Security in Wireless Sensor Networks Michael Krishnan

Outline Types of Attacks Clusters and Intrusion Detection Game Theory Approach

Characteristics of WSNs Limited Energy (~6Ah) Wireless: Intruders can see transmissions and add their own Traffic is either source to sink (base station) or broadcast

Types of Attacks Steal Data – Confidentiality Alter Data – Data Integrity Limit Service Availability (DoS) Consume Energy “Denial of Sleep” D.o.Sleep – Reduce chances to go to low-power states

Confidentiality Public key? Too computationally expensive Secret key? Bad if node is compromised Secure Network Encryption Protocol (SNEP)

SNEP Both sides keep (pair-wise) shared key, k, & shared counter, C, to use as IV in DES Semantic Security Whole network shares MAC() function for authentication: MAC(k,C|{D}) (8 bytes) (Weak) Freshness – replay protection and ordering MAC = msg authentication code

Data Integrity Authentication: Can’t use asymmetric digital signatures – too much overhead SNEP: two-party mTESLA: broadcast

Data Integrity - mTESLA One-way function, F(.) Kn = F(Kn+1) Keys disclosed periodically, not per packet Figure from Perrig et al.

Service Availability Bogus Routing Information Flooding Homing – look at traffic to find important nodes “Black Hole” Attack – compromise neighbors of base-station De-synchronization (transport layer)

Energy – Denial of Sleep Attack Unique to WSNs – can’t use techniques from wired networks Sources of Energy Loss Collision – Frequency Hopping, CDMA, FEC Message Overhearing – RTS/CTS, NAV Idle Listening – schedule sleep Brownfield et al. (2005)

Scheduling Sleep – S-MAC Fixed Sleep Schedule RTS During Listen Period If no RTS  sleep Vulnerable during listen period only Sensor MAC Time sync Broadcast throughout listening period Can keep one awake with RTS (also DoS) Figure from Brownfield et al.

Scheduling Sleep – T-MAC Timeout MAC Sleep Early: wait for timeout period Longest time hidden node must wait before first bit of CTS (TA = 1.5*(tCW_Max + tRTS + tSIFS) Saves energy in absence of attacker, but MORE vulnerable to attacks (if never get timeout, stay awake forever) 1.5 is just to keep stable

Scheduling Sleep – B-MAC No fixed listening start time Periodically wake up and sample channel using low power listening (LPL) Longer preamble (longer than sleep period) Just as vulnerable to attack as T-MAC Figure from Brownfield et al.

Scheduling Sleep – G-MAC Split Frame into Collection and Distribution Period Gateway Sensor (GS) node schedules traffic for cluster Rotate being GS to distribute energy use Gateway can keep misbehaving node in check

Scheduling Sleep – G-MAC Figure from Brownfield et al.

Clusters Cluster head (CH) and member nodes (MN) Popular in routing protocols Nearby nodes have redundancy, compressed at CH (save energy) Can also use for intrusion detection CH monitors MNs, while some subset of MNs monitor CH X MNs can decommission CH (homing) CH = GS, rotate

Methods of Intrusion Detection Anomaly Detection – Actions of monitored node are atypical High probability of false alarm Signature Detection – Actions of monitored node correspond to a type of attack Susceptible to new attacks Typical Attacks: Drop Packets Duplicate Packets Cause Collisions

Clusters for Authentification Everyone watch neighbors? Too much energy BS checks packet at the end? Waste energy transmitting bad packet whole route – need to discover this sooner Check packet everywhere? A lot of computation Check at CH. Send packets first to CH Also send to CH with some probability p so compromised node can’t bypass CH.

Game Theory Approach Agah et al. (2004) Model: 2-player, non-cooperative, nonzero-sum Players: IDS, attacker IDS can choose 1 cluster to defend, Attacker can choose 1 to attack

Game Theory Approach - Notation U = Utility of working WSN Ck = Cost to defend cluster k ALk = Average loss for losing cluster k PI = Attackers profit for intruding CI = Attackers cost to intrude CW = Attacker’s cost to wait

Game Theory Approach - Assumptions PI = SAL CW < PI-CI Ck ~ gk, where gk = # previous attacks to k

Game Theory Approach Payoff Matrix (for cluster k): Attack k Do Nothing Attack k” Defend k U-Ck PI-CI CW U-Ck-ALk” Defend k’ U-Ck’-ALk U-Ck’ U-Ck’-ALk”

What’s wrong with this? Attacker benefit is independent of what IDS does… Intuitively, this should matter We defend one cluster at a time Why not more? How do they coordinate? (Extra transmissions)

Modified Game Theory Approach Uk = Utility of cluster k Ck = Cost to defend cluster k We can defend as many clusters as we want If we defend cluster k, utility of cluster is Uk-Ck If we don’t and it’s not attacked, utility is Uk If we don’t and it is attacked, utility is 0 Since attacker always attacks, his utility is proportional to IDS’s loss minus a constant (CI)

Modified Game Theory Approach No Pure NE: Suppose there were, then attacker always attacks one particular cluster, k. IDS should then only defend k. But then utility of attacker is less than it would be for attacking another cluster. Requirement for mixed NE: E[util. of attacker] indep. of k – equally likely to attack any cluster  (1-pk)Uk = const, where pk is probability of defending cluster k

Modified Game Theory Approach Strategy: each cluster knows its own utility (maybe from G-MAC) Defend with probability pk=1-X/Uk where X is a constant known to the whole WSN. Expected utility of cluster k: pk(Uk-Ck)+(1- pk)(Uk*(m-1)/m) where m = # clusters

Modified Game Theory Approach Total expected utility of WSN: S[pk(Uk-Ck)+(1- pk)(Uk*(m-1)/m)] = S[(1-X/Uk )(Uk-Ck)+ X/Uk(Uk*(m-1)/m)] = S[Uk-Ck-X+XCk/Uk + X*(m-1)/m)] = m(X*(m-1)/m-X)+S[Uk-Ck+XCk/Uk] = -X+S[Uk-Ck+XCk/Uk]

Modified Game Theory Approach Total expected utility of WSN always defending (pk = 1 for all k): S[Uk-Ck ] = -X+S[Uk-Ck+XCk/Uk ] Gain for using pk < 1 -X+S[Uk-Ck+XCk/Uk] - S[Uk-Ck ] = -X+S[XCk/Uk ] = X(S[Ck/Uk ] –1)

Modified Game Theory Approach Utility gain = X(S[Ck/Uk ] –1) What does this mean? Goes to -X As Ck  0 Positive for larger Ck and smaller Uk. Increases with X (Counter-intuitive) Conclusion: We can improve our utility by defending less when per cluster utility is low and Ck is relatively high 1-> h for h attackers

Review Classified Attacks: Confidentiality, Authenticity, Service Availability, Energy Clusters are useful for intrusion detection Game theory approach