Top 7 recommendation data science cybersecurity 2019
When you looking for data science cybersecurity, you must consider not only the quality but also price and customer reviews. But among hundreds of product with different price range, choosing suitable data science cybersecurity is not an easy task. In this post, we show you how to find the right data science cybersecurity along with our top-rated reviews. Please check out our suggestions to find the best data science cybersecurity for you.
Best data science cybersecurity
1. Malware Data Science: Attack Detection and Attribution
DescriptionMalware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.
Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist.
In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis.
You'll learn how to:
- Analyze malware using static analysis
- Observe malware behavior using dynamic analysis
- Identify adversary groups through shared code analysis
- Catch 0-day vulnerabilities by building your own machine learning detector
- Measure malware detector accuracy
- Identify malware campaigns, trends, and relationships through data visualization
Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.
2. How to Measure Anything in Cybersecurity Risk
FeatureJohn Wiley Sons Inc
DescriptionA ground shaking expos on the failure of popular cyber risk management methods
How to Measure Anything in Cybersecurity Risk exposes the shortcomings of current "risk management" practices, and offers a series of improvement techniques that help you fill the holes and ramp up security. In his bestselling book How to Measure Anything, author Douglas W. Hubbard opened the business world's eyes to the critical need for better measurement. This book expands upon that premise and draws from The Failure of Risk Management to sound the alarm in the cybersecurity realm. Some of the field's premier risk management approaches actually create more risk than they mitigate, and questionable methods have been duplicated across industries and embedded in the products accepted as gospel. This book sheds light on these blatant risks, and provides alternate techniques that can help improve your current situation. You'll also learn which approaches are too risky to save, and are actually more damaging than a total lack of any security.
Dangerous risk management methods abound; there is no industry more critically in need of solutions than cybersecurity. This book provides solutions where they exist, and advises when to change tracks entirely.
- Discover the shortcomings of cybersecurity's "best practices"
- Learn which risk management approaches actually create risk
- Improve your current practices with practical alterations
- Learn which methods are beyond saving, and worse than doing nothing
Insightful and enlightening, this book will inspire a closer examination of your company's own risk management practices in the context of cybersecurity. The end goal is airtight data protection, so finding cracks in the vault is a positive thingas long as you get there before the bad guys do. How to Measure Anything in Cybersecurity Risk is your guide to more robust protection through better quantitative processes, approaches, and techniques.
3. Data Mining and Machine Learning in Cybersecurity
With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need.
From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challengesdetailing cutting-edge machine learning and data mining techniques. It also:
- Unveils cutting-edge techniques for detecting new attacks
- Contains in-depth discussions of machine learning solutions to detection problems
- Categorizes methods for detecting, scanning, and profiling intrusions and anomalies
- Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions
- Details privacy-preserving data mining methods
This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.
4. AI in Cybersecurity (Intelligent Systems Reference Library)
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. Thecurrent variety and scope of cybersecurity threats far exceed the capabilities of even the most skilled security professionals. In addition, analyzing yesterdays security incidents no longer enables experts to predict and prevent tomorrows 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.
5. DATA ANALYSIS FOR NETWORK CYBER-SECURITY
DescriptionThere is increasing pressure to protect computer networks against unauthorized intrusion, and some work in this area is concerned with engineering systems that are robust to attack. However, no system can be made invulnerable. Data Analysis for Network Cyber-Security focuses on monitoring and analyzing network traffic data, with the intention of preventing, or quickly identifying, malicious activity.Such work involves the intersection of statistics, data mining and computer science. Fundamentally, network traffic is relational, embodying a link between devices. As such, graph analysis approaches are a natural candidate. However, such methods do not scale well to the demands of real problems, and the critical aspect of the timing of communications events is not accounted for in these approaches.This book gathers papers from leading researchers to provide both background to the problems and a description of cutting-edge methodology. The contributors are from diverse institutions and areas of expertise and were brought together at a workshop held at the University of Bristol in March 2013 to address the issues of network cyber security. The workshop was supported by the Heilbronn Institute for Mathematical Research.
6. A Practical Guide to Computer Forensics Investigations (Pearson IT Cybersecurity Curriculum (ITCC))
FeaturePearson It Certification
All you need to know to succeed in digital forensics: technical and investigative skills, in one book
Complete, practical, and up-to-date
Thoroughly covers digital forensics for Windows, Mac, mobile, hardware, and networks
Addresses online and lab investigations, documentation, admissibility, and more
By Dr. Darren Hayes, founder of Pace Universitys Code Detectives forensics labone of Americas Top 10 Computer Forensics Professors
Perfect for anyone pursuing a digital forensics career or working with examiners
Criminals go where the money is. Today, trillions of dollars of assets are digital, and digital crime is growing fast. In response, demand for digital forensics experts is soaring. To succeed in this exciting field, you need strong technical and investigative skills. In this guide, one of the worlds leading computer orensics experts teaches you all the skills youll need.
Writing for students and professionals at all levels, Dr. Darren Hayes presents complete best practices for capturing and analyzing evidence, protecting the chain of custody, documenting investigations, and scrupulously adhering to the law, so your evidence can always be used.
Hayes introduces todays latest technologies and technical challenges, offering detailed coverage of crucial topics such as mobile forensics, Mac forensics, cyberbullying, and child endangerment.
This guides practical activities and case studies give you hands-on mastery of modern digital forensics tools and techniques. Its many realistic examples reflect the authors extensive and pioneering work as a forensics examiner in both criminal and civil investigations.
- Understand what computer forensics examiners do, and the types of digital evidence they work with
- Explore Windows and Mac computers, understand how their features affect evidence gathering, and use free tools to investigate their contents
- Extract data from diverse storage devices
- Establish a certified forensics lab and implement good practices for managing and processing evidence
- Gather data and perform investigations online
- Capture Internet communications, video, images, and other content
- Write comprehensive reports that withstand defense objections and enable successful prosecution
- Follow strict search and surveillance rules to make your evidence admissible
- Investigate network breaches, including dangerous Advanced Persistent Threats (APTs)
- Retrieve immense amounts of evidence from smartphones, even without seizing them
- Successfully investigate financial fraud performed with digital devices
- Use digital photographic evidence, including metadata and social media images
7. You'll see this message when it is too late: The Legal and Economic Aftermath of Cybersecurity Breaches (Information Policy)
DescriptionWhat we can learn from the aftermath of cybersecurity breaches and how we can do a better job protecting online data.
Cybersecurity incidents make the news with startling regularity. Each breachthe theft of 145.5 million Americans' information from Equifax, for example, or the Russian government's theft of National Security Agency documents, or the Sony Pictures data dumpmakes headlines, inspires panic, instigates lawsuits, and is then forgotten. The cycle of alarm and amnesia continues with the next attack, and the one after that. In this book, cybersecurity expert Josephine Wolff argues that we shouldn't forget about these incidents, we should investigate their trajectory, from technology flaws to reparations for harm done to their impact on future security measures. We can learn valuable lessons in the aftermath of cybersecurity breaches.
Wolff describes a series of significant cybersecurity incidents between 2005 and 2015, mapping the entire life cycle of each breach in order to identify opportunities for defensive intervention. She outlines three types of motives underlying these attacksfinancial gain, espionage, and public humiliation of the victimsthat have remained consistent through a decade of cyberattacks, offers examples of each, and analyzes the emergence of different attack patterns. The enormous TJX breach in 2006, for instance, set the pattern for a series of payment card fraud incidents that led to identity fraud and extortion; the Chinese army conducted cyberespionage campaigns directed at U.S.-based companies from 2006 to 2014, sparking debate about the distinction between economic and political espionage; and the 2014 breach of the Ashley Madison website was aimed at reputations rather than bank accounts.