1. Presented at the International Workshop on Research Challenges in Security and Privacy for Mobile and Wireless Networks (WSPWN 2006), Miami, Florida, March 15-16, 2006 CS 6910: Advanced Computer and Information Security Lecture 2b Opportunistic Networks: The Concept and Research Challenges in Privacy and Security Leszek Lilien, Zille Huma Kamal, Vijay Bhuse and Ajay Gupta WiSe (Wireless Sensornets) Lab WiSe (Wireless Sensornets) Lab http://www.cs.wmich.edu/wsn Department of Computer Science Western Michigan University Kalamazoo, MI 49008

2. March 15-16, 2006 > CS 6910: Go to Slide 15 CS 6910: Go to Slide 15 < Basic Concepts for Opportunistic Networks New paradigm and technology: New paradigm and technology: opportunistic networks or oppnets Innovative Innovative Facing the challenge of pervasive computing Facing the challenge of pervasive computing Advancing leading-edge pervasive computing and networking know- how Advancing leading-edge pervasive computing and networking know- how Oppnet deployed as a seed oppnet Oppnet deployed as a seed oppnet Localizes its nodes Localizes its nodes Configures itself Configures itself Adapts to environment Adapts to environment 2

3. March 15-16, 2006 Startup: Seed Oppnet 3 Oppnet starts as a seed oppnet Oppnet starts as a seed oppnet Seed oppnet grows into an expanded oppnet Seed oppnet grows into an expanded oppnet Seed Nodes Link to the World Controller (distributed)

4. March 15-16, 2006 Growth: Expanded Oppnet 4 Heterogenous helpers join oppnet Heterogenous helpers join oppnet Add communication, computing, sensing, storage, other resources Add communication, computing, sensing, storage, other resources Seed Nodes Link to the World Controller (distributed) Appliance (refrigerator) Computer Network Micro- wave Relay Overturned Vehicle with OnStar Cellphone Tower Satellite

5. March 15-16, 2006 Oppnet Growth Activities Detecting & identifying candidate helpers Detecting & identifying candidate helpers Contacting & inviting selected candidates Contacting & inviting selected candidates Admitting & integrating helpers that join oppnet Admitting & integrating helpers that join oppnet Offloading tasks to helpers Offloading tasks to helpers Determining useful colaborative functionalities Determining useful colaborative functionalities Managing offloaded tasks Managing offloaded tasks Clean up and release each helper when no longer needed 5

6. March 15-16, 2006 Basic Oppnet Categories 2 major oppnet categories: 2 major oppnet categories: Benevolent oppnets Benevolent oppnets Malevolent oppnets Malevolent oppnets Corresponding oppnets scenarios: Corresponding oppnets scenarios: Benevolent oppnet scenario: Benevolent oppnet scenario: „Citizens Called to Arms” Malevolent oppnet scenario: Malevolent oppnet scenario: „Bad Guys Gang Up” „Bad Guys Gang Up” 6

7. March 15-16, 2006 Benevolent Oppnet Scenario: „Citizens Called to Arms” (1) Seed oppnet deployed Seed oppnet deployed after an earthquake (un- predictable emergency) Seed is ad hoc wireless network with very powerful nodes Seed is ad hoc wireless network with very powerful nodes More energy, computing and communication resources More energy, computing and communication resources Seed tries to detect candidate helpers Seed tries to detect candidate helpers For help in damage assessment and disaster recovery For help in damage assessment and disaster recovery Uses any available detection method — including: Uses any available detection method — including: Celphone- or radio-based detection Celphone- or radio-based detection Searching for nodes using the IP address range for the affected geographic area Searching for nodes using the IP address range for the affected geographic area AI-based visual detection (next) AI-based visual detection (next) 7 7

8. March 15-16, 2006 Benevolent Oppnet Scenario: „Citizens Called to Arms” (2) Example: Example: Helper 1 monitoring a surveillance net detects an overturned car Helper 1 monitoring a surveillance net detects an overturned car Helper 2 asked to recognize its license plate Helper 2 asked to recognize its license plate Helper 3 finds that the cars has OnStar link Helper 3 finds that the cars has OnStar link Helper 4 contacts BANs (Body Area Network) on or within bodies of car occupants via OnStar infrastructure Helper 4 contacts BANs (Body Area Network) on or within bodies of car occupants via OnStar infrastructure Helper 5 evaluates obtained info and dispatches rescuers Helper 5 evaluates obtained info and dispatches rescuers 8 8

9. March 15-16, 2006 Benevolent Oppnet Scenario: „Citizens Called to Arms” (3) Oppnet selects optimal subset of detected nodes Oppnet selects optimal subset of detected nodes Inviting devices, clusters & entire networks Inviting devices, clusters & entire networks Helpers for communicating, sensing, computing Helpers for communicating, sensing, computing Using „hidden” capabilities, e.g. for sensing: Using „hidden” capabilities, e.g. for sensing: Desktop can „sense” presence of a potential victim at its keyboard Desktop can „sense” presence of a potential victim at its keyboard Cellphones can „sense” location Cellphones can „sense” location Even ones w/o GPS can be triangulated Even ones w/o GPS can be triangulated 9 9

10. March 15-16, 2006 Using „hidden” Using „hidden” emergency functionalities Oppnet contacts 2 independent sensornets (SNs): Oppnet contacts 2 independent sensornets (SNs): water infrastructure control SN / water infrastructure control SN / public space surveillance SN public space surveillance SN SNs ordered to abandon normal functions & help in rescue & recovery operations SNs ordered to abandon normal functions & help in rescue & recovery operations Water infrastructure SN (with multisensor capabilities, under road surfaces) — ordered to sense vehicular movement and traffic jams Water infrastructure SN (with multisensor capabilities, under road surfaces) — ordered to sense vehicular movement and traffic jams Public space surveillance SN — ordered to search for images of human victims Public space surveillance SN — ordered to search for images of human victims 10 Benevolent Oppnet Scenario: „Citizens Called to Arms” (4)

11. March 15-16, 2006 11 Malevolent Oppnet Scenario: „Bad Guys Gang Up” (1) 11 Scenario 1 — Terrorists Scenario 1 — Terrorists create apparently harmless weather monito- ring sensornet (SN): SN becomes a seed of a malevolent opportunistic SN SN becomes a seed of a malevolent opportunistic SN SN exploits other nodes from many other networks (w/o revealing its true goals) SN exploits other nodes from many other networks (w/o revealing its true goals) “Critical mass” of the opportunistic SN is reached (in terms of geographical spread and sensing capabilities) “Critical mass” of the opportunistic SN is reached (in terms of geographical spread and sensing capabilities) SN waits for wind patterns that can speed up spread of poisonous chemicals SN waits for wind patterns that can speed up spread of poisonous chemicals Collected data used to decide when to start chemical attack Collected data used to decide when to start chemical attack

12. March 15-16, 2006 12 Malevolent Oppnet Scenario: „Bad Guys Gang Up” (2) Scenario 2 — network at home starts spying on you: Scenario 2 — network at home starts spying on you: Becomes a seed oppnet Becomes a seed oppnet Exploits other devices/nets to collect all info on you: Exploits other devices/nets to collect all info on you: From your fridge (& RFID-equipped food packaging): what/when you eat From your fridge (& RFID-equipped food packaging): what/when you eat From your computer: keylogs your passwords, sensitive data From your computer: keylogs your passwords, sensitive data From your cellphone: who you call & when From your cellphone: who you call & when From your networked camera: what photos you take From your networked camera: what photos you take From your home security surveillance system: your private images From your home security surveillance system: your private images Cyberfly with camera eyes and microphone ears Cyberfly with camera eyes and microphone ears...... Huge privacy problem! / Huge security problem! Huge privacy problem! / Huge security problem! Controls to counteract malevolent oppnets badly needed Controls to counteract malevolent oppnets badly needed

13. March 15-16, 2006 Related Research Interoperability Interoperability Among wireless networks: WANs, MANs, LANs, PANs (personal) Among wireless networks: WANs, MANs, LANs, PANs (personal) Much less research on interoperability between wired & wireless nets Much less research on interoperability between wired & wireless nets Ambient networks (big European Union project, next-generation Internet—for 2015/2020, smaller networks able to compose themselves into bigger ones) Ambient networks (big European Union project, next-generation Internet—for 2015/2020, smaller networks able to compose themselves into bigger ones) Growth in P2P systems Growth in P2P systems Searching for peers in unstructured systems Searching for peers in unstructured systems Grid Systems Grid Systems Integrating and managing heterogeneous systems Integrating and managing heterogeneous systems Trojan Horses Trojan Horses Mimic their spread capabilities in search for helpers Mimic their spread capabilities in search for helpers Other Other 13

14. March 15-16, 2006 Research Challenges in Basic Operations Bypassed in this presentation Bypassed in this presentation Include: Include: Challenges in Seed Oppnet Deployment Challenges in Seed Oppnet Deployment E.g., localization, self-configuration, adatptability E.g., localization, self-configuration, adatptability Challenges in Detecting Helper Systems Challenges in Detecting Helper Systems E.g., primitives to detect candidates, identify and categorize them, evaluate and classify them (e.g., based on dependability and usefulness) E.g., primitives to detect candidates, identify and categorize them, evaluate and classify them (e.g., based on dependability and usefulness) Challenges in Inviting & Admitting Candidate Helpers Challenges in Inviting & Admitting Candidate Helpers E.g., select candidates to invite, develop protocols for candidates to accept or reject invitation, devise primitives /methods to manage expanded oppnet E.g., select candidates to invite, develop protocols for candidates to accept or reject invitation, devise primitives /methods to manage expanded oppnet Etc., etc. for remaining operations Etc., etc. for remaining operations 14

15. March 15-16, 2006 > CS 6910: Start here CS 6910: Start here < Research Challenges in Security and Privacy 1) Major privacy challenges in oppnets 2) Security challenges in oppnets With secondary privacy challenges With secondary privacy challenges 15

16. March 15-16, 2006 Major Privacy Challenges (1) Privacy challenges in oppnets Privacy challenges in oppnets Oppnets are and use pervasive systems Oppnets are and use pervasive systems Must face all privacy challenges inherent to pervasive computing Must face all privacy challenges inherent to pervasive computing „Make it or break it” issue for oppnets (and perv. comp) „Make it or break it” issue for oppnets (and perv. comp) Major privacy goals Major privacy goals Assure privacy of communications and data storage Assure privacy of communications and data storage Protect helper resources from the host oppnet Protect helper resources from the host oppnet Protect oppnet from its helpers Protect oppnet from its helpers Protect environment from privacy violations by oppnet Protect environment from privacy violations by oppnet Also from malevolent oppnets Also from malevolent oppnets 16

17. March 15-16, 2006 Major Privacy Challenges (2) Classes of solutions to achieve the privacy goals Classes of solutions to achieve the privacy goals Provide protected private areas within seed nodes/helpers Provide protected private areas within seed nodes/helpers Anonymize or pseudonimize entities within oppnet range Anonymize or pseudonimize entities within oppnet range Detect and neutralize malevolent oppnets Detect and neutralize malevolent oppnets Detect and neutralize exploiting oppnets for privacy violations Detect and neutralize exploiting oppnets for privacy violations Special solutions for emergency oppnet applications Special solutions for emergency oppnet applications Strict privacy protection relaxed in life-or-death situations Strict privacy protection relaxed in life-or-death situations Must follow law and ethics Must follow law and ethics Basic assumptions: Basic assumptions: Entity gives up only as much privacy as indispensable for becoming a helper Entity gives up only as much privacy as indispensable for becoming a helper Entity’s privacy disclosure is proportional to: Entity’s privacy disclosure is proportional to: Benefits for the entity, or Benefits for the entity, or A broader common good A broader common good 17

18. March 15-16, 2006 Security Challenges (1) Sources of security challenges Sources of security challenges Dependable authentication cannot be performed when helpers join oppnet Dependable authentication cannot be performed when helpers join oppnet Not possible to guarantee that malicious devices will not join Not possible to guarantee that malicious devices will not join Can detect notorius behavior after entity becomes a helper Can detect notorius behavior after entity becomes a helper If available, reputation can be used beforehand If available, reputation can be used beforehand Delivering secret keys securely to all and only non- malicious devices is very difficult Delivering secret keys securely to all and only non- malicious devices is very difficult Relying alone on crypto authentication mechanisms (e.g., Kerberos) not sufficient Relying alone on crypto authentication mechanisms (e.g., Kerberos) not sufficient => security challenges in oppnets are bigger Incl. MITM, packet dropping, ID spoofing (masquerading), DoS Incl. MITM, packet dropping, ID spoofing (masquerading), DoS 18

19. March 15-16, 2006 The major security (and privacy) challenges: The major security (and privacy) challenges: Secure routing via increasing trust Secure routing via increasing trust Routing through more trusted systems Routing through more trusted systems Shared secrets for each communicating pair Shared secrets for each communicating pair Using shared secrets with broadcast authentication Using shared secrets with broadcast authentication Using digital signatures Using digital signatures … Helper privacy and oppnet privacy via intrusion detection (also above) Helper privacy and oppnet privacy via intrusion detection (also above) Protecting data privacy and data integrity Protecting data privacy and data integrity Identifying and preventing most dangerous attacks Identifying and preventing most dangerous attacks Intrusion detection Intrusion detection All discussed next All discussed next 19 Security Challenges (2)

20. March 15-16, 2006 Secure routing via increased trust Secure routing via increased trust Maintain list of “more trusted” entities and list of „less trusted” entities Maintain list of “more trusted” entities and list of „less trusted” entities Secure routing can use both lists Secure routing can use both lists Secure wireless ad hoc routing protocol most relevant for opnets: Ariadne [Hu, Perrig, and Johnson, 2002] Secure wireless ad hoc routing protocol most relevant for opnets: Ariadne [Hu, Perrig, and Johnson, 2002] On-demand protocol On-demand protocol Works in the presence of compromised nodes Works in the presence of compromised nodes Uses symmetric cryptography Uses symmetric cryptography Authenticates routing messages Authenticates routing messages Still, cannot use directly Still, cannot use directly More heterogeneous (esp. w.r.t. wired/wireless transmission media) More heterogeneous (esp. w.r.t. wired/wireless transmission media) Can look for less energy-efficient oppnet solutions Can look for less energy-efficient oppnet solutions Can rely on growth to amass needed resources (even with a big safety margin) Can rely on growth to amass needed resources (even with a big safety margin) 20 Secure Routing via Increased Trust

21. March 15-16, 2006 Protect privacy via detecting intrusions, illegal resource accesses Protect privacy via detecting intrusions, illegal resource accesses Helper privacy supported via: Helper privacy supported via: Access control (authentication and authorization) Access control (authentication and authorization) Intrusion detection Intrusion detection 2nd line of privacy defense 2nd line of privacy defense Meant to work by scaring away attackers Meant to work by scaring away attackers More difficult than in many other nets More difficult than in many other nets Bec. of heterogeneity, spontaneous growth Bec. of heterogeneity, spontaneous growth Oppnet privacy supported via: Oppnet privacy supported via: Intrusion detection Intrusion detection Catches helpers that become attackers Catches helpers that become attackers 21 Helper Privacy and Oppnet Privacy via Intrusion Detection

22. March 15-16, 2006 Data privacy challenges Data privacy challenges Capture of even a single oppnet entity (especially in crisis when providing physical protection is even more difficult) cripples whole symmetric key cryptography scheme Capture of even a single oppnet entity (especially in crisis when providing physical protection is even more difficult) cripples whole symmetric key cryptography scheme Attacker masquerading as controller (or cluster head) can distribute its own crypto keys Attacker masquerading as controller (or cluster head) can distribute its own crypto keys Data integrity challenges Data integrity challenges Digital signatures are expensive computationally for lightweight devices (cellphone, PDA, etc.) Digital signatures are expensive computationally for lightweight devices (cellphone, PDA, etc.) Packet format convesrsions can be attacked Packet format convesrsions can be attacked Heterogeneous entities/media fragment/aggregate packets Heterogeneous entities/media fragment/aggregate packets 22 Protecting Data Privacy and Data Integrity

23. March 15-16, 2006 MITM: e.g., malicious device becomes a MITM on the communication line between a victim and first responders MITM: e.g., malicious device becomes a MITM on the communication line between a victim and first responders Solution: Use mutliple, heterogenous routes between victim and the center forredundant message Solution: Use mutliple, heterogenous routes between victim and the center forredundant message Packet dropping: e.g., malicious device drops some packets between a victim and the center Packet dropping: e.g., malicious device drops some packets between a victim and the center Solution: As above (will work if no adversary on at ≥ one route) Solution: As above (will work if no adversary on at ≥ one route) DoS attacks: e.g., flooding emergency center with false requests for help DoS attacks: e.g., flooding emergency center with false requests for help Solution: Limit number of requests any device can generate. „Call back” the victim to confirm her emergency request. Solution: Limit number of requests any device can generate. „Call back” the victim to confirm her emergency request. Other: DoS attacks on weak links, ID spoofing,... Other: DoS attacks on weak links, ID spoofing,... 23 Identifying and Preventing Most Dangerous Attacks - Examples

24. March 15-16, 2006 Motivation – Why needed? Motivation – Why needed? When prevention fails When prevention fails Lack of initial authentication mechanism Lack of initial authentication mechanism Challenges: Challenges: Securely distributing information about malicious entities in the presence of other (unknown) malicious entities Securely distributing information about malicious entities in the presence of other (unknown) malicious entities Avoiding malicious entities while maintaining connectivity Avoiding malicious entities while maintaining connectivity Real-time intrusion detection and response more difficult than in other networks types Real-time intrusion detection and response more difficult than in other networks types Bec. highly heterogeneous Bec. highly heterogeneous 24 Intrusion Detection (1)

25. March 15-16, 2006 Possible intrusion detection approach: [Zamboni, 2001] Possible intrusion detection approach: [Zamboni, 2001] Internal „software sensors” used as embedded detectors Internal „software sensors” used as embedded detectors Intrusion detection performed by autonomous agents using embedded detectors Intrusion detection performed by autonomous agents using embedded detectors Benefits of embedded detectors: Benefits of embedded detectors: More resistant to tampering or disabling, because they are a part of the program they monitor. More resistant to tampering or disabling, because they are a part of the program they monitor. Very low CPU overhead (not executing continuously) Very low CPU overhead (not executing continuously) Perform direct monitoring have access to the internal data of programs they monitor) Perform direct monitoring have access to the internal data of programs they monitor) Detection data is safer—does not travel through an external path (a log file, for example) between its generation and its use Detection data is safer—does not travel through an external path (a log file, for example) between its generation and its use 25 Intrusion Detection (2)

26. March 15-16, 2006 Conclusions Oppnets are a new wide category of networks Oppnets are a new wide category of networks Leverage resources they can detect in the vicinity Leverage resources they can detect in the vicinity Sensing / monitoring / computing / communication / etc. resources Sensing / monitoring / computing / communication / etc. resources Particularly well suited to emergency operations Particularly well suited to emergency operations Starts with a buildup of communications infrastructure Starts with a buildup of communications infrastructure Applicable for non-emergency situations as well Applicable for non-emergency situations as well High-payoff potential for this paradigm/technology High-payoff potential for this paradigm/technology Reduction of human suffering & loss of life Reduction of human suffering & loss of life Economic benefits Economic benefits Technological, educational & research benefits Technological, educational & research benefits 26

27. March 15-16, 2006 Future Work Investigating oppnet fundamentals Investigating oppnet fundamentals Designing oppnet architecture Designing oppnet architecture With its associated components With its associated components Methods, protocols, and algorithms Methods, protocols, and algorithms Building a prototype Building a prototype For stimulation and feedback For stimulation and feedback Necessary for fine-tuning oppnet design Necessary for fine-tuning oppnet design Proof of concept: technical prowess & economic benefits Proof of concept: technical prowess & economic benefits 27

28. March 15-16, 2006 Thank you very much for your time and attention! 28

29. March 15-16, 2006 Selected WiSe Lab Publications on Sensornets, Oppnets & Pervasive Computing * Directly related to oppnets 1.L. Lilien and A. Gupta, ” Opportunistic Networks for Emergency Preparadness and Response” (submitted). (*) 2.V. Bhuse, A. Gupta, and L. Lilien, "Research challenges in lightweight intrusion detection for sensornets" (submitted). 3.L. Lilien and B. Bhargava, ”A Scheme for Privacy-preserving Data Dissemination,” IEEE Transactions on Systems, Man and Cybernetics (to appear). 4.L. Lilien, Z. Kamal, V. Bhuse and A. Gupta, "Opportunistic Networks: The Concept and Research Challenges in Privacy and Security,” International Workshop on Research Challenges in Security and Privacy for Mobile and Wireless Networks (WSPWN 2006), Miami, Florida, March 2006. (*) 5.T. Canli, M. Terwilliger, A. Gupta and A. Khokhar, "Power Efficient Algorithms for Computing Fast Fourier Transform over Wireless Sensor Networks," The Fourth ACS/IEEE Conference on Computer Systems and Applications, Dubai, UAE, March 2006. 6.V. Bhuse, A. Gupta and L. Lilien, "DPDSN: Detection of packet-dropping attacks for wireless sensor networks," Proceedings of the 4th International Trusted Internet Workshop (TIW), International Conference on High Performance Computing, Goa, India, December 2005. 7.A. Gupta and V. Bhuse, "Anamoly Intrusion Detection in Wireless Sensor Networks," Journal of High Speed Networks, vol. 15, issue 1, January- March 2006. 8.M. Terwilliger, A. Gupta, A. Khokhar and G. Greenwood, "Localization using Evolution Strategies in Sensornets," Proceedings of the IEEE Congress on Evolutionary Computation, Edinburgh, UK, September 2005. 9.V. Bhuse, A. Gupta, M. Terwilliger, Z. Yang and Z. Kamal, "Using Routing Data for Information Authentication in Sensor Networks," Proceedings of the 3rd International Trusted Internet Workshop (TIW), International Conference on High Performance Computing, Bangalore, India, December 2004. 10.T. Canli, M. Terwilliger, A. Gupta and A. Khokhar, "Power-Time Efficient Algorithm for Computing FFT in Sensor Networks," (Extended Abstract). Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys), Baltimore, Maryland, November 2004. 11.B. Bhargava, L. Lilien, A. Rosenthal, and M. Winslett, “PervasiveTrust,” IEEE Intelligent Systems, vol. 19(5), Sep./Oct.2004, pp. 74-77. (*) 12.B. Bhargava and L. Lilien, “Private and Trusted Collaborations,” Proc. Secure Knowledge Management (SKM 2004): A Workshop, Amherst, NY, Sep. 2004. 13.M. Jenamani, L. Lilien, and B. Bhargava, “Anonymizing Web Services Through a Club Mechanism with Economic Incentives,” Proc. International Conference on Web Services (ICWS 2004), San Diego, California, July 2004, pp. 792-795. 14.Z. Kamal, M. Salahuddin, A. Gupta, M. Terwilliger, V. Bhuse and B. Beckmann, "Analytical Analysis of Data and Decision Fusion in Sensor Networks," The 2004 International Conference on Embedded Systems and Applications. Las Vegas, June 2004. 15.M. Terwilliger, A. Gupta, V. Bhuse, Z. Kamal, and M. Salahuddin, "A Localization System Using Wireless Sensor Networks: A Comparison of Two Techniques," Proceedings of the 2004 Workshop on Positioning, Navigation and Communication, Hanover, Germany, March 2004, pp. 95-100. 16.V. Bhuse, A. Gupta and R. Pidva, "A Distributed Approach to Security in Sensornets," The 58th IEEE Semiannual Vehicular Technology Conference, Orlando, Florida, USA, October 2003. 17.L. Lilien, “Developing Pervasive Trust Paradigm for Authentication and Authorization,” Proc. Third Cracow Grid Workshop (CGW’03), Kraków (Cracow), Poland, October 2003, pp. 42-49 (invited paper). 29

30. March 15-16, 2006 WiSe Lab Experience in Sensornets – Selected Projects Since 1/03 * Results useful for oppnets  Designing of WiSe Security Protocols: DSPS  Location Tracker Using Motes (*)  RHS: Remote Home Surveillance (*)  Directed Diffusion: Attacks & Countermeasures  Improving the Accuracy of Mote Measurements by Using Neural Networks  SOMS: Smart Occupancy Monitoring System Using Motes (*)  Comparative Study of Network Simulators  Collaborative Image Processing (*)  DENSe: a Development Environment for Networked Sensors  Incorporating Mobile-ware in Distributed Computations / Grids (*)  Extending the ns-2 Simulator to Satellite and WCN Simulations  Smart Antennas for WCNs  Energy Efficient MAC Protocols for IEEE 802.11x  A Wireless Security Testing System (*)  Mobile and Self-Calibrating Irrigation System  Collective Communications for Sensornets (*) 30