4,847 research outputs found
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
Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET
Wireless networks that are decentralized and communicate without using
existing infrastructure are known as mobile ad-hoc networks. The most common
sorts of threats and attacks can affect MANETs. Therefore, it is advised to
utilize intrusion detection, which controls the system to detect additional
security issues. Monitoring is essential to avoid attacks and provide extra
protection against unauthorized access. Although the current solutions have
been designed to defeat the attack nodes, they still require additional
hardware, have considerable delivery delays, do not offer high throughput or
packet delivery ratios, or do not do so without using more energy. The
capability of a mobile node to forward packets, which is dependent on the
platform's life quality, may be impacted by the absence of the network node
power source. We developed the Safe Routing Approach (SRA), which uses
behaviour analysis to track and monitor attackers who discard packets during
the route discovery process. The attacking node recognition system is made for
irregular routing node detection to protect the controller network's usual
properties from becoming recognized as an attack node. The suggested method
examines the nearby attack nodes and conceals the trusted node in the routing
pathway. The path is instantly assigned after the initial discovery of trust
nodes based on each node's strength value. It extends the network's life span
and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy
consumption, network performance, and detection of attack nodes, the suggested
approach is contrasted with AIS, ZIDS, and Improved AODV. The findings
demonstrate that the recommended strategy performs superior in terms of PDR,
residual energy, and network throughput
Using Sequence Analysis to Perform Application-Based Anomaly Detection within an Artificial Immune System Framework
The Air Force and other Department of Defense (DoD) computer systems typically rely on traditional signature-based network IDSs to detect various types of attempted or successful attacks. Signature-based methods are limited to detecting known attacks or similar variants; anomaly-based systems, by contrast, alert on behaviors previously unseen. The development of an effective anomaly-detecting, application based IDS would increase the Air Force\u27s ability to ward off attacks that are not detected by signature-based network IDSs, thus strengthening the layered defenses necessary to acquire and maintain safe, secure communication capability. This system follows the Artificial Immune System (AIS) framework, which relies on a sense of self , or normal system states to determine potentially dangerous abnormalities ( non self ). A method for anomaly detection is introduced in which self\u27 is defined by sequences of events that define an application\u27s execution path. A set of antibodies that act as sequence detectors are developed and used to attempt to identify modified data within a synthetic test set
Towards Loop-Free Forwarding of Anonymous Internet Datagrams that Enforce Provenance
The way in which addressing and forwarding are implemented in the Internet
constitutes one of its biggest privacy and security challenges. The fact that
source addresses in Internet datagrams cannot be trusted makes the IP Internet
inherently vulnerable to DoS and DDoS attacks. The Internet forwarding plane is
open to attacks to the privacy of datagram sources, because source addresses in
Internet datagrams have global scope. The fact an Internet datagrams are
forwarded based solely on the destination addresses stated in datagram headers
and the next hops stored in the forwarding information bases (FIB) of relaying
routers allows Internet datagrams to traverse loops, which wastes resources and
leaves the Internet open to further attacks. We introduce PEAR (Provenance
Enforcement through Addressing and Routing), a new approach for addressing and
forwarding of Internet datagrams that enables anonymous forwarding of Internet
datagrams, eliminates many of the existing DDoS attacks on the IP Internet, and
prevents Internet datagrams from looping, even in the presence of routing-table
loops.Comment: Proceedings of IEEE Globecom 2016, 4-8 December 2016, Washington,
D.C., US
From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods
Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communicatio
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