2,187 research outputs found
A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
An intrusion detection system (IDS) perform postcompromise detection of security breaches whenever preventive measures such as firewalls do not avert an attack. However, these systems raise a vast number of alerts that must be analyzed and triaged by security analysts. This process is largely manual, tedious, and time-consuming. Alert correlation is a technique that reduces the number of intrusion alerts by aggregating alerts that are similar in some way. However, the correlation is performed outside the IDS through third-party systems and tools, after the IDS has already generated a high volume of alerts. These third-party systems add to the complexity of security operations. In this paper, we build on the highly researched area of alert and event correlation by developing a novel hierarchical event correlation model that promises to reduce the number of alerts issued by an intrusion detection system. This is achieved by correlating the events before the IDS classifies them. The proposed model takes the best features from similarity and graph-based correlation techniques to deliver an ensemble capability not possible by either approach separately. Further, we propose a correlation process for events rather than alerts as is the case in the current art. We further develop our own correlation and clustering algorithm which is tailor-made to the correlation and clustering of network event data. The model is implemented as a proof of concept with experiments run on standard intrusion detection sets. The correlation achieves an 87% data reduction through aggregation, producing nearly 21,000 clusters in about 30 s.</jats:p
Parallel Network Alert Management System For IDS False Positive Reduction
Every secure system has the possibility to fail. Therefore, extra effort should be taken to protect these systems. Intrusion detection systems (IDSs) had been proposed with the aim of providing extra protection to security systems. IDS is a powerful computer security system used to secure the computer environments. These systems trigger thousands of alerts per day, which prompt security analysts to verify each alert for relevance and severity based on an aggregation and correlation criterion. Several aggregation and correlation methods have been proposed to collect these alerts
TRIDEnT: Building Decentralized Incentives for Collaborative Security
Sophisticated mass attacks, especially when exploiting zero-day
vulnerabilities, have the potential to cause destructive damage to
organizations and critical infrastructure. To timely detect and contain such
attacks, collaboration among the defenders is critical. By correlating
real-time detection information (alerts) from multiple sources (collaborative
intrusion detection), defenders can detect attacks and take the appropriate
defensive measures in time. However, although the technical tools to facilitate
collaboration exist, real-world adoption of such collaborative security
mechanisms is still underwhelming. This is largely due to a lack of trust and
participation incentives for companies and organizations. This paper proposes
TRIDEnT, a novel collaborative platform that aims to enable and incentivize
parties to exchange network alert data, thus increasing their overall detection
capabilities. TRIDEnT allows parties that may be in a competitive relationship,
to selectively advertise, sell and acquire security alerts in the form of
(near) real-time peer-to-peer streams. To validate the basic principles behind
TRIDEnT, we present an intuitive game-theoretic model of alert sharing, that is
of independent interest, and show that collaboration is bound to take place
infinitely often. Furthermore, to demonstrate the feasibility of our approach,
we instantiate our design in a decentralized manner using Ethereum smart
contracts and provide a fully functional prototype.Comment: 28 page
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
A framework for implementing a Distributed Intrusion Detection System (DIDS) with interoperabilty and information analysis
Computer Intrusion Detection Systems (IDS) are primarily designed to protect availability, condentiality and integrity of critical information infrastructures. A Distributed IDS (DIDS) consists of several IDS over a large network(s), all of which communicate with each other, with a central server or with a cluster of servers that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using cooperative intelligent sensors distributed across the network(s). A significant challenge remains for IDS designers to combine data and information from numerous heterogeneous distributed agents into a coherent process which can be used to evaluate the security of the system. Multisensor data sensing, or distributed sensing, is a discipline used to combine data from multiple and diverse sensors and sources in order to make inferences about events, activities and situations. Today, common environments consists in large networks of high bandwidth. In these scenarios the amount of data produced by the sensors is extremely large so the efficient processing becomes a critical factor.
In this article we propose a framework that aims to achieve the interoperability of the diverse heterogeneous agents that compose the typical infrastructure of a DIDS. Also, we address the alert aggregation and correlation problem proposing an alert processing software pipeline.Presentado en el XI Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI
A framework for implementing a Distributed Intrusion Detection System (DIDS) with interoperabilty and information analysis
Computer Intrusion Detection Systems (IDS) are primarily designed to protect availability, condentiality and integrity of critical information infrastructures. A Distributed IDS (DIDS) consists of several IDS over a large network(s), all of which communicate with each other, with a central server or with a cluster of servers that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using cooperative intelligent sensors distributed across the network(s). A significant challenge remains for IDS designers to combine data and information from numerous heterogeneous distributed agents into a coherent process which can be used to evaluate the security of the system. Multisensor data sensing, or distributed sensing, is a discipline used to combine data from multiple and diverse sensors and sources in order to make inferences about events, activities and situations. Today, common environments consists in large networks of high bandwidth. In these scenarios the amount of data produced by the sensors is extremely large so the efficient processing becomes a critical factor.
In this article we propose a framework that aims to achieve the interoperability of the diverse heterogeneous agents that compose the typical infrastructure of a DIDS. Also, we address the alert aggregation and correlation problem proposing an alert processing software pipeline.Presentado en el XI Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI
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