國立高雄大學圖資館 |

Language: English

Back

Android malware detection using mach...

Karbab, ElMouatez Billah.

Android malware detection using machine learningdata-driven fingerprinting and threat intelligence /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Android malware detection using machine learningby ElMouatez Billah Karbab ... [et al.].
Reminder of title:	data-driven fingerprinting and threat intelligence /
other author:	Karbab, ElMouatez Billah.
Published:	Cham :Springer International Publishing :2021.
Description:	xiv, 202 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Malware (Computer software)Congresses.
Online resource:	https://doi.org/10.1007/978-3-030-74664-3
ISBN:	9783030746643$q(electronic bk.)

Android malware detection using machine learningdata-driven fingerprinting and threat intelligence /
Android malware detection using machine learningdata-driven fingerprinting and threat intelligence /[electronic resource] :by ElMouatez Billah Karbab ... [et al.]. - Cham :Springer International Publishing :2021. - xiv, 202 p. :ill., digital ;24 cm. - Advances in information security,v.861568-2633 ;. - Advances in information security ;12..

Introduction -- Background and Related Work -- Fingerprinting Android Malware Packages -- Robust Android Malicious Community Fingerprinting -- Android Malware Fingerprinting Using Dynamic Analysis -- Fingerprinting Cyber-Infrastructures of Android Malware -- Portable Supervised Malware Fingerprinting using Deep Learning -- Resilient and Adaptive Android Malware Fingerprinting and Detection -- Conclusion.

The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.

ISBN: 9783030746643$q(electronic bk.)

Standard No.: 10.1007/978-3-030-74664-3doiSubjects--Uniform Titles:

Android (Electronic resource)
Subjects--Topical Terms:

451427
Malware (Computer software)
--Congresses.

LC Class. No.: QA76.76.C68

Dewey Class. No.: 005.88

Android malware detection using machine learningdata-driven fingerprinting and threat intelligence /
LDR:03619nmm a2200337 a 4500 001 602009
003 DE-He213
005 20210710114750.0
006 m d
007 cr nn 008maaau
008 211112s2021 sz s 0 eng d
020 $a 9783030746643$q(electronic bk.)
020 $a 9783030746636$q(paper)
024 7 $a 10.1007/978-3-030-74664-3 $2 doi
035 $a 978-3-030-74664-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.C68
072 7 $a UTN $2 bicssc
072 7 $a COM043050 $2 bisacsh
072 7 $a UTN $2 thema
082 0 4 $a 005.88 $2 23
090 $a QA76.76.C68 $b A574 2021
245 0 0 $a Android malware detection using machine learning $h [electronic resource] : $b data-driven fingerprinting and threat intelligence / $c by ElMouatez Billah Karbab ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 202 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in information security, $x 1568-2633 ; $v v.86
505 0 $a Introduction -- Background and Related Work -- Fingerprinting Android Malware Packages -- Robust Android Malicious Community Fingerprinting -- Android Malware Fingerprinting Using Dynamic Analysis -- Fingerprinting Cyber-Infrastructures of Android Malware -- Portable Supervised Malware Fingerprinting using Deep Learning -- Resilient and Adaptive Android Malware Fingerprinting and Detection -- Conclusion.
520 $a The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
630 0 0 $a Android (Electronic resource) $3 376300
650 0 $a Malware (Computer software) $v Congresses. $3 451427
650 0 $a Computer security. $3 184416
650 0 $a Machine learning. $3 188639
650 1 4 $a Mobile and Network Security. $3 825732
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Mobile Computing. $3 763350
700 1 $a Karbab, ElMouatez Billah. $3 897605
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Advances in information security ; $v 12. $3 451557
856 4 0 $u https://doi.org/10.1007/978-3-030-74664-3
950 $a Computer Science (SpringerNature-11645)