Network Security An Introduction to Intrusion Detection Prevention Vulnerability

Network Security An Introduction to Intrusion Detection/Prevention, Vulnerability Assessment and related Technologies 1

Contents • Lecture aims and learning outcomes • Assumptions • Motivation - Why Intrusion Detection and Vulnerability Assessment • Attack Development • Vulnerability Development • Hacker Strategy • Detection - Intrusion Detection Systems • Host based IDS • Network Based IDS • Prevention - Vulnerability Assessment • Software • Services (Audits) • Web-Based Services • Counter attacks • Honey Pots • Appliances • Summary 2

Lecture aims and learning outcomes • The lecture aims are: • To describe the problems related to network based attacks • To describe how some these problems may be addressed • At the end of this lecture you will be able to: • Demonstrate an understanding of the main issues relating to threats in the context of network attacks • Understand a number of basic design components for building a network security architecture • Demonstrate an understanding of the importance of a security policy with reference to the security of a computer network • Describe the features and security mechanisms which are generally used to implement security policies for dealing with the security of a computer network 3

Assumption • Perimeter security devices (e. g. firewalls) and computer security mechanisms (e. g. application and OS security) can only offer best effort at preventing attacks. • They may fail to do so: • a firewall may be misconfigured, • a password may be sniffed off the network, • a new attack type may emerge. (cf. Zero-day attacks) • They do not detect when an attack is underway or has taken place. • And they do not react to attacks. 4

Traditional Methods • Example: • • Imagine continuous inspection of a Unix system by hand (similar examples for NT, W 2 K): The following simplified checklist is taken from CERT (http: //www. cert. org/tech_tips/intruder_detection_checklist. html): 1. Examine log files for connections from unusual locations or other unusual activity. For example, look at your 'last' log, process accounting, all logs created by syslog, and other security logs. 2. Look for setuid and setgid files (especially setuid root files) everywhere on your system. Intruders often leave setuid copies of /bin/sh or /bin/time around to allow them root access at a later time. 5

Ad Hoc Intrusion Detection • Imagine the complexity and degree of expertise needed to carry out the tasks in this checklist for every host and every sensitive network link on a network every single day. • The ad hoc approach is not recommended! • Automated systems are needed: • monitor multiple hosts and network links for suspicious behaviour; • report this behaviour, possibly react to it. • Hence: Intrusion Detection Systems (IDS). 6

Motivation 4 Vulnerability Development 700 600 Linux (aggr. ) 500 Solaris Windows 400 Total 300 200 100 0 1999 2000 2001 2002 2003 Source: Security. Focus 8

Motivation Auto Coordinated Cross site scripting Attack Sophistication “stealth” / advanced scanning techniques High packet spoofing Staged denial of service distributed attack tools sniffers sweepers www attacks automated probes/scans GUI back doors network mgmt. diagnostics disabling audits burglaries Attack Sophistication hijacking sessions exploiting known vulnerabilities password cracking self-replicating code Intruder Knowledge password guessing Low 1980 Source: Carnegie Mellon University 1985 1990 1995 2000 9

Motivation Vulnerability & Exploit Lifecycle Vulnerability Scanners adding detection signature First Discovery Selective Awareness Widespre ad Awarenes s Advisory Release 10

Unauthorized Use of Computer Systems within the Last 12 Months 11

Origin of the Attack 12

Which Type of Attacks ? 13

Dollar Amount of Losses by Type 14

Reactions to attacks 15

A Typical Hacker Strategy PING CORP SWEEP NETWORK Internet Primary Target Identification - Identify Hosts ( ) with external visibility denotes internal hosts with high value data but no external view 16

A Typical Hacker Strategy DNS PORT CORP NFS SWEEP NETWORK WEB Primary Target Analysis - Identify services running on visible hosts to prioritize further probing activities 17

A Typical Hacker Strategy FINGER NFS CORP NETWORK Primary Target Selection - Determine vulnerability state of weakest point and concentrate further activities against this system 18

A Typical Hacker Strategy Rlogin Root NFS CORP NETWORK Primary Target Exploitation - Gain privileges & control of primary target - attacker now controls a ‘trusted’ corporate system ! 19

A Typical Hacker Strategy R&D $ NFS CORP NETWORK HR Secondary Target Identification - Probing for high value information or systems which are then compromised and data stolen or trojan horses planted, etc. 20

Animated Demo 21

Detection 22

Intrusion Detection Systems • Popular second layer of network security enforcement • Passive supervision of exiting network, analogues to intruder alarms • Creates more work for personal • There exist 2 different approaches to the implementation of Intrusion Detection Systems (IDS) • Knowledge-based IDS • Network based • Host based • Behaviour-based IDS • Statistical anomaly detection 23

Intrusion Detection Systems • An Intrusion Detection System (IDS) is a network security system designed to identify intrusive or malicious behaviour via monitoring of network activity. The IDS identifies suspicious patterns that may indicate an attempt to attack, break in to, or otherwise compromise a system. An IDS can be network-based or host-based, passive or reactive, and can rely on either misuse detection or anomaly detection. IDS vs Firewalls specify policies about what traffic may or may not enter a particular computer network. An IDS monitors patterns of traffic and signals an alert once it deems that an attack has taken place. 24

Knowledge-based IDS • ALL commercial IDS look for attack signatures: • specific patterns of network traffic or activity in log files that indicate suspicious behaviour. • Called a knowledge-based or misuse detection IDS • Example signatures might include: • a number of recent failed login attempts on a sensitive host; • a certain pattern of bits in an IP packet, indicating a buffer overflow attack; • certain types of TCP SYN packets, indicating a SYN flood Do. S attack. 25

Knowledge-based IDS • Knowledge-based IDS uses information such as: • Security policy; • Known vulnerabilities of particular OS and applications; • Known attacks on systems. • They are only as good as the information in the database of attack signatures: • new vulnerabilities not in the database are constantly being discovered and exploited; • vendors need to keep up to date with latest attacks and issue database updates; customers need to install these; • large number of vulnerabilities and different exploitation methods, so effective database difficult to build; • large database makes IDS slow to use. 26

Behaviour-based IDS • Statistical Anomaly Detection (or behaviour-based detection) is a methodology where statistical techniques are used to detect penetrations and attacks. • Begin by establishing base-line statistical behaviour: what is normal for this system? • Then gather new statistical data and measure the deviation from the base-line. • If a threshold is exceeded, issue an alarm. 27

Behaviour-based IDS • Example: monitor the number of failed login attempts at a sensitive host over a period; • if a burst of failures occurs, an attack may be under way; • or maybe the admin just forgot his password? • This raises the issue of false positives (an attack is flagged when one was not taking place – a false alarm) and false negatives (an attack was missed because it fell within the bounds of normal behaviour). • This issue does also apply to knowledge-based systems. 28

Behaviour-based IDS • IDS does not need to know about security vulnerabilities in a particular system • the base-line defines normality; • don’t need to know the details of the construction of a buffer overflow packet. • Normal behaviour may overlap with forbidden behaviour. • Legitimate users may deviate from the baseline, causing false positives (e. g. user goes on holiday, or works late in the office, or forgets password, or starts to use new application). • If the base-line is adjusted dynamically and automatically, a patient attacker may be able to gradually shift the base-line over time so that his attack does not generate an alarm. 29

Host-based and Network-based IDS • When an IDS looks for attack signatures in network traffic, it is called a network-based IDS (NIDS). • When an IDS looks for attack signatures in log files of hosts, it is called a host-based IDS (HIDS). • Naturally, the most effective Intrusion Detection System will make use of both kinds of information. 30

IDS Architecture • Distributed set of sensors – either located on hosts or on network – to gather data. • Centralised console to manage sensor network, analyze data, report and react. • Ideally: • • • Protected communications between sensors and console; Protected storage for signature database/logs; Secure console configuration; Secured signature updates from vendor; Otherwise, the IDS itself can be attacked and manipulated. 31

Placement of Network-based IDS Internet Sensor Mail server Firewall Perimeter Network Sensor Web server Sensor Console Protected Network 35

Animated Demo 36

Host-based IDS • Typically monitors system, event, and security logs on Windows and syslog in Unix environments. • Checks key system files and executables via checksums at regular intervals for unexpected changes. • Some products can use regular-expressions to refine attack signatures (e. g. passwd program executed AND. rhosts file changed). • Some products listen to port activity and alert when specific ports are accessed – limited NIDS capability. 37

Placement of Host-based IDS Internet Firewall Sensor Mail server Perimeter Network Web server Sensor Console Sensor Human Resources Network 40

IDS as a Response Tool • Given the (near) real-time nature of IDS alerts, an IDS can be used as a response tool as well as for detection. • NIDS and HIDS have different response capabilities – because they detect different attacks, or the same attacks but in different ways. 41

HIDS and NIDS • There attack types that a HIDS can detect but a NIDS cannot: • SYN flood, Land, Smurf and Teardrop attacks, Back. Orifice, … • And vice-versa: • Trojan login script, walk up to unattended keyboard attack, encrypted traffic, … • For more reliable detection, combine both types of IDS. 42

IDS Response Options 43

IDS Response Options • Dangers of automated response: • Attacker tricks IDS to respond, but response aimed at innocent target (say, by spoofing source IP address); • Users locked out of their accounts because of false positives; • Repeated e-mail notification becomes a denial of service attack on sysadmin’s e-mail account; • Repeated restoration of index. html from CD reduces website availability. 44

What is Snort? • Snort is a fast, flexible, small-footprint, open-source NIDS developed by the security community and a “benevolent dictator” • Lead coder: Marty Roesch, now founder of Sourcefire (http: //www. sourcefire. com) • Initially developed in late 1998 as a sniffer with consistent output, unlike protocol-dependent output of TCPDump • Licensed under GPL, but version 2. 0 may change to a different license 45

Snort Rules • Snort rules are extremely flexible and are easy to modify, unlike many commercial NIDS • Sample rule to detect Sub. Seven trojan: alert tcp $EXTERNAL_NET 27374 -> $HOME_NET any (msg: "BACKDOOR subseven 22"; flags: A+; content: "|0 d 0 a 5 b 52504 c 5 d 3030320 d 0 a|"; reference: arachnids, 485; reference: url, www. hackfix. org/subseven/; sid: 103; classtype: misc-activity; rev: 4; ) • Elements before parentheses comprise ‘rule header’ • Elements in parentheses are ‘rule options’ 46

Third-Party Enhancements • Analysis Console for Intrusion Databases (ACID) • http: //acidlab. sourceforge. net/ • PHP-based analysis engine to search and process a database of security events generated by various IDSes, firewalls, and network monitoring tools • Query-builder and search interface, packet viewer (decoder), alert management, chart and statistics generation • Description and screenshots taken from ACID web 47

48

49

Third-Party Enhancements • Demarc • www. demarc. com • NIDS management console, integrating Snort with the convenience and power of a centralized interface for all network sensors • Monitor all servers / hosts to make sure network services such as a mail or web servers remain accessible at all times • Monitor system logs for anomalous log entries that may indicate intruders or system malfunctions • Description and screenshots taken from demarc web 50

51

52

IDS – The Future • Integrated approach to IDS: • Network and host-based in one system (some products already do this in a limited way); • The strengths of both NIDS and HIDS (but maybe all of the weaknesses!) • Better visualisation, management and reporting tools • Event correlation: • Correlate a number of sub-events which individually do not indicate an attack but which when viewed in combination do; • Requires much more sophisticated software and data processing. • Potentially much better attack detection. • Commercial Statistical Anomaly Detection 53

Prevention Vulnerability Assessment Intrusion Prevention Systems 54

Vulnerability Assessment 55

Vulnerability Assessment • An examination of the ability of a system or application, including current security procedures and controls, to withstand assault. • A vulnerability assessment may be used to: • identify weaknesses that could be exploited; • predict the effectiveness of additional security measures in protecting information resources from attack. 56

Vulnerability Assessment • Vulnerability Assessment Methods • Software solutions (ISS Scanner, Stat, Nessus etc. ) • Audit Services (manual Penetration tests etc) • Web based commercial (Qualys, Security Point etc. ) • Use a database of vulnerability signatures • Usually perform a port scan to detect which services available • Try to identify if service is vulnerable by: • Looking for banner information • Sending a harmless request and analysing the response • Actually performing the attack! • Offer various reporting and management facilities 57

Animated Demo 58

Lesson learnt from VA • Keep up-to-date with security (and other) patches • Form Microsoft OS www. windowsupdate. com • Enterprise version available – Windows Update Services (WUS) • Microsoft Baseline Security Advisor • Includes hfnetcheck. exe (from Shavlik) • Similar for SUN, HP, IBM, CISCO etc. OSs 59

Intrusion Prevention Systems Rate based Content based 60

Intrusion Prevention System - IPS • Relatively new (marketing) term • Essentially a combination of access control (firewall/router) and intrusion detection systems • Often shared technologies between stateful inspection and signature recognition (“looking deep into the packet”) • Inline network IDS allows for instant access control policy modification • 2004 Gartner study claims by 2005 only integrated firewalls with IDS (i. e. IPS) will survive • Most success to-date with “flood” (Do. S) attacks 61

Definition of an IPS • Can be defined as an in-line product that focuses on identifying and blocking malicious network activity in real time. • Two general categories: • rate-based products • content-based (also referred to as signature- and anomaly-based) • Often look like firewalls and often have some basic firewall functionality. • But firewalls block all traffic except that which they have a reason to pass; • IPSs pass all traffic except that which they have a reason to block. 62

Rate-based IPS • Block traffic based on load: • too many packets, • too many connects, • too many errors. • In the presence of too much of anything, the rate-based IPS kicks in and blocks, throttles or otherwise mediates the traffic. • Most useful rate-based IPS include a combination of powerful configuration options with range of response technologies • For example, limit queries to your DNS server to 1, 000 per second • Other simple rules covering bandwidth and connection limiting 63

Disadvantages of Rate-based IPS • Biggest problem with deploying rate-based IPS products is deciding what constitutes an overload. • For any rate-based IPS to work properly, need to know not only what "normal" traffic levels are (on a host-by-host and port-by-port basis) but also other network details such as how many connections your Web servers can handle. • Most products do not provide any help but require a “trained” system engineer • Because rate-based IPSs require frequent tuning and adjustment, they will be most useful in very high-volume Web, application and mail server environments. 64

Content-based products • Block traffic based on attack signatures and protocol anomalies • Worms, e. g. Blaster and My. Doom, that match a signature can be blocked. • Packets that do not comply to TCP/IP RFCs can be dropped. • Suspicious behaviour such as port scanning triggers the IPS to block future traffic from a single host • The best content-based IPSs offer a range of techniques for identifying malicious content and many options for how to handle the attacks, • simply dropping bad packets to • dropping future packets from the same attacker, and • reporting and alerting strategies. • IDS-like technology for identifying threats and blocking them, contentbased IPSs can be used deep inside the network to complement firewalls and provide security policy enforcement. 65

Counter attacks The Problem of origin Honeypots/nets 66

Problem of origin • Denial of Service attacks (Do. S) In contrast to unauthorised access attacks a Do. S attack does not need to contain method for communicating back to the attacker • Distributed Denial of Service (DDo. S) attacks • Trin 00/Stacheldraht (Feb 2000) • Attacks on ebay, amazon. com and etrade. com • MS. Blaster (August 2003) • Problem of lack of metrics to measure the impact of Denial of Service attacks – more research required 67

What is a DDo. S Attack ? • In a Denial of Service (Do. S) attack, • The attacker overwhelms a targeted system with a flood of packets to deny availability of services to legitimate users • In a Distributed Denial of Service (DDo. S) attack, • The attacker uses dozens or even hundreds of ‘zombie’ machines to multiply the force of the attack 68

Motives Behind DDo. S Attacks • Until recently attacks appear to be motivated by: • Desire for attention • Notoriety • Fun • Long term, DDo. S type attacks could become motivated by: • • Economic warfare between competition Disgruntled employees/customers Monetary gains (i. e. stock market manipulation/online betting) Political sabotage and vandalism (party websites during election campaigns) 69

DDo. S Components • All DDo. S attacks consist of three parts: • Client Program • Master Server • Agent (Zombie) Program 70

DDo. S Attack Illustrated Hacker 1 Hacker scans Internet for unsecured systems that can be compromised Unsecured Computers Internet Scanning Program 71

DDo. S Attack Illustrated Hacker Zombies 2 Hacker secretly installs zombie agent programs, turning unsecured computers into zombies Internet 72

DDo. S Attack Illustrated Hacker Master Server 3 Hacker selects a Master Server to send commands to the zombies Zombies Internet 73

DDo. S Attack Illustrated Hacker Master Server 4 Using Client program, Hacker sends commands to Master Server to launch zombie attack against a targeted system Zombies Internet Targeted System 74

DDo. S Attack Illustrated Hacker Master Server 5 Master Server sends signal to zombies to launch attack on targeted system Zombies Internet Targeted System 75

DDo. S Attack Illustrated Hacker Master Server Zombies 6 Targeted system is overwhelmed by bogus requests that shut it down for legitimate users Request Denied User Internet Targeted System 76

Minimizing Risk • Prevent yourself from being victimized • Ensure your computers are not zombies • Perform periodic assessments via automated scanning services • Implement an early warning system • Automated Intrusion Detection & Response tools • Collect forensic data to prosecute hackers later 77

Honeypots • Technology used to track, learn and gather evidence of hacker activities • Definition • “… a resource whose value is being attacked or compromised” Laurence Spitzner, “The value of honeypots”, Security. Focus, October 2001 • Strategically placed systems designed to mimic production systems, but not reveal “real” data • Modes of operation • • Baiting Waiting Collating Disseminating 78

Honeypot types of implementation • Level of Involvement • Low Involvement: Port Listeners • Mid Involvement: Fake Daemons • High Involvement: Real Services • Risk increases with level of involvement 79

Honeynet • Network of honeypots • Supplemented by firewalls and intrusion detection systems - Honeywall • Advantages: • “More realistic” environment • Improved possibilities to collect data 80

Honeynet 81

Sebek • Sebek is a data capture tool designed to capture all of the attackers activities on a honeypot, without the attacker knowing it. • 2 components. • Client that runs on the honeypots, its purpose is to capture all of the attackers activities (keystrokes, file uploads, passwords) then covertly send the data to the server. • Server which collects the data from the honeypots. The server normally runs on the Honeywall gateway. • Since the Sebek client runs as a kernel module on the honeypots, it can capture all activity, including encrypted, such as SSH, IPSec 82

Honeynet using a Honeywall 83

Summary • Threats are both internal and external. • Prevention, detection and reaction are needed in combination. • Intrusion detection systems are a very useful second line of defence (in addition to firewalls and other safeguards). • IDS deployment, customisation and management is generally not straightforward. • Vulnerability Assessment and Patch Management are King. • Newer technologies such as IPS and Honeynets can remove the burden from over worked system and network administrators. 84

IDS Further Reading • Stallings Chapter 9, pp. 292 -303 (possibly too much emphasis on statistical approach; research-focussed rather than commercially focussed). • An article: “The future of IDS” by Matthew Tanase at Security. Focus. com: • http: //online. securityfocus. com/infocus/1518 • An evaluation of IDS products by Kathleen A. Jackson: • http: //www. sekure. net/ids/00416750. pdf 85

Questions Thank You ! Merry Christmas & Happy New Year 86