PRIMERA ETAPA: APLICADA A 29 POBLADRES DE
7. ANÁLISIS DE LA INFORMACIÓN
7.3 HISTORIA DE VIDA
Design of low-cost honeypots for oppnets is challenging because physical security of honeypots cannot be guaranteed for the entire lifetime of an oppnet. Observations from honeypots cannot be trusted unless secure channels of communication are established. Attackers masquerading as honeypots or posing DoS attacks on honeypots are examples of problems that need to be solved.
We are investigating a hybrid honeyfarm architecture for oppnets that integrates the high-interaction technologies of Collapsar honeyfarm [22] and Potemkin honeyfarm [51], providing both centralized management and decentralized honeypot presence. The resulting system can be made scalable and efficient, using late binding of resources, flash cloning, and redirectors.
5.7 Conclusions
This chapter describes the concept of opportunistic networks (oppnets), and presents the related research challenges in privacy and security.
Oppnets constitute a newly identified category of computer networks. When deployed, oppnets attempt to detect candidate helper systems existing in their relative vicinity—ranging from sensing and monitoring, to computing and communication systems—and integrate them under their own control. When such a candidate is detected by an oppnet, the oppnet evaluates the benefits that it could realize if the candidate joins it. If the
112 Lilien et al.
evaluation is positive the oppnet invites the candidate to become its helper. In this manner, an oppnet can grow from a small seed into a large network with vast sensing, communication, and computation capabilities.
Oppnets will facilitate many applications. As an example, they can help building an integrated network called for in various critical or emergency situations [48]. Oppnets can be used to enable connectivity in an area where any existing communication or information infrastructure has been fractured or partially destroyed. Oppnets will integrate various systems that were not designed to work together. The integration will enhance the flow of information that, for example, can assist in rescue and recovery efforts for devastated areas, or can provide more data on phenomena that are just developing, such as wildfires or flash torrents.
Answering to the identified privacy and security challenges in oppnets will contribute to advancing knowledge and understanding not only for the opportunistic networks, but will simultaneously advance the state of the art of computer privacy and security in ad hoc and in general-purpose computer networks.
We continue working on a number of the identified challenges, continuing our investigation of privacy and security in oppnets. The planned prototype opportunistic network will provide a proof of concept for our solutions, as well as stimulation and feedback necessary for fine- tuning the proposed solutions.
Acknowledgements
This work was supported in part by the National Science Foundation under Grant IIS-0242840, and in part by the U.S. Department of Commerce under Grant BS123456.
The authors would also like to acknowledge Western Michigan University for its support and its contributions to the WiSe (Wireless Sensornet) Laboratory, Computational Science Center and Information Technology and Image Analysis (ITIA) Center.
L. Lilien, a co-PI on the NSF grant providing a partial support for this research, would like to thank Professor Bharat Bhargava from Purdue University, the PI for this grant.
L. Lilien would like to thank the participants of the International Workshop on Research Challenges in Security and Privacy for Mobile and Wireless Networks (WSPWN 2006) for their helpful comments and feedback on oppnets. In particular, he would like to thank Mr. Hien Nguyen, a Ph.D. student at the Florida International University, for a
The Concept of Opportunistic Networks and their Research Challenges 113
fruitful discussion that resulted in crystallizing the idea of the oppnet reserve.
L. Lilien also expresses his thanks to the following students of his advanced computer security course for their contributions to the following research projects: (a) contributors to helper privacy and oppnet privacy: N. Bhargava, T. Goodman, V. Kalvala, H.R. Ravi, R. Rekala, A. Rudra, V. Talati, and Y. Yoder ; (b) contributors to authentication of oppnet nodes and helpers: V.V. Krishna, P.E. Miller, and A.K. Yedugani; (c) contributors to dealing with specific attacks: S. Chittineni, N. Jawanda, D. Koka, S. Pulimamidi, and H. Singh; and (d) contributors to intrusion detection, honeypots and honeyfarms: R. Dondati and S. Mapakshi.
Any opinions, finding, conclusions or recommendation expressed in the paper are those of the authors and do not necessarily reflect the views of the funding agencies or institutions.
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6 On Performance Cost of On-demand
Anonymous Routing Protocols in Mobile
Ad Hoc Networks
Jiejun Kong
1, Jun Liu
2, Xiaoyan Hong
2, Dapeng Wu
3, and
Mario Gerla
41Jiejun Kong is currently with Scalable Network Technologies, Inc., 6701 Center
Drive West, Suite 520, Los Angeles, CA 90045.
2
Jun Liu and Xiaoyan Hong are with the Department of Computer Science, University of Alabama, Tuscaloosa, AL 35487.
3
Dapeng Wu is with the Department of Electric and Computer Engineering, University of Florida, Gainesville, FL 32611.
4Mario Gerla is with the Department of Computer Science, University of
California, Los Angeles, CA 90095.
6.1 Introduction
A mobile ad hoc network (MANET) can establish an instant communica- tion structure for many time-critical and mission-critical applications. Nevertheless, the intrinsic characteristics of ad hoc networks, such as wire- less transmission and node mobility, make it very vulnerable to security threats. Many security protocol suites have been proposed to protect wire- less communications, however, they do not consider anonymity protection and leave identity information freely available to nearby passive eaves- droppers. The goal of passive attacks is very different from those of other attacks on routing, such as route disruption or “denial-of-service” attacks. In fact, the passive enemy will avoid such aggressive schemes, in the at- tempt to be as “invisible” as possible, until it traces, locates, and then physically destroys legitimate assets [29, 51]. Consider for example a bat- tlefield scenario with ad hoc, multi-hop wireless communications support. The adversary could deploy reconnaissance and surveillance sensor net- works in the battlefield and maintain communications among them. Via in- tercepted wireless transmissions, they could infer the location, movement,
120 Kong et al.
number of participants, and even the goals of our task forces. Anonymity and location privacy guarantees for our ad hoc networks are critical, oth- erwise the entire mission may be compromised. This poses challenging constraints on routing and data forwarding.
6.1.1 Mobile sensor networks
Recent advances in manufacturing technologies have enabled the physical realization of small, light-weight, low-power, and low-cost micro air vehi- cles (MAVs) [21,22]. These MAVs refer to a new breed of unmanned air vehicles (UAVs) or aerial robots that are significantly smaller than cur- rently available UAVs. Figure 6.1(a) illustrates the WASP MAV recently tested by DARPA. It is a 32 cm “flying wing” made of a plastic lithium- ion battery material that provides both electrical power and wing structure. The wing utilizes synthetic battery materials, which generate an average output of more than nine watts during flight -- enough power to propel the miniature aircraft for one hour forty-seven minutes. Such aerial robots, equipped with information sensing and transmission capabilities, extend the sphere of awareness and mobility of human beings, and allow for sur- veillance or exploration of environments too hazardous or remote for hu- man beings.
MAVs are expected to serve as an enabling technology for a plethora of civilian and military applications, including homeland security, reconnais- sance, surveillance, tracking of terrorists/suspects, rescue and search, and highway/street patrol. With signal processing techniques (and other out-of- band techniques like visual perception which will not be discussed here), a team of three MAVs can locate the position of a target such as a person’s or a car’s communication interface. Due to the small size of MAVs, the tracking of MAVs is almost unnoticed by the target being tracked (Figure 6.1(b)). The velocity of an MAV is from 10 to 30 miles per hour, which is fast enough to track a human being or an automobile on local roads.
When a mobile ad hoc network is in operation, the mobile sensors car- ried by MAVs can eavesdrop routing messages and data traffic so to trace where a mobile wireless sender node is, infer the motion pattern of the mobile node, or identify a multi-hop path between a pair of nodes [51].
On Performance Cost of On-demand Anonymous Routing Protocols 121
Fig. 6.1(a). Micro Aerial Vehicle (MAV)
122 Kong et al.
6.1.2 On-demand routing
Most routing protocols in ad hoc networks fall into two categories: proac- tive routing and reactive routing (aka., on demand routing) [9]. In proac- tive ad hoc routing protocols like OLSR [1], TBRPF [34] and DSDV [53], mobile nodes constantly exchange routing messages which typically in- clude node identities and their connection status to other nodes (e.g., link state or distance vector), so that every node maintains sufficient and fresh network topological information to allow them to find any intended recipi- ents at any time. On the other hand, on demand routing has become a ma-