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Detecting Spoofing and Anomalous Traffic in Wireless Networks via Forge-Resistant Relationships Qing Li and Wade Trappe IEEE Transactions on Information Forensics and Security, VOL. 2, No. 4, December 2007 Presented by: Ryan Yandle Outline Spoofing ORBIT Family 1 – Relationships via Auxiliary Fields Family 2 – Relationships via Intrinsic Properties Method A – Sequence Number Method B – One-way chains Method A – Interarrival time Method B – Joint Background Traffic and Interarrival time Analysis Multilevel Classification Conclusion What is Spoofing? The practice of impersonating another entity in order to subvert security. Spoofing allows the attacker to remain anonymous and undetected in the network. More Specifically This paper refers to MAC address spoofing. The attacker tries to gain access to the WLAN by cloning the MAC address of a legitimate user. What are Forge-Resistant Relationships? Rules that govern the relationship between two distinct entities These rules define the relationship such that another entity (attacker) trying to forge the relationship would be caught Paper’s focus is to detect spoofing by creating these unique relationships The ORBIT Wireless Test Bed Composed of a 2d grid of wireless nodes Jointly run by several schools in the NY/NJ area Test Bed Setup A – Legitimate Sender B – Attacker X – Monitor Strategy Overview Consider that the legitimate sender has a unique identity Associated with their identity will be a particular sequence of packets From these packets we may we may observe states More Strategery… A Relationship Consistency Check (RCC) is a binary rule that returns 1 if the states obey the rule R with respect to each other. But… Simply using a relationship R and checking the corresponding RCC at the monitoring device is not going to provide reliable security We need to add forgeability requirements to the relationship Thus, a RRCC (forge-resistant RCC) is needed Definition of RRCC A ε-forge-resistant relationship R is a rule governing the relationship between a set of states from a particular identity, for which there is a small probability of another device being able to forge a set of states such that a monitoring device would evaluate the corresponding RCC as 1. More… We will view the output of an RRCC as the result of deciding between two different hypotheses. H0 – the null hypothesis that corresponds to nonsuspicious activity H1 – the alternate hypothesis that corresponds to anomalous behavior Quantifying Effectiveness We will use several measures to quantify the effectiveness of R. The probability of a false alarm PFA = Pr(H1;H0) Probability that we will decide a set of states is suspicious when it was really legitimate The probability of a missed detection PMD = Pr(H0;H1) Probability of deciding that a set of states are legitimate when they were not Quantifying Effectiveness Cont. The probability of detection Other Symbols: PD = 1 – PMD ε = PMD δ = PFA Therefore, we can define an RRCC by (ε,δ) Two Proposed Families for Relationships 1. 2. Using auxiliary fields in the MAC frame to create a monotonic relationship Using traffic inter-arrival statistics to detect anomalous traffic Family I - Forge-Resistant Relationships via Auxiliary Fields Method A Anomaly Detection via Sequence Number Monotonicity Enforce a rule that requires packet sequence numbers to follow a monotonic relationship, denoted as Rseq 802.11 MAC Frame Structure Generally used to re-assemble fragmented frames or detect duplicate packets. Fragment control – 4bits Sequence number – 12bits = 4096 possibilities ranging from [0,4095] Firmware Rseq It does not matter if the attacker can manipulate its own sequence numbers. Cloning attempt would be exposed due to duplicate sequence numbers Therefore, the forge resistance stems from the fact that the attacker cannot stop the sender from transmitting packets. Single Source Sequence Numbers t: the difference in sequence numbers between two consecutive packets The possible values for t : [1, 4096] A value of 4096 is equivalent to a sequence number difference of 0 (duplicate sequence numbers) The mean distribution for t is E[t] = 1/(1-p)2 where p is the packet loss rate The variance for the distribution of t is σt2 = p/(1-p)2 Theoretical Packet Loss Using the formula’s that we just learned, a theoretical transmission with packet loss of 50%: E[τ] = 2 στ2= 1.41 Even for networks with poor connectivity, the difference in sequence numbers between successive packets will be relatively small Dual Source Sequence Numbers Let y be the sequence number from the real source Let x be the sequence number from the attacker z = x-y gives us a range of [-4095,4095] This gap will be defined as t = z % 4096 Dual Source Cont. If we then map a difference of 0 to 4096, we have a uniform distribution over [1,4096] E[t] = 2048.5 σt = 1182 Single Source Behavior A single node is transmitting packets using a specified MAC address to a receiver No anomalous behavior is present in this scenario Dual Source Behavior Two nodes using the same MAC address to transmit packets One node is spoofing the other’s MAC address Lets build a detector… We will define the RRCC detection scheme as follows: Choose a window of packets coming from a specific MAC address We will choose a window with size L The detector will calculate L-1 sequence number gaps More on the detector The detector will determine that there is an anomaly if MAXl=1 to L-1 {tl} > g g is determined by solving for a desired false alarm rate Example: L = 5 & g = 3 1 MAX{ 2 3 1 76 73 71 73 5 7 2 8 9 10 11 } 73 > g , RETURN(1) Performance of Sequence Number Monotonicity L=2 Sequence Number Gap Statistics for a Single Source from ORBIT When would this not work? This method of detection could only work with a presence of heterogeneous sources; the legitimate device must be transmitting in order to reveal the anomaly. Family I - Forge-Resistant Relationships via Auxiliary Fields Method B One-way chain of Temporary Identifiers The sender attaches a TIF (temporary identifier field) to its identity, forcing the adversary to solve a cryptographic puzzle in order to spoof. Temporary Identifier Fields Similar to what was proposed in TESLA Compute a one-way chain of numbers, and attach them to the frames in reverse order. In order for the attacker to spoof a message, they would need to find the inverse of the function used to compute the one-way chain. This method is loss-tolerant ROC Curve for one-way chain TIF’s Bit Length = 10 Bit Length = 16 Outline Spoofing ORBIT Family 1 – Relationships via Auxiliary Fields Family 2 – Relationships via Intrinsic Properties Method A – Sequence Number Method B – One-way chains Method A – Interarrival time Method B – Joint Background Traffic and Interarrival time Analysis Multilevel Classification Conclusion Family II - Forge-Resistant Relationships via Intrinsic Properties Method A) Traffic Arrival Consistency Checks Use a traffic shaping tool to control the interarrival times observed by the monitoring device. These interarrival statistics are then used to determine anomalous behavior Traffic Arrival Consistency Checks Suppose we have our three devices, A, B, X A is set to transmit at a fixed interval X will take note of this behavior, if B starts transmitting (spoofing to impersonate A) then the detector will notice a change in the distribution of packet arrivals Resulting Histograms Experimental Results: 200ms Experimental Results cont. When would this method become unreliable on a wireless network? With the presence of high background traffic, this method would become less suitable. Background traffic would affect the transmission intervals of the sender, possibly causing false alarms. Family II - Forge-Resistant Relationships via Intrinsic Properties Method B) Joint Traffic Load and Interarrival Time Detector Jointly examine the interarrvial time and the background traffic load Use these two pieces of information to determine anomalous behavior, even under heavy traffic situations Joint Traffic Load and Interarrival Time Detector We can define t to be the observed average interarrival time, and L to be the observed traffic load. We then partition this (L, t) space into two regions Region I – non-suspicious behavior Region II – anomalous activity This idea is later revisited in the experimental validation section. Enhanced Detection using Multilevel Classification Extremely useful to have a severity analysis Plot severity vs. average sequence number gap of a particular window Severity is defined as the sum of the differences between a normal gap and the observed gap for all gaps in a window size L Severity vs. Average Sequence Number Gap Conclusion All methods have their flaws There are already mechanisms in place within 802.11 that can help detect spoofing attacks Thank you for your time! Questions / Comments Sequence Number Gap Statistics for Dual Source from ORBIT