國立高雄大學圖資館 |

Language: English

Back

AI in cybersecurity

Sikos, Leslie F.

AI in cybersecurity

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI in cybersecurityedited by Leslie F. Sikos.
other author:	Sikos, Leslie F.
Published:	Cham :Springer International Publishing :2019.
Description:	xvii, 205 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-319-98842-9
ISBN:	9783319988429$q(electronic bk.)

AI in cybersecurity
AI in cybersecurity[electronic resource] /edited by Leslie F. Sikos. - Cham :Springer International Publishing :2019. - xvii, 205 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1511868-4394 ;. - Intelligent systems reference library ;v.24..

OWL Ontologies in Cybersecurity: Conceptual Modeling of Cyber-Knowledge -- Knowledge Representation of Network Semantics for Reasoning-Powered Cyber-Situational Awareness -- The Security of Machine Learning Systems -- Patch Before Exploited: An Approach to Identify Targeted Software Vulnerabilities -- Applying Artificial Intelligence Methods to Network Attack Detection -- Machine Learning Algorithms for Network Intrusion Detection -- Android Application Analysis using Machine Learning Techniques.

This book presents a collection of state-of-the-art AI approaches to cybersecurity and cyberthreat intelligence, offering strategic defense mechanisms for malware, addressing cybercrime, and assessing vulnerabilities to yield proactive rather than reactive countermeasures. The current variety and scope of cybersecurity threats far exceed the capabilities of even the most skilled security professionals. In addition, analyzing yesterday's security incidents no longer enables experts to predict and prevent tomorrow's attacks, which necessitates approaches that go far beyond identifying known threats. Nevertheless, there are promising avenues: complex behavior matching can isolate threats based on the actions taken, while machine learning can help detect anomalies, prevent malware infections, discover signs of illicit activities, and protect assets from hackers. In turn, knowledge representation enables automated reasoning over network data, helping achieve cybersituational awareness. Bringing together contributions by high-caliber experts, this book suggests new research directions in this critical and rapidly growing field.

ISBN: 9783319988429$q(electronic bk.)

Standard No.: 10.1007/978-3-319-98842-9doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: TA347.A78

Dewey Class. No.: 006.3

AI in cybersecurity
LDR:02662nmm a2200337 a 4500 001 552534
003 DE-He213
005 20190617110848.0
006 m d
007 cr nn 008maaau
008 191106s2019 gw s 0 eng d
020 $a 9783319988429$q(electronic bk.)
020 $a 9783319988412$q(paper)
024 7 $a 10.1007/978-3-319-98842-9 $2 doi
035 $a 978-3-319-98842-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TA347.A78
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a TA347.A78 $b A288 2019
245 0 0 $a AI in cybersecurity $h [electronic resource] / $c edited by Leslie F. Sikos.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xvii, 205 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.151
505 0 $a OWL Ontologies in Cybersecurity: Conceptual Modeling of Cyber-Knowledge -- Knowledge Representation of Network Semantics for Reasoning-Powered Cyber-Situational Awareness -- The Security of Machine Learning Systems -- Patch Before Exploited: An Approach to Identify Targeted Software Vulnerabilities -- Applying Artificial Intelligence Methods to Network Attack Detection -- Machine Learning Algorithms for Network Intrusion Detection -- Android Application Analysis using Machine Learning Techniques.
520 $a This book presents a collection of state-of-the-art AI approaches to cybersecurity and cyberthreat intelligence, offering strategic defense mechanisms for malware, addressing cybercrime, and assessing vulnerabilities to yield proactive rather than reactive countermeasures. The current variety and scope of cybersecurity threats far exceed the capabilities of even the most skilled security professionals. In addition, analyzing yesterday's security incidents no longer enables experts to predict and prevent tomorrow's attacks, which necessitates approaches that go far beyond identifying known threats. Nevertheless, there are promising avenues: complex behavior matching can isolate threats based on the actions taken, while machine learning can help detect anomalies, prevent malware infections, discover signs of illicit activities, and protect assets from hackers. In turn, knowledge representation enables automated reasoning over network data, helping achieve cybersituational awareness. Bringing together contributions by high-caliber experts, this book suggests new research directions in this critical and rapidly growing field.
650 0 $a Artificial intelligence. $3 194058
650 0 $a Computer security. $3 184416
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Security. $3 760527
700 1 $a Sikos, Leslie F. $3 545201
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.24. $3 558591
856 4 0 $u https://doi.org/10.1007/978-3-319-98842-9
950 $a Intelligent Technologies and Robotics (Springer-42732)