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Advanced Persistent threats and how to monitor and deter them

Tankard, Colin·2011·Network Security

Abstract

Advanced Persistent Threat (APT) is a term coined over the past couple of years for a new breed of insidious threats that use multiple attack techniques and vectors and that are conducted by stealth to avoid detection so that hackers can retain control over target systems unnoticed for long periods of time. Traditional defences aimed at keeping known threats out of the network are no longer sufficient against the exploits being used to conduct such attacks. The focus should be on developing a defence in depth strategy that aims to constantly monitor networks and security controls for their effectiveness, explains Colin Tankard of Digital Pathways. The UK Government has recently estimated that cybercrime costs the country some £27bn per year and, according to some estimates, the global cost is $1 trillion every year. This crime wave has been greatly facilitated by the rise of electronic communications, primarily those making use of the Internet. The purpose of electronic communications is to make it more efficient and easier to communicate – but they are also relatively easy to attack or intercept. No-one is immune – such attacks are aimed at individuals, small firms, multinationals and governments.

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Metadata

Authors
Tankard, C.
Venue
Network Security
Published
2011-08
Volume
2011
Issue
8
Pages
16-19
ISSN
1353-4858 (ISSN-L)
OpenAlex
W2039858940
Cited by
388
References
0
FWCI
17.72
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Advanced Persistent threats and how to monitor and deter them. https://app.cochat.ai/papers/10.1016/s1353-4858(11)70086-1
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Tankard, C. (2011). Advanced Persistent threats and how to monitor and deter them. Network Security. https://doi.org/10.1016/s1353-4858(11)70086-1

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.