Impact of Deep Learning | Cyber Security & Malware Detection

Speaker: Daniel Ingitaraj

Thursday, February 20th, 2020, 6:30 PM

 
Event Summary

A lot of detection software and methods today are unable to detect malware and other APTs (Advanced Persistent Threats) because they rely on heuristics that are manually designed and tuned. Deep Learning systems therefore represents a system that does not require manual imprints and interpretations. A lot of advanced solutions today make use of manually selected criteria, which is then imputed in a machine that is able to classify files as malicious or safe. This is the reason why evolved malware systems are able to bypass this detection software.

In addition, ISSA Chapter President Justin White will lead an analysis of the most recent news and discuss how cyber attackers continue to evolve during his monthly Garden Fresh presentation. In this presentation, we will explore the trends that define today’s threat landscape such as recent cyber attacks, intelligence-led insights, and advanced defensive strategies.

Join us for the after meeting soirée with food and drinks at the Islander Restaurant. Plan on bringing a friend!!! This is a great opportunity to network with your peers, introduce your friend and to meet new people: Address 2441 76th Ave SE, Mercer Island, WA 98040

Speaker

Daniel Ingitaraj is CEO and CoFounder of marketingAI.

For businesses that greatly depend on their systems to be functional, malware can represent a crippling or devastating effect on operations. It is therefore very important that Deep Learning is applied to cyber security and malware detection. This is the only way through which computer systems are going to stand a chance. Deep Learning represents a system of more accurate detection and it could even reduce the costs incurred through dealing with these malicious systems.

Deep Learning has already started to show promising solutions when compared to classical machine learning. Its detection always supersede any solution provided by manual systems and represents the one true chance that these businesses have to cope with these threats.

Also, Deep Learning does not require any manual engineering as opposed to other solutions. All that is required is for information of millions of corrupt and malicious files are loaded into the framework, enabling the Deep Learning system to develop its own understanding of the subject matter and begin to provide solutions. Also, because of its input-agnostic features, Deep Learning is able to detect a file framework such as EXE, PDF, DOC, DLL etc

Deep Learning provides a cutting-edge revolution in terms of accurate detection of malware and cyber threats. It also facilitates real time detection and prevention, which is almost instinctive. A huge benefit of this is that it can be applied on any platform such as computers, smart devices and mobile phones.

Deep Learning has therefore begun to blaze a trail for other systems to follow when it comes to cyber security and malware protection. Thanks to Deep Learning, the manual imprint and orientation required for systems can be done away with. Computer frameworks can advance at a rate that is far quicker than regular manual techniques. This is the best possible solution if computer systems are going to have to keep up with these constantly evolving cyber threats.

Chapter Meeting Time:
Thursday, February 20, 2020
6:30 pm – 8:30 pm

Location:
Community Center at Mercer Island
8236 SE 24th Street Mercer Island, WA 98040

Thanks to our Chapter Sponsors:

Diamond Level Sponsor:

Code42

Detect, Investigate, and Responde to Insider Threats

Covestic

Professional It & Service Now Consultating Services

Gold Level Sponsor:

RedSeal

Cyber Risk Modeling for Hybrid Environments

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