referred as m (denoted by method, there is no centralized co-ordination between the C- fk , where k = 1, . . . , m and m < W ) in the total (narrow- Worm instances to obtain feedback information about the value band) frequency range. Without loss of generality, X(t) is ap- of M (t). The distributed co-ordination requires each C-Worm 2m proximately represented by X(t) = k=1 ak cos(2πfk t + θ), infected computer to be marked with a watermark indicating where θ is uniformly distributed in the interval [0, 2π]) and that the C-Worm infection code has already been installed ak is uniformly distributed in the interval [−l, l]. Based on on the scanned host as with “Code-Red” worms. Thus, when the relationship among autocorrelation, mean, and autoconva- an already infected computer (say for example, host A) scans riance, we have RX (τ ) = CX (t1 , t2 ) + E[X(t1 )]E[X(t2 )], another infected computer (say for example, computer B), then where τ = t2 − t1 , E[X(t1 )] = E[X(t2 )] = 0, and computer A will detect the watermark and know that computer CX (t1 , t2 ) = E[(X(t1 ) − EX (t1 ))(X(t2 ) − EX (t2 ))] is B has already been infected. By scanning vulnerable computers the auto-covariance of a random process X(t). Thus, it and obtaining the watermarks information during the scanning, m ak 2 a C-Worm instance can estimate M (t) at run-time as follows. is easy to verify that RX (τ ) = k=1 [ 2 cos(2πfk τ )]. Let us assume that T , which refers to the whole Internet Thus, the PSD of X(t) can be represented by SX (f ) = k=m ak 2 ak 2 IP address space, is the C-Worm scanning target space. In k=1 [ 4 δ(f − fk ) + 4 δ(f + fk )]. As X(t) and Y (t) this scanning target space, assume we have the case where are independent random process (SY (f ) = σ), we have k=m 2 2 H(t) number of scans in time t resulted in K(t) number SZ (f ) = k=1 [ ak δ(f − fk ) + ak δ(f + fk )] + σ. 4 4 2 of infected computers indicated by presence of watermarks Define ak 4σ k ) = R( δ(f 1), SZ (f ) can be rewritten k=m (identified by duplicate hits). We model the number of infected by SZ (f ) = σ{ k=1 [Rδ(f − fk ) + Rδ(f + fk )] + 1} computers indicated by watermarks during the scanning pro- and the SFM of Z(t) can be represented by SZ (f ) = 1 m cess as binomial process. Then H(t) scanning tries and each σ2W −2m Rσ 2m 2W = RW We can rewrite SZ (f ) ¯ (t) 1 [2mσR+σ(2W −2m)] m (R−1)+1 . scanning try has successful probability MT to be indicated 2W W xt ¯ by watermarks, where M (t) is the estimated M (t) at time in above formula as the function of R as F (x) = t(x−1)+1 , ¯ (t) txt−1 (x−1)(t−1) t. Thus K(t) = H(t) · MT . With the above equation, we where x = R, t = W < 1. As F (x) = m [t(x−1)+1]2 < 0, ¯ ·K(t) have M (t) = TH(t) . Note that the above watermarking-based the function SZ (f ) is a decreasing function of x (= R) and it 2 method might lack the accuracy for the worm propagator to is observable that R1 = ak 4δ(f ) + 1 σ 1 (due to the Dirac’s track the accurate status of worm propagation. Nevertheless, δ function property), SZ (f ) → 0. Thus, the SFM of C-Worm this approach can provide a rough estimation for the worm is close to 0. Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.
  • 14. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING ,VOL. 8, NO. 3, MAY-JUNE 2011 14 Appendix C: SFM of PRS Worm Prasad Calyam Dr. Prasad Calyam received the BS degree in Electrical and Electronics In the following, we conduct analysis on the spectrum-based Engineering from Bangalore University, India, detection scheme to detection PRS worms. Let the input for and the MS and Ph.D. degrees in Electrical detection data Z1 in one sample window be given by Z1 = and Computer Engineering from The Ohio State University, in 1999, 2002, and 2007 respec- X1 + Y1 , where X1 is the random variable representing the tively. He is currently a Senior Systems Devel- attack traffic (e.g., scan volume) for PRS worms and Y1 is oper/Engineer at the Ohio Supercomputer Cen- the random variable representing the background traffic (e.g., ter, The Ohio State University. His current re- search interests include multimedia networking, scan volume). We define X = X1 − E[X1 ], where E[X1 ] is cyber security, cyber infrastructure systems, and the mean value of X1 . We also define Y = Y1 − E[Y1 ], where network management. E[Y1 ] is the mean value of Y1 . Thus, we have Z = X + Y , where X and Y are independent zero-mean random variables. We assume the overall frequency is within −W ≤ F ≤ W range. Similarly, we approximately represent Y1 (t) by white Gaussian noise. Thus, Y can be approximately represented by a white noise with zero mean and a standard derivation of σ. Thus, in the frequency band limited within [−W ≤ F ≤ W ], the PSD of Y is SY (F ) = σ, which shows that frequency Dong Xuan Dr. Dong Xuan received the BS Y ∈ [−W, W ] has a constant power spectrum. Based on the and MS degrees in electronic engineering from SFM definition, it is easy to observe that SFM of Y is close Shanghai Jiao Tong University (SJTU), China, in 1990 and 1993, and the PhD degree in computer to 1. engineering from Texas A&M University in 2001. According to PRS worm propagation dynamics in For- Currently, he is an associate professor in the De- mula (1), the result is M (t) eβ·N ·t at the early stage partment of Computer Science and Engineering, The Ohio State University. He was on the faculty of worm propagation. As β · N · t 1 at the early stage, of Electronic Engineering at SJTU from 1993 to M (t) 1 + β · N · t. Follow the similar procedure, the PSD of 1997. In 1997, he worked as a visiting research 1 X = f (t)−E(f (t)) can be represented by SX (F ) = β·N · F 2 , scholar in the Department of Computer Science, 1 City University of Hong Kong. From 1998 to 2001, he was a research where F ∈ [−W, W ]. Thus, the PSD of Z is β · N · F 2 + σ. assistant/associate in Real-Time Systems Group of the Department According to Formula (7), we can observe that the value of of Computer Science, Texas A&M University. He is a recipient of the SFM for PRS worms is close to 0. It indicates that spectrum- US National Science Foundation (NSF) CAREER award. His research interests include distributed computing, computer networks and cyber based scheme can detect the PRS worm propagation at the space security. early stage as well. Wei Yu Dr. Wei Yu is an assistant professor in the Department of Computer and Informa- tion Sciences, Towson University, Towson, MD 21252. Before that, He worked for Cisco Sys- Wei Zhao Dr. Wei Zhao is currently the Rector tems Inc. for almost nine years. He received of the University of Macau. Before joining the the BS degree in Electrical Engineering from University of Macau, he served as the Dean of Nanjing University of Technology in 1992, the the School of Science at Rensselaer Polytechnic MS degree in Electrical Engineering from Tongji Institute. Between 2005 and 2006, he served as University in 1995, and the PhD degree in com- the director for the Division of Computer and puter engineering from Texas A&M University in Network Systems in the US National Science 2008. His research interests include cyber space Foundation when he was on leave from Texas security, computer network, and distributed systems. A&M University, where he served as Senior As- sociate Vice President for Research and Profes- sor of Computer Science. He was the founding director of the Texas A&M Center for Information Security and Assur- ance, which has been recognized as a Center of Academic Excellence in Information Assurance Education by the National Security Agency. Dr. Zhao completed his undergraduate program in physics at Shaanxi Normal University, Xian, China, in 1977. He received the MS and PhD Xun Wang Dr. Xun Wang received the BS and degrees in Computer and Information Sciences at the University of MS in computer engineering from The East Massachusetts at Amherst in 1983 and 1986, respectively. Since then, China Normal University, Shanghai, China, in he has served as a faculty member at Amherst College, the University 1999 and 2002, and the PhD degree in Com- of Adelaide, and Texas A&M University. As an elected IEEE fellow, Wei puter Science and Engineering from The Ohio Zhao has made significant contributions in distributed computing, real- State University in 2007. He has been working time systems, computer networks, and cyber space security. for Cisco Systems, Inc. since 2007. His research interests include network security, overlay net- works, and wireless sensor networks. Authorized licensed use limited to: Universidade de Macau. Downloaded on July 16,2010 at 02:00:53 UTC from IEEE Xplore. Restrictions apply.

×