FACEBOOK LATEST UPDATE | FACEBOOK SECURITY | LATEST UPDATE OF FACEBOOK

With 800 millon users around world facebook security is very tough........
as known there is 25 millon actions in a day on facebook every day...... Facebook security is important for all users around world......

 FACEBOOK  released about its new massive security infrastructure called the " FACEBOOK IMMUNE SYSTEM" for catching any suspicious activities on facebook.........

It protects against scams by harnessing artificially intelligent software to detect suspicious patterns of behaviour. 


This technique has been developed over a 3 year peiod will will save scam in facebook too much.....


The number of users affected by spam has been reduced to less than 1%. 


Microsoft Research has put forward a PDF detailing the principles of FIS.

• Classifier services: Classifier services are networked interfaces to an abstract classifier interface. That abstraction is implemented by a number of different machine-learning algorithms, using standard object-oriented methods. Implemented algorithms include random forests, SVMs, logistic regression, and a version of boosting, among other algorithms. Classifier services are always online and are designed never to be restarted.

• Feature Extraction Language (FXL): FXL is the dynamically executed language for expressing features and rules. It is a Turing-complete, statically-typed functional language. Feature expressions are checked then loaded into classifier services and feature tailers1 online, without service restart.

• Dynamic model loading: Models are built on features and those features are either basic or derived via an FXL expression. Like features, models are loaded online into classifier services, without service or tailer restart. As well, many of classifier implementations support online training.

• Policy Engine: Policies organize classification and features to express business logic, policy, and also holdouts for evaluating classifier performance. Policies are Boolean-valued FXL expressions that trigger responses. Policies execute on top of machine-learned classification and feature data providers. Responses are system actions. There are numerous responses.

Some examples are blocking an action, requiring an authentication challenge, and disabling an account.

• Feature Loops (Floops): Classification generates all kinds of information and associations during feature extraction. The floops take this data, aggregate it, and make it available to the classifiers as features. The floops also incorporate user feedback, data from crawlers2, and query data from the data warehouse.

Although FIS has come a long way in tackling spam, it should be noted that FIS is still vulnerable to tactics that are new to it such as socialbots. A socialbot works by sending friend requests to random people. The profile data of people who accept this friend request is used for identity theft, phishing attacks etc.

So it is always up to the end user to remain cautious of these types of attacks in order to protect your personal information.