1. 1 Chapter 9 Intruders

2. 2 Chapter 9 - Intruders significant issue for networked systems is hostile or unwanted access either via network or local can identify classes of intruders: –masquerader –misfeasor –clandestine user varying levels of competence

3. 3 Intruders clearly a growing publicized problem –from “Wily Hacker” in 1986/87 –to clearly escalating CERT stats may seem benign, but still cost resources may use compromised system to launch other attacks

4. 4 Levels of hackers Sophisticated, with a thorough knowledge of the technology, supplying the cracking programs; “foot solders” using the supplied programs and willing to spend time cracking a system.

5. 5 The Stages of a Network Intrusion 1. Scan the network to: locate which IP addresses are in use, what operating system is in use, what TCP or UDP ports are “open” (being listened to by Servers). 2. Run “Exploit” scripts against open ports 3. Get access to Shell program which is “suid” (has “root” privileges). 4. Download from Hacker Web site special versions of systems files that will let Cracker have free access in the future without his cpu time or disk storage space being noticed by auditing programs. 5. Use IRC (Internet Relay Chat) to invite friends to the feast. 5

6. 6 Intrusion Techniques aim to increase privileges on system basic attack methodology –target acquisition and information gathering –initial access –privilege escalation –covering tracks key goal often is to acquire passwords so then exercise access rights of owner

7. 7 Password Guessing one of the most common attacks attacker knows a login (from email/web page etc) then attempts to guess password for it –try default passwords shipped with systems –try all short passwords –then try by searching dictionaries of common words –intelligent searches try passwords associated with the user (variations on names, birthday, phone, common words/interests) –before exhaustively searching all possible passwords check by login attempt or against stolen password file success depends on password chosen by user surveys show many users choose poorly

8. 8 Password Capture another attack involves password capture –watching over shoulder as password is entered –using a trojan horse program to collect –monitoring an insecure network login (eg. telnet, FTP, web, email) –extracting recorded info after successful login (web history/cache, last number dialed etc) using valid login/password can impersonate user users need to be educated to use suitable precautions/countermeasures

9. 9 Intrusion Detection inevitably will have security failures so need also to detect intrusions so can –block if detected quickly –act as deterrent –collect info to improve security assume intruder will behave differently to a legitimate user –but will have imperfect distinction between

10. 10 Intrusion Detection The intruder can be identified and ejected from the system. An effective intrusion detection can prevent intrusions. Intrusion detection enables the collection of information about intrusion techniques that can be used to strengthen the intrusion prevention facility.

11. 11 Profiles of Behavior of Intruders and Authorized Users

12. 12 Measures used for Intrusion Detection Login frequency by day and time. Frequency of login at different locations. Time since last login. Password failures at login. Execution frequency. Execution denials. Read, write, create, delete frequency. Failure count for read, write, create and delete.

13. 13 Audit records Fundamental tool for intrusion detection - record of ongoing activity used as input to an intrusion detection system, Two kinds : - native audit records used normally by an accounting software and collecting information on user activity - have to be filtered and may not be complete; - detection specific audit records containing only information required by the the intrusion detection system - have to be especially generated.

14. 14 Audit Record Analysis foundation of statistical approaches analyze records to get metrics over time –counter, gauge, interval timer, resource use use various tests on these to determine if current behavior is acceptable –mean & standard deviation, multivariate, markov process, time series, operational key advantage is no prior knowledge used

15. 15 Statistical Anomaly Detection threshold detection –count occurrences of specific event over time –if exceed reasonable value assume intrusion –alone is a crude & ineffective detector profile based –characterize past behavior of users –detect significant deviations from this –profile usually multi-parameter, e.g. counter, gauge, interval timer, resource utilization –Different tets can performed, e.g. mean and standard deviation, multivariate, Markov process

16. 16 Rule-Based Intrusion Detection observe events on system & apply rules to decide if activity is suspicious or not rule-based anomaly detection –analyze historical audit records to identify usage patterns & auto-generate rules for them –then observe current behavior & match against rules to see if conforms –like statistical anomaly detection does not require prior knowledge of security flaws

17. 17 Rule-Based Intrusion Detection rule-based penetration identification –uses expert systems technology –with rules identifying known penetration, weakness patterns, or suspicious behavior –rules usually machine & O/S specific –rules are generated by experts who interview & codify knowledge of security admins –quality depends on how well this is done –compare audit records or states against rules

18. 18 Base-Rate Fallacy practically an intrusion detection system needs to detect a substantial percentage of intrusions with few false alarms –if too few intrusions detected -> false security –if too many false alarms -> ignore / waste time this is very hard to do existing systems seem not to have a good record

19. 19 Distributed Intrusion Detection traditional focus is on single systems but typically have networked systems more effective defense has these working together to detect intrusions issues –dealing with varying audit record formats –integrity & confidentiality of networked data –centralized or decentralized architecture

20. 20 Distributed Intrusion Detection - Architecture

21. 21 Distributed Intrusion Detection – Agent Implementation

22. 22 Template driven logic analysis Notable events – failed file accesses, accessing system files, changing file access control; Signatures – known attack patterns; Noteworthy sessions - anomalous behavior with respect to the number of programs executed, number of files accessed

23. 23 Honeypots decoy systems to lure attackers –away from accessing critical systems –to collect information of their activities –to encourage attacker to stay on system so administrator can respond are filled with fabricated information instrumented to collect detailed information on attackers activities may be single or multiple networked systems

24. 24 Password Management front-line defense against intruders users supply both: –login – determines privileges of that user –password – to identify them passwords often stored encrypted –Unix uses multiple DES (variant with salt) –more recent systems use crypto hash function

25. 25 Managing Passwords need policies and good user education ensure every account has a default password ensure users change the default passwords to something they can remember protect password file from general access set technical policies to enforce good passwords –minimum length (>6) –require a mix of upper & lower case letters, numbers, punctuation –block known dictionary words

26. 26 Managing Passwords may reactively run password guessing tools –note that good dictionaries exist for almost any language/interest group may enforce periodic changing of passwords have system monitor failed login attempts, & lockout account if see too many in a short period do need to educate users and get support balance requirements with user acceptance be aware of social engineering attacks

27. 27 Password protection System maintain a file that associates a password with each authorized user. Password file can be protected with: –One-way encryption –Access Control

28. 28 Storing UNIX Passwords UNIX passwords were kept in in a publicly readable file, etc/passwords. Now they are kept in a “shadow” directory and only visible by “root”.

29. 29 UNIX Password Scheme Loading a new password

30. 30 ”Salt” The salt serves three purposes: –Prevents duplicate passwords. –Effectively increases the length of the password. –Prevents the use of hardware implementations of DES

31. 31 UNIX Password Scheme Verifying a password file

32. 32 Threats to password scheme By using a guest account or other means to access a machine and then running a password guessing program on that machine, obtaining a copy of the password file, and then running the password cracking program on another machine.

33. 33 Password Selecting Strategies User education Computer-generated passwords Reactive password checking Proactive password checking

34. 34 Proactive Password Checking most promising approach to improving password security allow users to select own password but have system verify it is acceptable –simple rule enforcement –compile a large dictionary of “bad” passwords and check every new password against them - require space (was e.g 30 MB) and time use algorithmics (markov model or bloom filter) to detect poor choices

35. 35 Markov Model

36. 36 Transition Matrix 1.Determine the frequency matrix f, where f(i,j,k) is the number of occurrences of the trigram consisting of the ith, jth and kth character. 2.For each bigram ij, calculate f(i,j, ) as the total number of trigrams beginning with ij. 3.Compute the entries of T as follows:

37. 37 Spafford (Bloom Filter) where The following procedure is then applied to the dictionary: 1.A hash table of N bits is defined, with all bits initially set to 0. 2.For each password, its k hash values are calculated, and the responding bits in the hash table are set to 1

38. 38 Spafford (Bloom Filter) Design the hash scheme to minimize false positive. Probability of false positive:

39. 39 Performance of Bloom Filter

40. 40 Advantages of Bloom filter Much less space required by the hash table in comparison with the full dictionary. Simple calculation of the hash functions independent of the size of the dictionary, No need for search of the dictionary.