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Computer Worm Ecology in Encounter-Based Networks Computer Worm Ecology in Encounter-based Networks (Invited Paper) Sapon Tanachaiwiwat Ahmed Helmy Ming Hsieh Department of Electrical Engineering Computer and Information Science and Engineering University of Southern California, CA University of Florida, FL [email protected] [email protected] Abstract — Encounter-based network is a frequently- to disease spreading. disconnected wireless ad-hoc network requiring immediate neighbors to store and forward aggregated data for information Using traditional approaches such as gateways or disseminations. Using traditional approaches such as gateways firewalls for deterring worm propagation in encounter-based or firewalls for deterring worm propagation in encounter-based networks is inappropriate. Because this type of network is networks is inappropriate. Because this type of network is highly highly dynamic and has no specific boundary, a fully dynamic and has no specific boundary, we need a fully distributed counter-worm mechanism is needed. We propose distributed security response mechanism. We propose the worm to investigate the worm interaction approach that relies upon interaction approach that relies upon automated beneficial automated beneficial worm generation [1]. This approach uses worm generation aiming to alleviate problems of worm an automatic generated beneficial worm to terminate propagations in such networks. This work is motivated by the ‘War of the Worms’ of the Internet worms between competing malicious worms and patch vulnerable hosts. worms such as NetSky, Bagle and MyDoom. To understand the Our work is motivated by competitions of these Internet dynamic of worm interactions and its performance, we worms. In 2004, majority of worm outbreaks are caused by mathematically model several classes of worms and interactions the “War of the Worms” between NetSky, Bagle and using ordinary differential equations and analyze their behaviors. MyDoom. In this paper, we try to answer following questions: How is the war of the worms affects the worm propagation in I. INTRODUCTION encounter-based networks? What are the possible variants of wars of the worms? and how can we incorporate the An encounter-based network is a frequently-disconnected encounter characteristics to the worm propagations. wireless ad-hoc networks requiring close proximity of This scenario is described as “worm interactions” in neighbors, i.e., encounter, to disseminate information. Hence, which one or multiple types of worm terminate or patch other we call this the “encounter-based network” which can be types of worms. In this paper, we show that the interaction considered as a terrestrial delay-and-disruptive-tolerant causes significant change in the traditional one-type network. It is an emerging technology that is suitable for propagation pattern. Furthermore different types of applications in highly dynamic wireless networks. interactions show entirely different patterns. Originally Most previous work on worm propagation has focused propagation patterns of worms follow variants of phase on modeling single worm type in well-connected wired transition patterns. Hence, we develop a comprehensive novel network. However, many new worms are targeting wireless worm ecology model extending the epidemic model [7] for mobile phones. The characteristics of worms in mobile several classes of worm interactions based on their behaviors, networks are different from random-scan network worms. capabilities and strategies. Our worm ecology model consists Worm propagations in mobile networks depend heavily on of aggressive one-sided, conservative one-sided, aggressive user encounter patterns. Many of those worms rely on two-sided and, two-group aggressive one-sided worm Bluetooth to broadcast their replications to vulnerable phones, interactions. Our worm interaction models focus on worm e.g., Cabir and ComWar.M [14]. Since Bluetooth radios have behaviors and group behavior in encounter-based networks very short range around 10-100 meters, the worms need neighbors in close proximity to spread out their replications. Our main contribution in this paper is our proposed new Hence, we call this “encounter-based worms”. This worm comprehensive Worm Interaction Model categorizing worm spreading pattern is very similar to spread of packet interactions by worm types, sidedness, aggressiveness, and replications in delay tolerant networks [16, 20], i.e., flooding group. This worm interaction model can be extended to the copies of messages to all close neighbors. An earlier study support more complicated current and future worm in encounter-based networks actually used the term “ epidemic interactions in encounter-based networks. routing ” [16] to describe the similarity of this routing protocol Next we discuss related work in Section II. Then, in Section III, we explain worms’ behaviors in our model and Much of this work was performed at the University of Southern California their parameters in details. We discuss multi-group aggressive with support from NSF awards: CAREER 0134650, ACQUIRE 0435505 and one-sided interaction in Section IV. In Section IV, we Intel. conclude our work and discuss the future work. Authorized licensed use limited to: University of Florida. Downloaded on November 28, 2008 at 22:47 from IEEE Xplore. Restrictions apply. Worm Interactions Types Sidedness Aggressiveness Group Single Multiple One-sided Two-sided Aggressive Conservative Single Multiple Fig.1. Worm Interaction Classification automatic patching was also investigated in [17]. Their work II RELATED WORK assumes a patch server and overlay network architecture. We Worm-like message propagation or epidemic routing has provide the mathematical model that can explain the behavior been studied for delay tolerant network applications [16, 20]. of automatic-generated beneficial worm and automatic patch As in worm propagation, a sender in this routing protocol distribution using one-sided worm interaction. In [17] authors spreads messages to all nodes in close proximity, and those assume patch blocking by worms after infection, and hence nodes repeatedly spread the copies of messages until the this scenario yields aggressive two-sided worm interaction messages reach a destination, similarly to generic flooding but which we model in this paper. Our work aims to understand without producing redundant messages. Performance and evaluate automated worm (with patch) generation but we modeling for epidemic routing in delay tolerant networks [20] do not address details of vulnerabilities nor related software based on ODE is proposed to evaluate the delivery delay, loss engineering techniques to generate patches or worms. Active probability and power consumption. They also propose the defense using beneficial worms is also mathematically concept of anti-packet to stop unnecessary overhead from modeled in [10]; however, the work focuses only on one- forwarding extra packets copies after the destination has sided worm interaction for delay-limited worms. Our work in received the packets. This can be considered as a special case [13] focuses more on aggressive one-sided worm interaction of non-zero delay of aggressive one-sided interaction which and impact on networks infrastructure while this work consider in our model. concentrates on worm behaviors resulting from comprehensive worm interactions in encounter-based Epidemic model and its variance, a set of ordinary networks. differential equations, were earlier used to describe the contagious disease spread including SI, SIS, SIR SIRS, SEIR III. W ORM INTERACTION MODEL and SEIRS models [3, 7, 15] in which S, I, E, R stand for We aim to build a fundamental worm propagation model Susceptible, Infected, Exposed and Recovered state that captures worm interaction as a key factor in uniform respectively. We can see the pattern similarity of computer encounter-based networks. Furthermore, our proposed model worm infection and the disease spread in which both of them addresses and analyzes dynamics of susceptible and infected depending on node’s status, i.e., vulnerable, infected or hosts over the course of time. recovered) and encounter pattern. For the Internet worms, numerous worm propagation models have been investigated Because the constant removal rate in basic SIR model in earlier work [5, 6, 8, 21]. However, only few works [1, 10, and its variance [7] cannot directly portray such interactions 12, 13] consider worm interaction among different worm impact on multi-type worm propagations, our model builds types. Our work is focusing more on understanding of how upon and extends beyond the conventional epidemic model to we can systemically categorize and model worm propagation accommodate the notion of interaction. and interaction among each other in encounter-based Basic operation of a worm is to find susceptible nodes to networks. be infected and the main goal of attackers is to have their In [1], the authors suggested modifying existing worms worms infect the largest amount of hosts in the least amount such as Code Red, Slammer and Blaster to terminate the of time, and if possible, undetected by antivirus or intrusion original worm types. The modified code will retain portion of detection systems. Our beneficial worm, on the other hand, attacking method so it would choose and attack the same set aims to eliminate opposing worms or limit the scope of of susceptible hosts. In this paper, we model this as aggressive opposing worms’ infection. We want to investigate the worm one-sided worm interaction. Other active defense
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