ADDENDUM TO DETERMINING PLACEMENT OF INTRUSION DETECTORS FOR A DISTRIBUTED APPLICATION THROUGH BAYESIAN NETWORK MODELING

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1 ADDENDUM O DEERMINING PLACEMEN O INRUSION DEECORS OR A DISRIBUED APPLICAION HROUGH BAYESIAN NEWORK MODELING Gaspar Modelo-Howard, Purdue University ECOMMERCE SYSEM Web Server DMZ Internet irewall Internal Network Application Server Database Server VOIP NEWORK DNS Mail Internal User Internet DNS Mail DMZ Internal Network irewall VoIP Network PSN VoIP Phone (hardware VoIP Phone (software Voic PBX/Proxy

2 DIRECED ACYCLIC GRAPH REPRESENAION O AACK SCENARIO OR E-COMMERCE DISRIBUED SYSEM 3 1 DESCRIPION O NODES NODE 1: Attack step - ping or traceroute to web servers NODE : Attack step - run portscanner on web servers NODE 3: Detector alert IPables 7 NODE : Attack step - exploit ssldump vuln. on web server NODE : Attack step - access web server admin site NODE : Attack step - Brute force admin pwd NODE 7: Detector alert Snort 8 NODE 8: Attack step - Copy hacker tool to web server by using tftp b NODE : Attack step - Install vuln scanner on web server NODE 10: Attack step - Run portscanner on internal network NODE 11: Attack step - Install sniffer to capture pwds NODE 1: Attack step - Exploit rpc.statd service on app controller 1 1 NODE 13: Detector alert Libsafe NODE 1: Attack step - Exploit remote vuln. on MySQL server NODE 1: Attack step - Brute force root pwd on app controller NODE 1: Attack step - Run SQLplus to execute queries on tables NODE 17: Attack step - Connect to MySQL server with admin account NODE 18: Attack step - Read customer data table AACK SEP (UNOBSERVED NODE DEECOR (OBSERVED NODE NODE : Attack step - Copy customer credit card list NODE : Detector alert - Database IDS (Application Security DbProtect

3 b X1 X1 X X X X 0.0 X7 X X X X X X X X X8 X10 X X X8 X X8 X X10 X1 X1 X13 X1 X1 X1 X1 BAYESIAN NEWORK OR E-COMMERCE DISRIBUED SYSEM 0.0 X17 X1 X18 X1 X17 X X1 X1 X1 X17 X X18 X1 X X1 X3 X

4 DIRECED ACYCLIC GRAPH REPRESENAION O AACK SCENARIO OR VOIP NEWORK (v DESCRIPION O NODES NODE 1: Attack step - ping or traceroute to DNS server 3 NODE : Attack step - run portscanner on DNS server NODE 3: Detector alert - IPables NODE : Attack step exploit P buffer overflow in Mail server NODE : Attack step brute force administrator password using VNC in DNS server NODE : Detector alert Snort 7 8 NODE 7: Attack step install sniffer to capture passwords NODE 8: Attack step P to external repository to download sniffer 10 NODE : Attack step P to external repository to download sniffer NODE 10: Attack step sniff traffic to DNS server NODE 11: Attack step - login with sniffed account/password from mail server to voic server NODE 1: Attack step exploit IIS buffer overflow in voic server NODE 13: Attack step exploit SQL buffer overflow in Voic server NODE 1: Attack step - portscan VoIP network NODE 1: Attack step local privilege escalation exploit in voic server 17 NODE 1: Attack step ARP poisoning the communication switch NODE 17: Attack step brute force administrator password using VNC in PBX/Proxy server NODE 18: Detector alert Snort NODE : Attack step exploit IIS buffer overflow in PBX/Proxy server 1 NODE : Attack step buffer overflow to WinK of computer with soft phone NODE 1: Attack step eavesdrop VoIP communication NODE : Detector alert antivirus software

5 DIRECED ACYCLIC GRAPH REPRESENAION O AACK SCENARIO OR GENERIC NEWORK b AACK SEP (UNOBSERVED NODE DEECOR (OBSERVED NODE 1

6 Experiment: Impact on Choice and Placement of Single Detector he objective of this experiment was to determine the impact of selecting the detectors and their corresponding locations, in order to provide a high accuracy when detecting if an attached step has been achieved. We ran experiments on the e commerce network to determine a single detector that would be most effective to detect when attack step was performed. his is similar to the experiment 3 shown in the paper, but with the difference in the number of detectors selected. An optimal detector is chosen according to the algorithm described in Section 3. of the paper. he performance of the optimal detector is presented with a ROC curve and compared against additional pairs selected at random. or our experiment, node is selected as the ultimate goal in the e commerce system. o calculate the performance of each detector, we created 0,000 samples from each Bayesian network, corresponding to that many actual attacks. hen we performed Bayesian inference and calculated the conditional probability of the attack step, given the detector being evaluated. We determined the true positive rate and false positive rate by sweeping across threshold values. 1 PR 0. (d 3,x 1 (d 13,x 1 (d,x 17 (d,x 18 (d,x PR ig. 1. ROC curves for detection of attack step, using a single detector, in the e-commerce network. Results show that the detector determined from the algorithm performs better than the other randomly selected alternatives. igure 1 shows the situation in which a single detector (d attached to the attack node (x performs better than the same detector connected to other attack nodes (x 17 and x 18. It also performs better than other detectors connected to different nodes (d 13 to x 1 and d 3 to x 1. his can be explained by the fact that having a detector directly connected to the attack step provides better predictive performance than having the detector connected to any other attack step, since the performance of the detector, in terms of its P and P values, is high.

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