Intrusion Detection. Chapter 8. Computer Security: Principles and Practice презентация

Computer Security: Principles and Practice EECS710: Information Security Professor Hossein Saiedian Fall 2014

Classes of intruders: criminals Individuals or members of an organized crime group with a goal of financial reward Identity theft Theft of financial credentials Corporate espionage Data theft Data ransoming Typically young, often Eastern European, Russian, or southeast Asian hackers, who do business on the Web Meet in underground forums to trade tips and data and coordinate attacks

Classes of intruders: activists Are either individuals, usually working as insiders, or members of a larger group of outsider attackers, who are motivated by social or political causes Also known as hacktivists Skill level is often quite low Aim of their attacks is often to promote and publicize their cause typically through: Website defacement Denial of service attacks Theft and distribution of data that results in negative publicity or compromise of their targets

Intruders: state-sponsored Groups of hackers sponsored by governments to conduct espionage or sabotage activities Also known as Advanced Persistent Threats (APTs) due to the covert nature and persistence over extended periods involved with any attacks in this class Widespread nature and scope of these activities by a wide range of countries from China to the USA, UK, and their intelligence allies

Intruders: others Hackers with motivations other than those previously listed Include classic hackers or crackers who are motivated by technical challenge or by peer-group esteem and reputation Many of those responsible for discovering new categories of buffer overflow vulnerabilities could be regarded as members of this class Given the wide availability of attack toolkits, there is a pool of “hobby hackers” using them to explore system and network security (Lamer)

Skill level: apprentice Hackers with minimal technical skill who primarily use existing attack toolkits They likely comprise the largest number of attackers, including many criminal and activist attackers Given their use of existing known tools, these attackers are the easiest to defend against Also known as “script-kiddies”, due to their use of existing scripts (tools), or “Lamers”

Skill level: journeyman Hackers with sufficient technical skills to modify and extend attack toolkits to use newly discovered, or purchased, vulnerabilities They may be able to locate new vulnerabilities to exploit that are similar to some already known Hackers with such skills are likely found in all intruder classes Adapt tools for use by others

Skill level: master Hackers with high-level technical skills capable of discovering brand new categories of vulnerabilities Write new powerful attack toolkits Some of the better known classical hackers are of this level Some are employed by state-sponsored organizations Defending against these attacks is of the highest difficulty

Intruders: another classification Masquerader: unauthorized individuals who penetrates a system Misfeasor: legit user who accesses unauthorized data Clandestine: seizes supervisory control

User and software trespass User trespass: unauthorized logon, privilege abuse Software trespass: virus, worm, or Trojan horse

Example of intrusion Remote root compromise Web server defacement Guessing/cracking passwords Copying databases containing credit card numbers Viewing sensitive data without authorization Running a packet sniffer Distributing pirated software Using an unsecured modem to access internal network Impersonating an executive to get information Using an unattended workstation

Intruder behavior Target acquisition and information gathering Initial access Privilege escalation Information gathering or system exploit Maintaining access Covering tracks

Hacker behavior example Select target using IP lookup tools Map network for accessible services study physical connectivity (via NMAP – looks for open ports) Identify potentially vulnerable services Brute force (guess) passwords Install remote administration tool Wait for admin to log on and capture password Use password to access remainder of network

Criminal intruder behavior Act quickly and precisely to make their activities harder to detect Exploit perimeter via vulnerable ports Use Trojan horses (hidden software) to leave back doors for re-entry Use sniffers to capture passwords Do not stick around until noticed Make few or no mistakes

Insider intruder behavior Create network accounts for themselves and their friends Access accounts and applications they wouldn't normally use for their daily jobs E-mail former and prospective employers Conduct furtive (covert) instant-messaging chats Visit web sites that cater to disgruntled employees, such as f*dcompany.com Perform large downloads and file copying Access the network during off hours

Insider attacks Among most difficult to detect and prevent Employees have access & systems knowledge May be motivated by revenge/entitlement When employment terminated Taking customer data when move to competitor IDS/IPS may help but also need Least privilege, monitor logs, strong authentication, termination process to block access & take mirror image of employee’s HD (for future purposes)

Security intrusion & detection (RFC 2828) Security intrusion: a security event, or combination of multiple security events, that constitutes a security incident in which an intruder gains, or attempts to gain, access to a system (or system resource) without having authorization to do so. Intrusion detection: a security service that monitors and analyzes system events for the purpose of finding, and providing real-time or near real-time warning of attempts to access system resources in an unauthorized manner.

Intrusion techniques Objective to gain access or increase privileges Initial attacks often exploit system or software vulnerabilities to execute code to get backdoor e.g. buffer overflow Or to gain protected information Password guessing or acquisition (or via social engineering)

Intrusion detection systems Host-based IDS: monitor single host activity Network-based IDS: monitor network traffic Distributed or hybrid: Combines information from a number of sensors, often both host and network based, in a central analyzer that is able to better identify and respond to intrusion activity

IDS principles Assumption: intruder behavior differs from legitimate users Expect overlap as shown for legit users: Observe major deviations from past history Problems of: false positives false negatives must compromise

IDS requirements

IDS requirements Run continually with minimal human supervision Be fault tolerant: recover from crashes Resist subversion: monitor itself from changes by the intruder Impose a minimal overhead on system Configured according to system security policies Adapt to changes in systems and users Scale to monitor large numbers of systems Provide graceful degradation of service: if one component fails, others should continue to work Allow dynamic reconfiguration

Detection techniques Anomaly (behavior) detection Signature/heuristic detection

IDS: anomaly (behavior) detection Involves the collection of data relating to the behavior of legitimate users over a period of time Current observed behavior is analyzed to determine whether this behavior is that of a legitimate user or that of an intruder

Anomaly detection Threshold detection checks excessive event occurrences over time alone a crude and ineffective intruder detector must determine both thresholds and time intervals lots of false positive/false negative may be possible Profile based characterize past behavior of users/groups then detect significant deviations based on analysis of audit records: gather metrics

Example of metrics Counters: e.g., number of logins during an hour, number of times a cmd executed Gauge: e.g., the number of outgoing messages [pkts] Interval time: the length of time between two events, e.g., two successive logins Resource utilization: quantity of resources used (e.g., number of pages printed) Mean and standard deviations

Signature/heuristic detection Uses a set of known malicious data patterns or attack rules that are compared with current behavior Also known as misuse detection Can only identify known attacks for which it has patterns or rules (signature) Very similar to anti-virus (requires frequent updates) Rule-based penetration identification rules identify known penetrations/weaknesses often by analyzing attack scripts from Internet (CERTs)

Example of rules in a signature detection IDS Users should not be logged in more than one session Users do not make copies of system, password files Users should not read in other users’ directories Users must not write other users’ files Users who log after hours often access the same files they used earlier Users do not generally open disk devices but rely on high-level OS utils

Host-based IDS: signature vs anomaly detection Connection attempt from a reserved IP address Attempt to copy the password file Email containing a particular virus File access attack on an FTP server by issuing file and directory commands to it without first logging in

Host-based IDS Specialized software to monitor system activity to detect suspicious behavior primary purpose is to detect intrusions, log suspicious events, and send alerts can detect both external and internal intrusions Two approaches, often used in combination: Anomaly detection: consider normal/expected behavior over a period of time; apply statistical tests to detect intruder threshold detection: for various events (#/volume of copying) profile based (time/duration of login) Signature detection: defines proper (or bad) behavior (rules)

Audit records A fundamental tool for intrusion detection Two variants: Native audit records: provided by O/S always available but may not be optimum Detection-specific audit records: IDS specific additional overhead but specific to IDS task often log individual elementary actions e.g. may contain fields for: subject, action, object, exception-condition, resource-usage, time-stamp possible overhead (two such utilities)

Common data sources Common data sources include: System call traces Audit (log file) records File integrity checksums Registry access

Distributed host-based IDS

Distributed host-based IDS: agent architecture

Distributed host-based IDS: agent architecture

Network-Based IDS Network-based IDS (NIDS) Monitor traffic at selected points on a network (e.g., rlogins to disabled accounts) In (near) real time to detect intrusion patterns May examine network, transport and/or application level protocol activity directed toward systems Comprises a number of sensors Inline (possibly as part of other net device) – traffic passes thru it Passive (monitors copy of traffic)

Passive sensors

NIDS Sensor Deployment

NIDS intrusion detection techniques Signature detection at application (FTP), transport (port scans), network layers (ICMP); unexpected application services (host running unexpected app), policy violations (website use) Anomaly detection of denial of service attacks, scanning, worms (significant traffic increase) When potential violation detected, sensor sends an alert and logs information Used by analysis module to refine intrusion detection parameters and algorithms by security admin to improve protection

Distributed hybrid intrusion detection (host-based, NIDS, distributed host-based)

Logging of alerts (for all types) Typical information logged by a NIDS sensor includes: Timestamp Connection or session ID Event or alert type Rating Network, transport, and application layer protocols Source and destination IP addresses Source and destination TCP or UDP ports, or ICMP types and codes Number of bytes transmitted over the connection Decoded payload data, such as application requests and responses State-related information

Intrusion detection exchange format

Honeypots Decoy systems Filled with fabricated info and instrumented with monitors/event loggers Lure a potential attacker away from critical systems Collect information about the attacker’s activity Encourage the attacker to stay on the system long enough for administrators to respond Divert and hold attacker to collect activity info without exposing production systems Initially were single systems More recently are/emulate entire networks

Honeypot classification Low interaction honeypot Consists of a software package that emulates particular IT services or systems well enough to provide a realistic initial interaction, but does not execute a full version of those services or systems Provides a less realistic target Often sufficient for use as a component of a distributed IDS to warn of imminent attack High interaction honeypot A real system, with a full operating system, services and applications, which are instrumented and deployed where they can be accessed by attackers

Honeypot deployment

Snort IDS Lightweight IDS Open source (rule-based) Real-time packet capture and rule analysis Passive or inline Components: decoder, detector, logger, alerter

SNORT Rules Use a simple, flexible rule definition language Fixed header and zero or more options Header includes: action, protocol, source IP, source port, direction, dest IP, dest port Many options Example rule to detect TCP SYN-FIN attack: alert tcp $EXTERNAL_NET any -> $HOME_NET any \ (msg: "SCAN SYN FIN"; flags: SF, 12; \ reference: arachnids, 198; classtype: attempted-recon;) detects an attack at the TCP level; $strings are variables with defined values; any source or dest port is considered; checks to see if SYN and FIN bits are set

Summary Introduced intruders & intrusion detection Hackers, criminals, insiders Intrusion detection approaches Host-based (single and distributed) Network Distributed adaptive Honeypots Snort example