Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Partner Perspectives //

bitdefender

12/12/2016
10:00 AM
Razvan Muresan
Razvan Muresan
Partner Perspectives
50%
50%

Machine-Learning Algorithms Improve Detection Time For Modern Threats

Artificial intelligence and machine learning are essential to combat a threat landscape that is larger and more sophisticated than ever.

Artificial intelligence and machine learning have become key drivers of innovation. Machine-learning algorithms significantly improve detection time for modern threats, as they can analyze large amounts of data significantly faster than any human could. If trained to accurately detect various types of malware behavior, machine-learning algorithms can have a high detection rate, even on new or unknown samples.

The merging of human ingenuity with the speed and relentless data analysis of machine learning significantly accelerates reactions against new malware, offering protection even from previously unknown samples – advanced persistent threats, zero-day attacks, and ransomware. However, it’s not always just a single machine-learning algorithm doing the detection. Detecting ransomware, for example, requires several algorithms, each specialized in detecting specific families with individual behaviors. This significantly increases the chances of detecting similar looking malware samples while reducing the number of false positives.

For its part, Bitdefender invests a quarter of its R&D budget in disruptive ideas, boosting its number of patents. From a total of 72 patents, Bitdefender has had 42 patents issued for core technologies in the past three years. In addition, 35 more are currently filed for examination. With almost 10% of Bitdefender’s patents pertaining to machine-learning algorithms for detecting malware and other online threats, deep learning and anomaly-based detection techniques play a vital role in proactively fighting new and unknown threats.

Bitdefender holds patents in all major areas of interest: machine-learning, antispam/anti-phishing/antifraud, antimalware, virtualization, BOX-functionality, and hardware design, among others. Bitdefender’s team of engineers and researchers reached the 600+ milestone this year. The company has been working on machine-learning algorithms since 2009, developing and training them to identify new and unknown threats. Artificial intelligence and machine learning are essential to combat a threat landscape that is larger and more sophisticated than ever.

Many of its patents hold the secrets to Bitdefender’s most recent innovations -- Bitdefender BOX, a solution that protects all of a user’s connected devices; and Hypervisor Introspection (HVI), a framework to secure virtualized environments from advanced targeted cyberattacks.

Bitdefender started integrating machine-learning technologies into its detection systems seven years ago, and its recent patents continue to help it achieve a high detection rate for new malware released in the wild.

Razvan, a security specialist at Bitdefender, is passionate about supporting SMEs in building communities and exchanging knowledge on entrepreneurship. A former business journalist, he enjoys taking innovative approaches to hot topics and believes that the massive amount of ... View Full Bio
Comment  | 
Print  | 
More Insights
Comments
Oldest First  |  Newest First  |  Threaded View
COVID-19: Latest Security News & Commentary
Dark Reading Staff 9/17/2020
Cybersecurity Bounces Back, but Talent Still Absent
Simone Petrella, Chief Executive Officer, CyberVista,  9/16/2020
Meet the Computer Scientist Who Helped Push for Paper Ballots
Kelly Jackson Higgins, Executive Editor at Dark Reading,  9/16/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Special Report: Computing's New Normal
This special report examines how IT security organizations have adapted to the "new normal" of computing and what the long-term effects will be. Read it and get a unique set of perspectives on issues ranging from new threats & vulnerabilities as a result of remote working to how enterprise security strategy will be affected long term.
Flash Poll
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-14180
PUBLISHED: 2020-09-21
Affected versions of Atlassian Jira Service Desk Server and Data Center allow remote attackers authenticated as a non-administrator user to view Project Request-Types and Descriptions, via an Information Disclosure vulnerability in the editform request-type-fields resource. The affected versions are...
CVE-2020-14177
PUBLISHED: 2020-09-21
Affected versions of Atlassian Jira Server and Data Center allow remote attackers to impact the application's availability via a Regex-based Denial of Service (DoS) vulnerability in JQL version searching. The affected versions are before version 7.13.16; from version 7.14.0 before 8.5.7; from versio...
CVE-2020-14179
PUBLISHED: 2020-09-21
Affected versions of Atlassian Jira Server and Data Center allow remote, unauthenticated attackers to view custom field names and custom SLA names via an Information Disclosure vulnerability in the /secure/QueryComponent!Default.jspa endpoint. The affected versions are before version 8.5.8, and from...
CVE-2020-25789
PUBLISHED: 2020-09-19
An issue was discovered in Tiny Tiny RSS (aka tt-rss) before 2020-09-16. The cached_url feature mishandles JavaScript inside an SVG document.
CVE-2020-25790
PUBLISHED: 2020-09-19
** DISPUTED ** Typesetter CMS 5.x through 5.1 allows admins to upload and execute arbitrary PHP code via a .php file inside a ZIP archive. NOTE: the vendor disputes the significance of this report because "admins are considered trustworthy"; however, the behavior "contradicts our secu...