Handbook of Big Data Analytics and Forensics
Kim-Kwang Raymond Choo, Ali Dehghantanha

Informasi Umum

Kode

23.21.1203

Klasifikasi

005.8 - Data Security

Jenis

Buku - Elektronik (E-Book)

Subjek

Data Security, Data Analytics

No. Rak

Tel-U Purwokerto : Rak 2
Tel-U Bandung - Gedung Manterawu Lantai 5 : Rak 4

Dilihat

142 kali

Informasi Lainnya

Abstraksi

This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud’s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter.

The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS’s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS’s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated.

This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters.

This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.

• TTI4I3 - AI DAN BIG DATA ANALYSIS
• TTI4I3 - AI dan Big Data Analysis
• CII6F3 - ANALISIS BIG DATA
• CII6F3 - ANALISIS BIG DATA
• CTI4R3 - ANALITIK BIG DATA UNTUK IOT
• CSI4D3 - ANALITIK BIG DATA UNTUK IOT
• CTJ4R3 - Analitik Big Data Untuk IOT
• EBI3B4 - BIG DATA AND DATA ANALYTICS
• EBI3B4 - BIG DATA AND DATA ANALYTICS
• ELI1E3 - BIG DATA DAN ANALISIS DATA
• EMI1E3 - BIG DATA DAN ANALITIK DATA
• KBI6J3 - MANAJEMEN & ANALISIS BIG DATA
• GIK2DAB2 - Analitik Data
• ACK4FBB3 - Big Data

Koleksi & Sirkulasi

Tersedia 1 dari total 1 Koleksi

Pengarang

Nama	Kim-Kwang Raymond Choo, Ali Dehghantanha
Jenis	Perorangan
Penyunting
Penerjemah

Penerbit

Nama	Springer
Kota	New York
Tahun	2022

Sirkulasi

Harga sewa	IDR 0,00
Denda harian	IDR 0,00
Jenis	Non-Sirkulasi

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