Widget Image
CURRENT ISSUE
ISSUE 00
SUPPORT US
PURCHASE PRINT
Follow us:
Saturday / April 10.
  • No products in the cart.

Growing Impact On Security

[IMAGE: GETTY IMAGES]

Machine Learning systems are often excellent learners. They can achieve superhuman performance in a wide range of activities, including detecting fraud and diagnosing disease. Excellent digital learners are being deployed across the economy, and their impact will be profound.
Today, this technique is revolutionizing natural language processing and malware detection. Deep learning can figure out how to solve tough problems, such as identifying suspicious online behaviour. This technique and related systems and tools will play an increasingly greater role in anti-fraud and security applications.

 

1. Spotting inappropriate behaviour
Social networks and other forums where users can contribute content sometimes attract deviant behaviour, such as people posting pornographic or violent images. With deep learning, companies can automatically spot prohibited content instead of employing people to manually review images reported from users. This saves money and time and is a more proactive way of ensuring that users are not violating company policies.

 

2. Photo verification
Cybercriminals often create fake photos and IDs. This gives them access to a new identity, so they can create fake accounts to dupe users into sharing data or signing up for bogus services. Large-scale marketplaces such as Airbnb are increasingly affected by these attacks. Deep neural networks can be trained to identify manipulated or duplicate images, and since 2015, neural networks have been outperforming humans on similar image-recognition tasks.

 

3. Phishing emails
Phishing the practice of sending emails that appear to come from legitimate senders such as UPS or a bank continue to trick people into clicking on the links and opening their PCs to data-stealing viruses. Some of us unwittingly give up our personal data, including account numbers and passwords, to these scammers. Deep-learning systems can be trained to recognize these phishing emails and prevent them from getting delivered to anyone’s inbox.

 

4. Spam detection
Deep learning can root out all forms of unwanted email by learning the difference between junk and legitimate messages. Deep neural networks can understand the concepts included in the email’s text and can, for example, identify if the email includes a call to action to purchase a product.

 

5. User and entity behaviour analytics
User and entity behaviour analytics focuses on analysing the behaviours of people who are connected to an organization’s network as well as entities such as servers, accounts, laptops, and so on. UEBA is used for external breach detection and for identifying rogue insiders by analysing what is normal behaviour such as where users normally log in from and what applications they access and looking for what is not. Deep learning reduces the feature engineering required for UEBA, and neural networks can learn patterns of user behaviour that may indicate a malicious session.