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Research Article
 

Finite State Machine Detection Model Based on Adaptive TTL Neuron



Zhang Jun and Gao Lei
 
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ABSTRACT

Without detailed knowledge of the network topology between Network Security Audit System (NSAS) and the end system, NSAS may be unable to determine whether a given packet will even be seen by the end system. Finite state machine detection model based on adaptive TTL neuron is proposed to solve it in this paper. By inspecting the variance of Time To Live (TTL) field in ingress sequence, the adaptive TTL neuron compares the TTL field of new fragment with the average to detect whether the TTL field of new fragment is normal or not. Combining the TTL neuron with the detection state node of finite state machine detection model, ingress flow will be benign and NSAS won`t be confused anymore.

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  How to cite this article:

Zhang Jun and Gao Lei , 2006. Finite State Machine Detection Model Based on Adaptive TTL Neuron. Journal of Applied Sciences, 6: 1248-1250.

DOI: 10.3923/jas.2006.1248.1250

URL: https://scialert.net/abstract/?doi=jas.2006.1248.1250

INTRODUCTION

Network security audit system (NSAS) just like IDS or content-based audit system (Cao et al., 2002) protects end systems of Intranet online. By exploiting ambiguities in the traffic stream as seen by NSAS, the ability of a skilled attacker can do insertion and evasion (IE) attack (Ptacek and Newsham, 1998; Patton et al., 2001) to end systems. NSAS can accept a packet that an end system rejects, that's insertion attack; an end system can accept a packet that an NSAS rejects, that's evasion attack. In the absence of external knowledge (end system implementation details, topology details), exploitable ambiguities can arise in two different ways (Handley et al., 2001): (1) Without detailed knowledge of the end system's protocol implementation, NSAS may be unable to determine how the end system will treat a given sequence of packets if different implementations interpret the same stream of packets in different ways. Unfortunately, Internet protocol specifications do not always accurately specify the complete behavior of protocols, especially for rare or exceptional conditions. In addition, different operating systems and applications implement different subsets of the protocols. (2) Without detailed knowledge of the network topology between the NSAS and the end system, NSAS may be unable to determine whether a given packet will even be seen by the end system. For example, a packet seen by NSAS that has a low Time To Live (TTL) field may or may not have sufficient hop count remaining to make it all the way to the end system. Figure 1 for an example.

Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron
Fig. 1: IE attack example

In this paper, finite state machine detection model based on adaptive TTL neuron is proposed to detect the latter case. Counting TTL average of ingress as Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron in each connection, the adaptive TTL neuron compares the TTL field of new fragment with the average Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron to detect whether the TTL field of new fragment is normal or not. Combining the TTL neuron with the detection state node of the TTL finite state machine detection model, ingress flow will be benign after filtering and NSAS will no more be confused by the IE attack based on TTL.

ADAPTIVE TTL NEURON

In the adaptive TTL neuron, the TTL average of ingress fragments as Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron in each connection is counted for the comparison of the TTL field of new fragment to detect whether the ingress is normal or not in TTL field. The result is FALSE for normal flow and TRUE for that it seems to be IE attack.

Each connection of the ingress flow is denoted as tuple (ID, Sip, Sport, Dip, Dport, C,) for the incoming fragments. ID is the identification field in the IP header. Sip is the source IP address. Sport is the source port. Dip is the destination IP address. Dport is the destination port. C is total packet which is counted for the connection. is the TTL average of ingress for the connection. Let D denote the depth of Intranet.

For every tuple, Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron is initialized to be the TTL of first fragment and C is initialized to be zero. The adaptive TTL neuron runs as follows for a new packet:

Step 1: For the new packet, Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron, C = C+1, TTLnew is the TTL field of the new packet.
Step 2: If Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron, it's normal, then =, the result is FALSE; else go to step 3. α is a given parameter, α = MIN(D/2, 2.5).
Step 3: The ingress may be IE attack, the TTL neuron get result TRUE.

Following above steps, the packet in abnormal TTL field will be detected. The adaptive TTL neuron will get result TRUE for detection of IE attack and FALSE for benign packet.

FINITE STATE MACHINE

Symbol meaning: We denote the finite state machine as FSM = [I, O, S, δ, s0]:

I: input symbol set
O: output symbol set
S: state set
δ: S x I → I state transition function
s0: initial state

With the definition of the finite state machine, the detection model is shown in Fig. 2. The meaning of the parameter in the finite state machine detection model is the following:

(1) Input symbol set I = {X, Y, Z}

Every element of I has two inputs: One is the OR function result of the more fragment bit of TTL field and the Fragment Offset field in the IP header, another is the result of TTL neuron.

Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron
Fig. 2: Finite state machine detection model

Different inputs have the following cases (the former is the OR function result, the latter is the result of TTL neuron, x means do not care):

X (0, x): The packet isn't fragment, there's no IE attack.
Y (1, 0): The packet is fragment, but there's no IE attack.
Z (1, 1): The packet is fragment and it seems to be IE attack.

(2) Stat set S = {s0, s1, s2, s3}

s0, s1: Detection state
s2: The ingress is normal
s3: Seem to be IE attack

(3) State transition function δ: S x I → I:

Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron

Detection method: Packet passes the initial state s0 to start detection. If the packet isn't fragment, that will not be IE attack and turn to be state s2, else turn to be state s1 for next detection. In state s1, the fragment will be submitted to state s3 for format formalization if the IE attack is detected. In state s3, TTL field of the fragment is modified to be Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron. Following is the detail of each state:

(1) s0: In this state, the finite state machine classifies the packet to be fragment or not by the Flags field and Fragment Offset field of the IP header. In the three bit of Flags field (IETF, 1981), bit 0 is reserved, bit 1(DF) means don't fragment in value 1 and may fragment in value 0, bit 2(MF) means there're more fragments in value 1 and to be the last fragment in value 0. Value (0, 1) is get from the OR function of MF and Fragment Offset. FALSE means the packet isn't a fragment, then turn to s2; and TRUE means it does, then turn to s1.
(2) s1: If just do the comparison with the TTL field value and some constant, but don't consider the variance of TTL field, the result may be false positive. In this study, the TTL field variance comparison is done according to the TTL average which is computed in the adaptive TTL neuron. If the variance is big, it means the fragment is abnormal. Then turn to state s3 to do the format formalization of TTL field. Else switch to state s2.
(3) s2: In this state, the packet is normal.
(4) s3: In this state, the fragment is abnormal in TTL field, it may be IE attack. Fill the in the TTL field of the fragment, then alert and record.

DETECTING ABILITY

With the inspection of the variance of TTL field, the finite state machine detection model based on adaptive TTL neuron is proposed to detect IE attack. The TTL field of fragment which is detected to be IE attack will be set to be TTL average Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron which is counted in TTL neuron. Then all of the ingress will be benign.

As the detection example for Fig. 1, the TTL average Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron and ingress sequence show in Fig. 3. In this case, D = 18, so that α = 2.5. As show in Fig. 1, the ingress sequence are (r, o, n, i, o, c, t, e), the corresponding TTL field are (23, 21, 15, 17, 20, 12, 21 and 17). Therefore, just the first, second, fifth and seventh packet whose TTL fields are (23, 21, 20 and 21) can arrive end system. In Fig. 3, the black curve denotes Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron, the dash curve is lower limit. All the ingress whose TTL field is in the shadow which is below lower limit are abnormal. With the finite state machine detection model, NSAS can detect that the ingress is "root" and will not be confused.

Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron
Fig. 3: Detection process

CONCLUSIONS

In this study finite state machine detection model based on adaptive TTL neuron is proposed to detect IE attack based on TTL. By inspecting the variance of TTL field in ingress sequence, the adaptive TTL neuron compares the TTL field of new fragment with the average Image for - Finite State Machine Detection Model Based on Adaptive TTL Neuron to detect whether the TTL field of new fragment is normal or not. Combining the TTL neuron with the detection state node of finite state machine detection model, ingress flow will be benign and NSAS won't be confused by the IE attack anymore.

REFERENCES
1:  Cao, J.X., D.Y. Zhang, Z. Wu and W.N. Liu, 2002. Content-based e-mail auditing system implementation. J. Xian Jiaotong Univ., 36: 608-611.

2:  Handley, M., C. Kreibich and V. Paxson, 2001. Network intrusion detection: Evasion, traffic normalization and end-to-end protocol semantics. Proceedings of USENIX Security Symposium, 2001, International Computer Science Institute, pp: 1-17.

3:  Internet Engineering Task Force (IETF), 1981. RFC79. Internet Protocol. IETF, San Francisco, CA., USA.

4:  Patton, S., W. Yurcik and D. Doss, 2001. An achilles heel in signature-based ids: Squealing false positives in snort. Proceedings of 4th International Symposium on Recent Advances in Intrusion Detection (RAID), Oct. 10-12, University of California-Davis, USA., pp: 1-8.

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