Intrusion detection system

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An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any malicious activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. [1]

Computer network collection of autonomous computers interconnected by a single technology

A computer network is a digital telecommunications network which allows nodes to share resources. In computer networks, computing devices exchange data with each other using connections between nodes. These data links are established over cable media such as wires or optic cables, or wireless media such as Wi-Fi.

In the field of computer security, security information and event management (SIEM) software products and services combine security information management (SIM) and security event management (SEM). They provide real-time analysis of security alerts generated by applications and network hardware.

Alarm filtering, in the context of IT network management, is the method by which an alarm system reports the origin of a system failure, rather than a list of systems failed.

Contents

IDS types range in scope from single computers to large networks. [2] The most common classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A system that monitors important operating system files is an example of an HIDS, while a system that analyzes incoming network traffic is an example of an NIDS. It is also possible to classify IDS by detection approach: the most well-known variants are signature-based detection (recognizing bad patterns, such as malware); and anomaly-based detection (detecting deviations from a model of "good" traffic, which often relies on machine learning). Some IDS products have the ability to respond to detected intrusions. Systems with response capabilities are typically referred to as an intrusion prevention system. [3] Intrusion detection systems can also serve specific purposes by augmenting them with custom tools, such as using a honeypot to attract and characterize malicious traffic. [4]

A host-based intrusion detection system (HIDS) is an intrusion detection system that is capable of monitoring and analyzing the internals of a computing system as well as the network packets on its network interfaces, similar to the way a network-based intrusion detection system (NIDS) operates. This was the first type of intrusion detection software to have been designed, with the original target system being the mainframe computer where outside interaction was infrequent.

Malware is any software intentionally designed to cause damage to a computer, server, client, or computer network. Malware does the damage after it is implanted or introduced in some way into a target's computer and can take the form of executable code, scripts, active content, and other software. The code is described as computer viruses, worms, Trojan horses, ransomware, spyware, adware, and scareware, among other terms. Malware has a malicious intent, acting against the interest of the computer user—and so does not include software that causes unintentional harm due to some deficiency, which is typically described as a software bug.

An anomaly-based intrusion detection system, is an intrusion detection system for detecting both network and computer intrusions and misuse by monitoring system activity and classifying it as either normal or anomalous. The classification is based on heuristics or rules, rather than patterns or signatures, and attempts to detect any type of misuse that falls out of normal system operation. This is as opposed to signature-based systems, which can only detect attacks for which a signature has previously been created.

Comparison with firewalls

Although they both relate to network security, an IDS differs from a firewall in that a firewall looks outwardly for intrusions in order to stop them from happening. Firewalls limit access between networks to prevent intrusion and do not signal an attack from inside the network. An IDS describes a suspected intrusion once it has taken place and signals an alarm. An IDS also watches for attacks that originate from within a system. This is traditionally achieved by examining network communications, identifying heuristics and patterns (often known as signatures) of common computer attacks, and taking action to alert operators. A system that terminates connections is called an intrusion prevention system, and performs access control like an application layer firewall. [5]

In computing, a firewall is a network security system that monitors and controls incoming and outgoing network traffic based on predetermined security rules. A firewall typically establishes a barrier between a trusted internal network and untrusted external network, such as the Internet.

In computer science, artificial intelligence, and mathematical optimization, a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut.

Intrusion detection

IDS can be classified by where detection takes place (network or host) and the detection method that is employed (signature or anomaly-based). [6]

A network host is a computer or other device connected to a computer network. A network host may offer information resources, services, and applications to users or other nodes on the network. A network host is a network node that is assigned a network address.

For IDS related to Wireless Sensor Networks (WSNs), readers may refer to Butun et al.'s [7] work.

Wireless sensor network

Wireless sensor network (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. WSNs measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on.

Analyzed activity

Network intrusion detection systems

Network intrusion detection systems (NIDS) are placed at a strategic point or points within the network to monitor traffic to and from all devices on the network. It performs an analysis of passing traffic on the entire subnet, and matches the traffic that is passed on the subnets to the library of known attacks. Once an attack is identified, or abnormal behavior is sensed, the alert can be sent to the administrator. An example of an NIDS would be installing it on the subnet where firewalls are located in order to see if someone is trying to break into the firewall. Ideally one would scan all inbound and outbound traffic, however doing so might create a bottleneck that would impair the overall speed of the network. OPNET and NetSim are commonly used tools for simulating network intrusion detection systems. NID Systems are also capable of comparing signatures for similar packets to link and drop harmful detected packets which have a signature matching the records in the NIDS. When we classify the design of the NIDS according to the system interactivity property, there are two types: on-line and off-line NIDS, often referred to as inline and tap mode, respectively. On-line NIDS deals with the network in real time. It analyses the Ethernet packets and applies some rules, to decide if it is an attack or not. Off-line NIDS deals with stored data and passes it through some processes to decide if it is an attack or not.

Subnetwork logically visible subdivision of an IP network

A subnetwork or subnet is a logical subdivision of an IP network. The practice of dividing a network into two or more networks is called subnetting.

OPNET Technologies, Inc. was a software business that provided performance management for computer networks and applications.

In computer networking, an Ethernet frame is a data link layer protocol data unit and uses the underlying Ethernet physical layer transport mechanisms. In other words, a data unit on an Ethernet link transports an Ethernet frame as its payload.

NIDS can be also combined with other technologies to increase detection, and prediction, rates. Artificial Neural Network based IDS are capable of analyzing huge volumes of data, in a smart way, due to the self-organizing structure that allows INS IDS to more efficiently recognize intrusion patterns [8] . Neural networks assist IDS in predicting attacks by learning from mistakes; INN IDS help develop an early warning system, based on two layers. The first layer accepts single values, while the second layer takes the first's layers output as input; the cycle repeats and allows the system to automatically recognize new unforeseen patterns in the network [9] . This system can average 99.9% detection and classification rate, based on research results of 24 network attacks, divided in four categories: DOS, Probe, Remote-to-Local, and user-to-root. [10]

Host intrusion detection systems

Host intrusion detection systems (HIDS) run on individual hosts or devices on the network. A HIDS monitors the inbound and outbound packets from the device only and will alert the user or administrator if suspicious activity is detected. It takes a snapshot of existing system files and matches it to the previous snapshot. If the critical system files were modified or deleted, an alert is sent to the administrator to investigate. An example of HIDS usage can be seen on mission critical machines, which are not expected to change their configurations. [11] [12]

Detection method

Signature-based

Signature-based IDS refers to the detection of attacks by looking for specific patterns, such as byte sequences in network traffic, or known malicious instruction sequences used by malware. [13] This terminology originates from anti-virus software, which refers to these detected patterns as signatures. Although signature-based IDS can easily detect known attacks, it is difficult to detect new attacks, for which no pattern is available. [14]

In Signature-based IDS, the signatures are released by a vendor for its all products. On-time updating of the IDS with the signature is a key aspect.

Anomaly-based

Anomaly-based intrusion detection systems were primarily introduced to detect unknown attacks, in part due to the rapid development of malware. The basic approach is to use machine learning to create a model of trustworthy activity, and then compare new behavior against this model. Although this approach enables the detection of previously unknown attacks, it may suffer from false positives: previously unknown legitimate activity may also be classified as malicious. Most of the existing IDSs suffer from the time-consuming during detection process that degrades the performance of IDSs. Efficient feature selection algorithm makes the classification process used in detection more reliable. [15]

New types of what could be called anomaly-based intrusion detection systems are being viewed by Gartner as User and Entity Behavior Analytics (UEBA) [16] (an evolution of the user behavior analytics category) and network traffic analysis (NTA). [17] In particular, NTA deals with malicious insiders as well as targeted external attacks that have compromised a user machine or account. Gartner has noted that some organizations have opted for NTA over more traditional IDS. [18]

Intrusion prevention

Some systems may attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system. Intrusion detection and prevention systems (IDPS) are primarily focused on identifying possible incidents, logging information about them, and reporting attempts. In addition, organizations use IDPS for other purposes, such as identifying problems with security policies, documenting existing threats and deterring individuals from violating security policies. IDPS have become a necessary addition to the security infrastructure of nearly every organization. [19]

IDPS typically record information related to observed events, notify security administrators of important observed events and produce reports. Many IDPS can also respond to a detected threat by attempting to prevent it from succeeding. They use several response techniques, which involve the IDPS stopping the attack itself, changing the security environment (e.g. reconfiguring a firewall) or changing the attack's content. [19]

Intrusion prevention systems (IPS), also known as intrusion detection and prevention systems (IDPS), are network security appliances that monitor network or system activities for malicious activity. The main functions of intrusion prevention systems are to identify malicious activity, log information about this activity, report it and attempt to block or stop it. [20] .

Intrusion prevention systems are considered extensions of intrusion detection systems because they both monitor network traffic and/or system activities for malicious activity. The main differences are, unlike intrusion detection systems, intrusion prevention systems are placed in-line and are able to actively prevent or block intrusions that are detected. [21] :273 [22] :289 IPS can take such actions as sending an alarm, dropping detected malicious packets, resetting a connection or blocking traffic from the offending IP address. [23] An IPS also can correct cyclic redundancy check (CRC) errors, defragment packet streams, mitigate TCP sequencing issues, and clean up unwanted transport and network layer options. [21] :278 [24] .

Classification

Intrusion prevention systems can be classified into four different types: [20] [25]

  1. Network-based intrusion prevention system (NIPS): monitors the entire network for suspicious traffic by analyzing protocol activity.
  2. Wireless intrusion prevention system (WIPS): monitor a wireless network for suspicious traffic by analyzing wireless networking protocols.
  3. Network behavior analysis (NBA): examines network traffic to identify threats that generate unusual traffic flows, such as distributed denial of service (DDoS) attacks, certain forms of malware and policy violations.
  4. Host-based intrusion prevention system (HIPS): an installed software package which monitors a single host for suspicious activity by analyzing events occurring within that host.

Detection methods

The majority of intrusion prevention systems utilize one of three detection methods: signature-based, statistical anomaly-based, and stateful protocol analysis. [22] :301 [26]

  1. Signature-based detection: Signature-based IDS monitors packets in the Network and compares with pre-configured and pre-determined attack patterns known as signatures.
  2. Statistical anomaly-based detection: An IDS which is anomaly-based will monitor network traffic and compare it against an established baseline. The baseline will identify what is "normal" for that network – what sort of bandwidth is generally used and what protocols are used. It may however, raise a False Positive alarm for legitimate use of bandwidth if the baselines are not intelligently configured. [27]
  3. Stateful protocol analysis detection: This method identifies deviations of protocol states by comparing observed events with "pre-determined profiles of generally accepted definitions of benign activity". [22]

Limitations

Evasion techniques

There are a number of techniques which attackers are using, the following are considered 'simple' measures which can be taken to evade IDS:

Development

The earliest preliminary IDS concept was delineated in 1980 by James Anderson at the National Security Agency and consisted of a set of tools intended to help administrators review audit trails. [30] User access logs, file access logs, and system event logs are examples of audit trails.

Fred Cohen noted in 1987 that it is impossible to detect an intrusion in every case, and that the resources needed to detect intrusions grow with the amount of usage. [31]

Dorothy E. Denning, assisted by Peter G. Neumann, published a model of an IDS in 1986 that formed the basis for many systems today. [32] Her model used statistics for anomaly detection, and resulted in an early IDS at SRI International named the Intrusion Detection Expert System (IDES), which ran on Sun workstations and could consider both user and network level data. [33] IDES had a dual approach with a rule-based Expert System to detect known types of intrusions plus a statistical anomaly detection component based on profiles of users, host systems, and target systems. The author of "IDES: An Intelligent System for Detecting Intruders," Teresa F. Lunt, proposed adding an Artificial neural network as a third component. She said all three components could then report to a resolver. SRI followed IDES in 1993 with the Next-generation Intrusion Detection Expert System (NIDES). [34]

The Multics intrusion detection and alerting system (MIDAS), an expert system using P-BEST and Lisp, was developed in 1988 based on the work of Denning and Neumann. [35] Haystack was also developed in that year using statistics to reduce audit trails. [36]

In 1986 the National Security Agency started an IDS research transfer program under Rebecca Bace. Bace later published the seminal text on the subject, Intrusion Detection, in 2000. [37]

Wisdom & Sense (W&S) was a statistics-based anomaly detector developed in 1989 at the Los Alamos National Laboratory. [38] W&S created rules based on statistical analysis, and then used those rules for anomaly detection.

In 1990, the Time-based Inductive Machine (TIM) did anomaly detection using inductive learning of sequential user patterns in Common Lisp on a VAX 3500 computer. [39] The Network Security Monitor (NSM) performed masking on access matrices for anomaly detection on a Sun-3/50 workstation. [40] The Information Security Officer's Assistant (ISOA) was a 1990 prototype that considered a variety of strategies including statistics, a profile checker, and an expert system. [41] ComputerWatch at AT&T Bell Labs used statistics and rules for audit data reduction and intrusion detection. [42]

Then, in 1991, researchers at the University of California, Davis created a prototype Distributed Intrusion Detection System (DIDS), which was also an expert system. [43] The Network Anomaly Detection and Intrusion Reporter (NADIR), also in 1991, was a prototype IDS developed at the Los Alamos National Laboratory's Integrated Computing Network (ICN), and was heavily influenced by the work of Denning and Lunt. [44] NADIR used a statistics-based anomaly detector and an expert system.

The Lawrence Berkeley National Laboratory announced Bro in 1998, which used its own rule language for packet analysis from libpcap data. [45] Network Flight Recorder (NFR) in 1999 also used libpcap. [46]

APE was developed as a packet sniffer, also using libpcap, in November, 1998, and was renamed Snort one month later. Snort has since become the world's largest used IDS/IPS system with over 300,000 active users. [47] It can monitor both local systems, and remote capture points using the TZSP protocol.

The Audit Data Analysis and Mining (ADAM) IDS in 2001 used tcpdump to build profiles of rules for classifications. [48] In 2003, Yongguang Zhang and Wenke Lee argue for the importance of IDS in networks with mobile nodes. [49]

In 2015, Viegas and his colleagues [50] proposed an anomaly-based intrusion detection engine, aiming System-on-Chip (SoC) for applications in Internet of Things (IoT), for instance. The proposal applies machine learning for anomaly detection, providing energy-efficiency to a Decision Tree, Naive-Bayes, and k-Nearest Neighbors classifiers implementation in an Atom CPU and its hardware-friendly implementation in a FPGA. [51] [52] In the literature, this was the first work that implement each classifier equivalently in software and hardware and measures its energy consumption on both. Additionally, it was the first time that was measured the energy consumption for extracting each features used to make the network packet classification, implemented in software and hardware. [53]

Free and open source systems

See also

Related Research Articles

In computer networking, promiscuous mode is a mode for a wired network interface controller (NIC) or wireless network interface controller (WNIC) that causes the controller to pass all traffic it receives to the central processing unit (CPU) rather than passing only the frames that the controller is specifically programmed to receive. This mode is normally used for packet sniffing that takes place on a router or on a computer connected to a wired network or one being part of a wireless LAN. Interfaces are placed into promiscuous mode by software bridges often used with hardware virtualization.

Deep packet inspection (DPI) is a type of data processing that inspects in detail the data being sent over a computer network, and usually takes action by blocking, re-routing, or logging it accordingly. While deep packet inspection can be used for innocuous reasons such as making sure that data is in the correct format or checking for malicious code, it can also be used for more nefarious motives such as eavesdropping and censorship. There are multiple headers for IP packets; network equipment only needs to use the first of these for normal operation, but use of the second header is normally considered to be shallow packet inspection despite this definition.

Network security consists of the policies and practices adopted to prevent and monitor unauthorized access, misuse, modification, or denial of a computer network and network-accessible resources. Network security involves the authorization of access to data in a network, which is controlled by the network administrator. Users choose or are assigned an ID and password or other authenticating information that allows them access to information and programs within their authority. Network security covers a variety of computer networks, both public and private, that are used in everyday jobs; conducting transactions and communications among businesses, government agencies and individuals. Networks can be private, such as within a company, and others which might be open to public access. Network security is involved in organizations, enterprises, and other types of institutions. It does as its title explains: It secures the network, as well as protecting and overseeing operations being done. The most common and simple way of protecting a network resource is by assigning it a unique name and a corresponding password.

Snort (software) free software intrusion prevention system

Snort is a free open source network intrusion detection system (IDS) and intrusion prevention system (IPS) created in 1998 by Martin Roesch, former founder and CTO of Sourcefire. Snort is now developed by Cisco, which purchased Sourcefire in 2013, at which Roesch is a chief security architect.

Internet security is a branch of computer security specifically related to not only the Internet, often involving browser security and the World Wide Web, but also network security as it applies to other applications or operating systems as a whole. Its objective is to establish rules and measures to use against attacks over the Internet. The Internet represents an insecure channel for exchanging information, which leads to a high risk of intrusion or fraud, such as phishing, online viruses, trojans, worms and more.

In the field of computer security, honeytokens are honeypots that are not computer systems. Their value lies not in their use, but in their abuse. As such, they are a generalization of such ideas as the honeypot and the canary values often used in stack protection schemes. Honeytokens do not necessarily prevent any tampering with the data, but instead give the administrator a further measure of confidence in the data integrity.

Sguil is a collection of free software components for Network Security Monitoring (NSM) and event driven analysis of IDS alerts. The sguil client is written in Tcl/Tk and can be run on any operating system that supports these. Sguil integrates alert data from Snort, session data from SANCP, and full content data from a second instance of Snort running in packet logger mode.

Wireless security

Wireless security is the prevention of unauthorized access or damage to computers or data using wireless networks. The most common types of wireless security are Wired Equivalent Privacy (WEP) and Wi-Fi Protected Access (WPA). WEP is a notoriously weak security standard: the password it uses can often be cracked in a few minutes with a basic laptop computer and widely available software tools. WEP is an old IEEE 802.11 standard from 1997, which was superseded in 2003 by WPA, or Wi-Fi Protected Access. WPA was a quick alternative to improve security over WEP. The current standard is WPA2; some hardware cannot support WPA2 without firmware upgrade or replacement. WPA2 uses an encryption device that encrypts the network with a 256-bit key; the longer key length improves security over WEP. Enterprises often enforce security using a certificate-based system to authenticate the connecting device, following the standard 802.1X.

Zeek is a free and open-source software network analysis framework; it was originally developed in 1994 by Vern Paxson and was named in reference to George Orwell's Big Brother from his novel Nineteen Eighty-Four. It can be used as a network intrusion detection system (NIDS) but with additional live analysis of network events. It is released under the BSD license.

OSSIM

OSSIM is an open source security information and event management system, integrating a selection of tools designed to aid network administrators in computer security, intrusion detection and prevention.

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Intrusion detection system evasion techniques are modifications made to attacks in order to prevent detection by an intrusion detection system (IDS). Almost all published evasion techniques modify network attacks. The 1998 paper Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection popularized IDS evasion, and discussed both evasion techniques and areas where the correct interpretation was ambiguous depending on the targeted computer system. The 'fragroute' and 'fragrouter' programs implement evasion techniques discussed in the paper. Many web vulnerability scanners, such as 'Nikto', 'whisker' and 'Sandcat', also incorporate IDS evasion techniques.

Sourcefire

Sourcefire, Inc was a technology company that developed network security hardware and software. The company's Firepower network security appliances are based on Snort, an open-source intrusion detection system (IDS). Sourcefire was acquired by Cisco for $2.7 billion in July 2013.

Network behavior anomaly detection (NBAD) provides one approach to network security threat detection. It is a complementary technology to systems that detect security threats based on packet signatures.

In network security, evasion is bypassing an information security device in order to deliver an exploit, attack, or other form of malware to a target network or system, without detection. Evasions are typically used to counter network-based intrusion detection and prevention systems but can also be used to by-pass firewalls and defeat malware analysis. A further target of evasions can be to crash a network security device, rendering it in-effective to subsequent targeted attacks.

A next-generation firewall (NGFW) is a part of the third generation of firewall technology, combining a traditional firewall with other network device filtering functionalities, such as an application firewall using in-line deep packet inspection (DPI), an intrusion prevention system (IPS). Other techniques might also be employed, such as TLS/SSL encrypted traffic inspection, website filtering, QoS/bandwidth management, antivirus inspection and third-party identity management integration.

Data center security is the set of policies, precautions and practices adopted to avoid unauthorized access and manipulation of a data center's resources. The data center houses the enterprise applications and data, hence why providing a proper security system is critical. Denial of service (DoS), theft of confidential information, data alteration, and data loss are some of the common security problems afflicting data center environments.

References

  1. Martellini, Maurizio; Malizia, Andrea (2017-10-30). Cyber and Chemical, Biological, Radiological, Nuclear, Explosives Challenges: Threats and Counter Efforts. Springer. ISBN   9783319621081.
  2. Axelsson, S (2000). "Intrusion Detection Systems: A Survey and Taxonomy" (retrieved 21 May 2018)
  3. Newman, Robert (2009-06-23). Computer Security: Protecting Digital Resources. Jones & Bartlett Learning. ISBN   9780763759940.
  4. Mohammed, Mohssen; Rehman, Habib-ur (2015-12-02). Honeypots and Routers: Collecting Internet Attacks. CRC Press. ISBN   9781498702201.
  5. Vacca, John R. (2013-08-26). Network and System Security. Elsevier. ISBN   9780124166950.
  6. Vacca, John R. (2009-05-04). Computer and Information Security Handbook. Morgan Kaufmann. ISBN   9780080921945.
  7. Butun, Ismail; Morgera, Salvatore D.; Sankar, Ravi (2014). "A Survey of Intrusion Detection Systems in Wireless Sensor Networks". IEEE Communications Surveys & Tutorials. 16 (1): 266–282. doi:10.1109/surv.2013.050113.00191. ISSN   1553-877X.
  8. Garzia, Fabio; Lombardi, Mara; Ramalingam, Soodamani (2017). An integrated internet of everything — Genetic algorithms controller — Artificial neural networks framework for security/safety systems management and support. 2017 International Carnahan Conference on Security Technology (ICCST). IEEE. doi:10.1109/ccst.2017.8167863. ISBN   9781538615850.
  9. Vilela, Douglas W. F. L.; Lotufo, Anna Diva P.; Santos, Carlos R. (2018). Fuzzy ARTMAP Neural Network IDS Evaluation applied for real IEEE 802.11w data base. 2018 International Joint Conference on Neural Networks (IJCNN). IEEE. doi:10.1109/ijcnn.2018.8489217. ISBN   9781509060146.
  10. Dias, L. P.; Cerqueira, J. J. F.; Assis, K. D. R.; Almeida, R. C. (2017). Using artificial neural network in intrusion detection systems to computer networks. 2017 9th Computer Science and Electronic Engineering (CEEC). IEEE. doi:10.1109/ceec.2017.8101615. ISBN   9781538630075.
  11. Inc, IDG Network World (2003-09-15). Network World. IDG Network World Inc.
  12. Groom, Frank M.; Groom, Kevin; Jones, Stephan S. (2016-08-19). Network and Data Security for Non-Engineers. CRC Press. ISBN   9781315350219.
  13. Brandon Lokesak (December 4, 2008). "A Comparison Between Signature Based and Anomaly Based Intrusion Detection Systems" (PPT). www.iup.edu.
  14. Douligeris, Christos; Serpanos, Dimitrios N. (2007-02-09). Network Security: Current Status and Future Directions. John Wiley & Sons. ISBN   9780470099735.
  15. Rowayda, A. Sadek; M Sami, Soliman; Hagar, S Elsayed (November 2013). "Effective anomaly intrusion detection system based on neural network with indicator variable and rough set reduction". International Journal of Computer Science Issues (IJCSI). 10 (6).
  16. "Gartner report: Market Guide for User and Entity Behavior Analytics". September 2015.
  17. "Gartner: Hype Cycle for Infrastructure Protection, 2016".
  18. "Gartner: Defining Intrusion Detection and Prevention Systems" . Retrieved 2016-09-20.
  19. 1 2 Scarfone, Karen; Mell, Peter (February 2007). "Guide to Intrusion Detection and Prevention Systems (IDPS)" (PDF). Computer Security Resource Center (800–94). Retrieved 1 January 2010.
  20. 1 2 "NIST – Guide to Intrusion Detection and Prevention Systems (IDPS)" (PDF). February 2007. Retrieved 2010-06-25.
  21. 1 2 Robert C. Newman (19 February 2009). Computer Security: Protecting Digital Resources. Jones & Bartlett Learning. ISBN   978-0-7637-5994-0 . Retrieved 25 June 2010.
  22. 1 2 3 Michael E. Whitman; Herbert J. Mattord (2009). Principles of Information Security. Cengage Learning EMEA. ISBN   978-1-4239-0177-8 . Retrieved 25 June 2010.
  23. Tim Boyles (2010). CCNA Security Study Guide: Exam 640-553. John Wiley and Sons. p. 249. ISBN   978-0-470-52767-2 . Retrieved 29 June 2010.
  24. Harold F. Tipton; Micki Krause (2007). Information Security Management Handbook. CRC Press. p. 1000. ISBN   978-1-4200-1358-0 . Retrieved 29 June 2010.
  25. John R. Vacca (2010). Managing Information Security. Syngress. p. 137. ISBN   978-1-59749-533-2 . Retrieved 29 June 2010.
  26. Engin Kirda; Somesh Jha; Davide Balzarotti (2009). Recent Advances in Intrusion Detection: 12th International Symposium, RAID 2009, Saint-Malo, France, September 23–25, 2009, Proceedings. Springer. p. 162. ISBN   978-3-642-04341-3 . Retrieved 29 June 2010.
  27. 1 2 nitin.; Mattord, verma (2008). Principles of Information Security. Course Technology. pp. 290–301. ISBN   978-1-4239-0177-8.
  28. 1 2 3 Anderson, Ross (2001). Security Engineering: A Guide to Building Dependable Distributed Systems. New York: John Wiley & Sons. pp. 387–388. ISBN   978-0-471-38922-4.
  29. http://www.giac.org/paper/gsec/235/limitations-network-intrusion-detection/100739
  30. Anderson, James P., "Computer Security Threat Monitoring and Surveillance," Washing, PA, James P. Anderson Co., 1980.
  31. David M. Chess; Steve R. White (2000). "An Undetectable Computer Virus". Proceedings of Virus Bulletin Conference. CiteSeerX   10.1.1.25.1508 .
  32. Denning, Dorothy E., "An Intrusion Detection Model," Proceedings of the Seventh IEEE Symposium on Security and Privacy, May 1986, pages 119–131
  33. Lunt, Teresa F., "IDES: An Intelligent System for Detecting Intruders," Proceedings of the Symposium on Computer Security; Threats, and Countermeasures; Rome, Italy, November 22–23, 1990, pages 110–121.
  34. Lunt, Teresa F., "Detecting Intruders in Computer Systems," 1993 Conference on Auditing and Computer Technology, SRI International
  35. Sebring, Michael M., and Whitehurst, R. Alan., "Expert Systems in Intrusion Detection: A Case Study," The 11th National Computer Security Conference, October, 1988
  36. Smaha, Stephen E., "Haystack: An Intrusion Detection System," The Fourth Aerospace Computer Security Applications Conference, Orlando, FL, December, 1988
  37. McGraw, Gary (May 2007). "Silver Bullet Talks with Becky Bace" (PDF). IEEE Security & Privacy Magazine. 5 (3): 6–9. doi:10.1109/MSP.2007.70 . Retrieved 18 April 2017.
  38. Vaccaro, H.S., and Liepins, G.E., "Detection of Anomalous Computer Session Activity," The 1989 IEEE Symposium on Security and Privacy, May, 1989
  39. Teng, Henry S., Chen, Kaihu, and Lu, Stephen C-Y, "Adaptive Real-time Anomaly Detection Using Inductively Generated Sequential Patterns," 1990 IEEE Symposium on Security and Privacy
  40. Heberlein, L. Todd, Dias, Gihan V., Levitt, Karl N., Mukherjee, Biswanath, Wood, Jeff, and Wolber, David, "A Network Security Monitor," 1990 Symposium on Research in Security and Privacy, Oakland, CA, pages 296–304
  41. Winkeler, J.R., "A UNIX Prototype for Intrusion and Anomaly Detection in Secure Networks," The Thirteenth National Computer Security Conference, Washington, DC., pages 115–124, 1990
  42. Dowell, Cheri, and Ramstedt, Paul, "The ComputerWatch Data Reduction Tool," Proceedings of the 13th National Computer Security Conference, Washington, D.C., 1990
  43. Snapp, Steven R, Brentano, James, Dias, Gihan V., Goan, Terrance L., Heberlein, L. Todd, Ho, Che-Lin, Levitt, Karl N., Mukherjee, Biswanath, Smaha, Stephen E., Grance, Tim, Teal, Daniel M. and Mansur, Doug, "DIDS (Distributed Intrusion Detection System) -- Motivation, Architecture, and An Early Prototype," The 14th National Computer Security Conference, October, 1991, pages 167–176.
  44. Jackson, Kathleen, DuBois, David H., and Stallings, Cathy A., "A Phased Approach to Network Intrusion Detection," 14th National Computing Security Conference, 1991
  45. Paxson, Vern, "Bro: A System for Detecting Network Intruders in Real-Time," Proceedings of the 7th USENIX Security Symposium, San Antonio, TX, 1998
  46. Amoroso, Edward, "Intrusion Detection: An Introduction to Internet Surveillance, Correlation, Trace Back, Traps, and Response," Intrusion.Net Books, Sparta, New Jersey, 1999, ISBN   0-9666700-7-8
  47. Kohlenberg, Toby (Ed.), Alder, Raven, Carter, Dr. Everett F. (Skip), Jr., Esler, Joel., Foster, James C., Jonkman Marty, Raffael, and Poor, Mike, "Snort IDS and IPS Toolkit," Syngress, 2007, ISBN   978-1-59749-099-3
  48. Barbara, Daniel, Couto, Julia, Jajodia, Sushil, Popyack, Leonard, and Wu, Ningning, "ADAM: Detecting Intrusions by Data Mining," Proceedings of the IEEE Workshop on Information Assurance and Security, West Point, NY, June 5–6, 2001
  49. Intrusion Detection Techniques for Mobile Wireless Networks, ACM WINET 2003 <http://www.cc.gatech.edu/~wenke/papers/winet03.pdf>
  50. Viegas, E.; Santin, A. O.; Fran?a, A.; Jasinski, R.; Pedroni, V. A.; Oliveira, L. S. (2017-01-01). "Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems". IEEE Transactions on Computers. 66 (1): 163–177. doi:10.1109/TC.2016.2560839. ISSN   0018-9340.
  51. França, A. L.; Jasinski, R.; Cemin, P.; Pedroni, V. A.; Santin, A. O. (2015-05-01). The energy cost of network security: A hardware vs. software comparison. 2015 IEEE International Symposium on Circuits and Systems (ISCAS). pp. 81–84. doi:10.1109/ISCAS.2015.7168575. ISBN   978-1-4799-8391-9.
  52. França, A. L. P. d; Jasinski, R. P.; Pedroni, V. A.; Santin, A. O. (2014-07-01). Moving Network Protection from Software to Hardware: An Energy Efficiency Analysis. 2014 IEEE Computer Society Annual Symposium on VLSI. pp. 456–461. doi:10.1109/ISVLSI.2014.89. ISBN   978-1-4799-3765-3.
  53. "Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems" (PDF). SecPLab.

PD-icon.svg This article incorporates  public domain material from the National Institute of Standards and Technology document "Guide to Intrusion Detection and Prevention Systems, SP800-94" by Karen Scarfone, Peter Mell.Retrieved on 1 January 2010.

Further reading