CMSC 414 Computer and Network Security Lecture 28 Jonathan Katz Administrivia Final exam reminder + study guide – DSS students contact me – A-G in.
Download ReportTranscript CMSC 414 Computer and Network Security Lecture 28 Jonathan Katz Administrivia Final exam reminder + study guide – DSS students contact me – A-G in.
CMSC 414 Computer and Network Security Lecture 28 Jonathan Katz Administrivia Final exam reminder + study guide – DSS students contact me – A-G in 3258 AVW; H-Z in classroom Course evaluations – www.CourseEvalUM.umd.edu SSL in wireshark IPsec Overview IPsec can provide security between any two network-layer entities – host-host, host-router, router-router Used widely to establish VPNs IPsec encrypts and/or authenticates network-layer traffic, and encapsulates it within a standard IP packet for routing over the Internet Overview IPsec is a complex, over-engineered protocol – Lots of un-needed features Interoperability is challenging – Defeats the point of having a standard IPsec is less used than it should(?) be Overview IPsec consists of two components – IKE --- Used to establish a key – AH/ESP --- Used to send data once a key is established (whether using IKE or out-of-band) AH (authentication header) – Data integrity, but no confidentiality ESP (encapsulating security payload) – Data integrity + confidentiality – (Other differences as well) Security policy database Nodes maintain a table specifying what is required for each incoming packet – Drop – Forward/accept without IPsec protection – Require IPsec protection • Auth only • Enc only • Both Decisions can be based on any information contained in the packet Security associations (SAs) When a node receives a packet, it needs to know who it is from – May be receiving IPsec traffic from multiple senders at the same time -- possibly even with the same IP address An SA defines a network-layer unidirectional logical connection – For bidirectional communication, need two SAs The IPsec header indicates which security association to use Security associations (SAs) An SA contains crypto keys, the identity/IP address of the other party, a sequence number, and crypto parameters (algorithms, auth/enc/both) IPsec: IKE Overview of IKE IKE provides mutual authentication, establishes a shared key, and creates an SA Assumes a long-term shared key, and uses this to establish a session key (as well as to provide authentication) Supported key types – Public signature keys – Public encryption keys – Symmetric keys IKE phases Phase 1: long-term keys used to derive a session key (and provide authentication) – Roughly analogous to SSL session Phase 2: the session key is used to derive SAs – Roughly analogous to SSL connection In theory, can run phase 1 once, followed by multiple executions of phase 2 • E.g., different flows between same endpoints • Why not use same key for each? – In practice, this rarely happens Phase 1 session keys Two session keys are defined in phase 1 – One each for encryption/authentication These keys are used to protect the final phase 1 messages as well as all phase 2 messages Key types As mentioned earlier… Why are there two PK options? – Signature-based option • Efficiency (can start protocol knowing only your own public key, then get other side’s key from their certificate) • Legal reasons/export control – Encryption-based option • Can be used to provide anonymity in both directions Adds tremendously to the complexity of implementation IKE phase 1 Aggressive mode – 3 messages Main mode – 6 messages – Additional features: • Anonymity • Negotiation of crypto parameters Anonymity Protocols can be designed so that identities of the parties are hidden from eavesdroppers – Even while providing authentication! Can also protect anonymity of one side against active attacks – Whom to protect? • Initiator: since responder’s identity is generally known… • Responder: since otherwise it is easy to get anyone’s identity Aggressive mode Alice sends ga, “Alice”, crypto algorithms – Note that choices are restricted by this message Bob sends gb, choice of crypto algorithm, “proof” that he is really Bob – If Bob does not support any of the suggested algorithms, he simply does not reply – Note that there is no way to authenticate a refusal, since no session key yet established Alice sends “proof” that she is Alice Derive shared key from gab Main mode Negotiate crypto algorithms (2 rounds) Alice and Bob do regular Diffie-Hellman key exchange (2 rounds) Alice sends encryption of “Alice” plus a proof that she is Alice, using long-term secret keys plus (keys derived from) gab Bob does similarly… Crypto parameters… Choice of: – Encryption method (DES, 3DES, …) – Hash function (MD5, SHA-1, …) – Authentication method (e.g., key type, etc.) – Diffie-Hellman group (e.g., (g, p), etc.) A complete set of protocols (a security suite) must be specified Negotiating parameters Many protocols allow parties to negotiate cryptographic algorithms and parameters – Allows users to migrate to stronger crypto; increases inter-operability (somewhat) But, opens up a potential attack if not authenticated somehow… Also makes for more complicated implementations “Proofs of identity” Depend on which type of long-term shared key is being used Similar (in spirit) to the authentication protocols discussed in class IPsec: AH/ESP AH vs. ESP Two header types… Authentication header (AH) – Provides integrity only Encapsulating security payload (ESP) – Provides encryption + integrity Both provide cryptographic protection of everything beyond the IP headers – AH additionally provides integrity protection of some fields of the IP header Transport vs. tunnel mode Transport mode: add IPsec information between IP header and rest of packet – IP header | IPsec | [ packet ] protected Designed for end-to-end secure communication Firewalls and transport mode Transport mode may cause problems if there are firewalls between the communicating hosts – Firewalls can’t inspect higher-layer information, like ports or applications Tunnel mode was suggested to overcome this… – Traffic secured between the firewalls (or between one firewall and the other end host) – End host(s) can be oblivious to what is being done Transport vs. tunnel mode Tunnel mode: keep original IP packet intact but protect it; add new header information outside – New IP header | IPsec | [ old IP header | packet ] protected – Can be used when IPSec is applied at intermediate points along path (e.g., for firewall-to-firewall traffic) • Treat the link as a secure tunnel – New IP header different from old header since, e.g., src/dest have changed Transport vs. tunnel mode Note that tunnel mode subsumes transport mode… – …but transport mode is more efficient Tunnel mode also hides (some information about) the communicating parties More on AH AH provides integrity protection on header – But some fields change en route! Immutable fields included in the integrity check Mutable but predictable fields are also included in the integrity check – The final value of the field is used More on ESP ESP provides both confidentiality and integrity – On data only, not header • Header can’t be encrypted More on AH vs. ESP ESP can already provide encryption and/or authentication So why do we need AH? – AH also protects the IP header – Export restrictions – Firewalls need some high-level data to be unencrypted None of these are compelling… Intrusion detection Prevention vs. detection Firewalls (and other security mechanisms) aim to prevent intrusion IDS aims to detect intrusion in case it occurs Use both in tandem! – Defense in depth, full prevention impossible – Outsider vs. insider attacks – The sooner intrusion is detected, the less the damage – IDS can also be a deterrent, and can be use to detect weaknesses in other security mechanisms IDS tradeoff IDS based on the assumption that attacker behavior is (sufficiently) different from legitimate user behavior In reality, there will be overlap – Some legitimate behavior may appear malicious – Intruder can attempt to disguise their behavior as that of an honest user False positives/negatives False positive – Alarm triggered by acceptable behavior False negative – No alarm triggered by illegal behavior Always a tradeoff between the false positive and false negative rate False alarms? Say we have an IDS that is 99% accurate – I.e., Pr[alarm | attack] = 0.99 and Pr[no alarm | no attack] = 0.99 An alarm goes off -- what is the probability that an attack is taking place? To increase this probability, what should we focus on improving?? False alarms Say the probability of an attack is 1/1000 Use Bayes’ law: Pr[attack | alarm] = Pr[alarm | attack] Pr[attack] / Pr[alarm] = 0.99 * 0.001 / (0.99 * 0.001 + 0.01 * 0.999) ≈ 0.001/(0.001 + 0.01) ≈ 0.1 I.e., when an alarm goes off, 90% of the time it will be a false alarm! How best to lower this number? False alarms Improving Pr[alarm | attack] to 100% gives Pr[attack | alarm] ≈ 0.1 (essentially unchanged) Improving Pr[no alarm | no attack] to 99.9% gives Pr[attack | alarm] ≈ 0.5 Two types of IDS Signature-based ≈ looks for improper behavior – Roughly analogous to blacklisting Anomaly-based ≈ looks for atypical behavior – Roughly analogous to whitelisting Signature (rule-based) detection Define a set of “bad patterns” (e.g., known exploit characteristics, known bad events) Detect these patterns if they occur Example rules Incoming packets with a certain pattern match a known exploit Users should not read files in other users’ directories Users should not be logged in simultaneously from more than one location Users do not make copies of system programs No incoming requests except to port 80 Anomaly detection Monitor behavior and compare to some “baseline” behavior using statistical tests – Look for deviations from “normal behavior” “Normal behavior” can be defined on a global level or a per-user level “Normal behavior” can be specified by a human, or learned automatically over time Probability density function Profile of Intruder behavior Profile of authorized user behavior Overlap in observed or expected behavior Average behaviour of intruder Average behaviour of authorized user Measurable behaviour parameter Metric Model Justification Login frequency by date Mean and standard and time deviation Intruders are more likely to login during off-hours Frequency of login at different locations Mean and standard deviation Intruders may login from a location that a legitimate user does not Time since last login Markov (time series) Break-in to unused account Length of session Mean and standard deviation Masquerader may run a much shorter or longer session Large amount of data copied to some location Mean and standard deviation Detect attempt to copy large amounts of sensitive data Password failures at login Unusual event/ operational Detect attempt to guess passwords Two places IDS can be run Host-based IDS (HIDS) Network-based IDS (NIDS) Host-based IDS Monitors events on a single host Can (potentially) observe the effects of an attack (in addition to possibly detecting the attack itself) Can detect both internal and external intrusions Distributed host-based IDS Combine information collected at many different hosts in the network One or more machines in the network will collect and analyze the network data – Can correlate information across multiple hosts – E.g., same event occurring simultaneously across all machines might be suspicious – Or, an event occurring on only one machine might be suspicious Network-based IDS Monitors traffic at selected points on the network – Real time; packet-by-packet By looking at all network traffic, can potentially get a global view Sensor types Inline sensor – Inserted in network path; all traffic passes through the sensor Passive sensor – Monitors a copy of network traffic Passive sensor more efficient; inline sensor can block attacks immediately Sensor placement Inside firewall? – Can detect attacks that penetrate firewall – Can detect firewall misconfiguration – Can examine outgoing traffic more easily to detect insider attacks – Can configure based on network resources being accessed (e.g., configure differently for traffic directed to web server) Outside firewall? – Can document attacks (types/locations/number) even if prevented by firewall (can then be handled out-of-band) Drawbacks of NIDS Cannot analyze encrypted traffic Cannot observe attack effects Honeypots Decoy systems to lure potential attackers – Divert attackers from critical systems – Collect information about attacker’s activity – Delay attacker long enough to respond Since honeypot is not legitimate, any access to the honeypot is suspicious Can have honeypot computers, or even honeypot networks Honeypot placement Outside firewall – Can detect attempted connections to unused IP addresses, port scanning – No risk of compromised system behind firewall – Does not divert internal attackers Fully internal honeypot – Catches internal attacks – Can detect firewall misconfigurations/vulnerabilities – If compromised, run the risk of a compromised system Course summary What should you take away from this course (after the final)? Security mind-set – Not limited to computers/networks! Security is complex – Draws on many different disciplines – Need to know what you are doing Security is hard, still evolving – We did not cover some of the most important presentday attacks: spam, phishing, DDos, viruses, … Security is challenging…but fun! Thank you!