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• W2774628209 abstract "Computer network defense has traditionally been provided using reactionary tools such as signature-based detectors, white/blacklisting, intrusion detection/protection systems, etc. While event detection/correlation techniques may identify threats — those threats are then dealt with manually, often employing obstruction-based responses (e.g., blocking). As threat sophistication grows, we find these perimeter-planted security efforts ineffective in combating competent adversaries. In 2015 Gartner, Inc. examined the potential for organizations to use deception as a strategy for thwarting attackers and making it costlier for adversaries to engage in threat campaigns. In today's current research, there are a limited number of deception platforms (tools, etc.) that have successfully been shown to enable strategic deception in a computer network operations environment. Through a deception framework, we conjecture that deception platforms can aid and assist in deceiving the adversary by: obscuring the real target, devaluing information gathering, causing the adversary to waste time and resources, forcing the adversary to reveal advanced capabilities, exposing adversary intent, increasing the difficulty of attack planning, limiting the scope of the attack, and limiting the duration of a successful attack. The objective of this paper is to survey the technological trends in cyber deception research, identify gaps in the techniques, and provide research in the emergent environment. Current findings suggest that network deception tools are attracting the interest of researchers as a valuable security technique that can be implemented to learn more about the nature of cyber attacks; however, there are significant shortcomings in the current approaches and the ability to reason about the adversary." @default.
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• W2774628209 date "2017-10-01" @default.
• W2774628209 modified "2023-09-23" @default.
• W2774628209 title "Technologies to enable cyber deception" @default.
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• W2774628209 doi "https://doi.org/10.1109/ccst.2017.8167793" @default.
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