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Misuse detection actively works against potential insider threats to vulnerable computer data.
| | This article needs additional citations for verification .(December 2011) |
Misuse detection actively works against potential insider threats to vulnerable computer data.
Misuse detection is an approach to detecting computer attacks. In a misuse detection approach, abnormal system behaviour is defined first, and then all other behaviour is defined as normal. It stands against the anomaly detection approach which utilizes the reverse: defining normal system behaviour first and defining all other behaviour as abnormal. With misuse detection, anything not known is normal. An example of misuse detection is the use of attack signatures in an intrusion detection system. Misuse detection has also been used more generally to refer to all kinds of computer misuse. [1]
In theory, misuse detection assumes that abnormal behaviour has a simple-to-define model. Its advantage is the simplicity of adding known attacks to the model. Its disadvantage is its inability to recognize unknown attacks.
Malware is any software intentionally designed to cause disruption to a computer, server, client, or computer network, leak private information, gain unauthorized access to information or systems, deprive access to information, or which unknowingly interferes with the user's computer security and privacy. By contrast, software that causes harm due to some deficiency is typically described as a software bug. Malware poses serious problems to individuals and businesses on the Internet. According to Symantec's 2018 Internet Security Threat Report (ISTR), malware variants number has increased to 669,947,865 in 2017, which is twice as many malware variants as in 2016. Cybercrime, which includes malware attacks as well as other crimes committed by computer, was predicted to cost the world economy $6 trillion USD in 2021, and is increasing at a rate of 15% per year.
In computing, a denial-of-service attack is a cyber-attack in which the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to a network. Denial of service is typically accomplished by flooding the targeted machine or resource with superfluous requests in an attempt to overload systems and prevent some or all legitimate requests from being fulfilled.
An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Any intrusion 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.
A rootkit is a collection of computer software, typically malicious, designed to enable access to a computer or an area of its software that is not otherwise allowed and often masks its existence or the existence of other software. The term rootkit is a compound of "root" and the word "kit". The term "rootkit" has negative connotations through its association with malware.
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its application across business problems, machine learning is also referred to as predictive analytics.
In computer terminology, a honeypot is a computer security mechanism set to detect, deflect, or, in some manner, counteract attempts at unauthorized use of information systems. Generally, a honeypot consists of data that appears to be a legitimate part of the site which contains information or resources of value to attackers. It is actually isolated, monitored, and capable of blocking or analyzing the attackers. This is similar to police sting operations, colloquially known as "baiting" a suspect.
Network security consists of the policies, processes and practices adopted to prevent, detect 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.
Psychopathology is the study of abnormal cognition, behaviour, and experiences which differs according to social norms and rests upon a number of constructs that are deemed to be the social norm at any particular era.
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.
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.
In information security, intruder detection is the process of detecting intruders behind attacks as unique persons. This technique tries to identify the person behind an attack by analyzing their computational behaviour. This concept is sometimes confused with Intrusion Detection techniques which are the art of detecting intruder actions.
In computing, a wireless intrusion prevention system (WIPS) is a network device that monitors the radio spectrum for the presence of unauthorized access points (intrusion detection), and can automatically take countermeasures (intrusion prevention).
In data analysis, anomaly detection is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behaviour. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data.
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.
Misuse case is a business process modeling tool used in the software development industry. The term Misuse Case or mis-use case is derived from and is the inverse of use case. The term was first used in the 1990s by Guttorm Sindre of the Norwegian University of Science and Technology, and Andreas L. Opdahl of the University of Bergen, Norway. It describes the process of executing a malicious act against a system, while use case can be used to describe any action taken by the system.
Network forensics is a sub-branch of digital forensics relating to the monitoring and analysis of computer network traffic for the purposes of information gathering, legal evidence, or intrusion detection. Unlike other areas of digital forensics, network investigations deal with volatile and dynamic information. Network traffic is transmitted and then lost, so network forensics is often a pro-active investigation.
In computer security, a threat is a potential negative action or event facilitated by a vulnerability that results in an unwanted impact to a computer system or application.
Georgios (George) V. Magklaras is a computer scientist working as a Senior Computer Systems Engineer at the Norwegian Meteorological Institute, in Norway. He also co-founded Steelcyber Scientific, an information security based consultancy specialising in digital forensics. He is a High Performance Computing engineer and information security researcher. He developed methods in the field of insider IT misuse detection and prediction and digital forensics. He is the author of the LUARM and POFR tools for the Linux Operating System. He has been a strong advocate of Linux, Open Source tools and the Perl programming language and has given a series of lectures internationally in the fields of Intrusion Detection Systems, Digital forensics, Bioinformatics, Computer Programming and Systems Administration.
The following outline is provided as an overview of and topical guide to computer security:
Automotive security refers to the branch of computer security focused on the cyber risks related to the automotive context. The increasingly high number of ECUs in vehicles and, alongside, the implementation of multiple different means of communication from and towards the vehicle in a remote and wireless manner led to the necessity of a branch of cybersecurity dedicated to the threats associated with vehicles. Not to be confused with automotive safety.
For more information on Misuse Detection, including papers written on the subject, consider the following: