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Thanks to Big Data, Threat Detection in Real Time

2017-08-17 11:25

Endpoints have traditionally proven to be the Achilles heel of any corporate network’s security. Protecting them is vital to any security strategy. The MO of traditional antivirus solutions is to classify software using black-and-white lists. But this is not enough, as threats continue to find new ways to wriggle their way into our systems.

Modern times, new tools

A company whose devices are protected against malware by traditional methods is wide open to unknown attacks, period. The malware it is protected against is classified on lists based on signatures and previously collected data. Whitelist systems classify executable files based on this data and the known signatures of malware that has already been “caught”, as well as real-time detection techniques. Such solutions allow so-called “goodware” to do its work, while trying to avoid false positives. But what if this software changed at some point and became used for malicious purposes? It would be virtually impossible to react in time because the software is already whitelisted. It becomes necessary to constantly observe what software is doing at any given time, monitoring its behavior and stopping any type of suspicious action.

The best EPP systems allow not only to classify the executables, but also their behavior. This continuous monitoring allows for immediate action in response to new threats. Key performance profiles are categorized and recorded for each executable and analyzed in real time.

Special techniques are needed to obtain, manage and analyze massive flows of information. Mining data then analyzing it with so-called Big Data Analytics, which uses advanced algorithms and artificial intelligence, we can detect and evaluate, in real time, the action of possible malware. It also becomes possible to input specifically tailored countermeasures, as well as to see in detail the scope of possible damage caused by the malicious software. These aspects are key to any next-generation EDR solution.

Big Data can detect 100% threats

Sadly, many companies tend to hold onto the “tried and true” approaches to cybersecurity long after they are no longer relevant, while cybercriminals are always up on the latest developments. Security officers need to understand protection as an active system and not as a static measure to make it effective. A good EPP solution must be able to identify changes in data patterns at all times, which gets all kinds data sources involved: backlogs, network events, user activity, etc. This can only be possible, as we have said, with advanced analytical capabilities and, of course, the possibility of gathering a wealth of data. Thinking about security as an active process at all times and developing solutions accordingly is tantamount to placing your bets on the resilience of the system.

Unlike traditional SIEM solutions, modern varieties use algorithms based on Machine Learning to optimize their tasks. This creates active protection, which improves both in terms of detections and in responding to new threats. The effectiveness of solutions such as Panda Adaptive Defense 360 ​​is such that it can anticipate attacks (both internal and external) and generate automatic workflows to ensure protection.

Today, using Big Data as the basis for defending against new threats can even mean increasing company profits, as well as avoiding possible losses caused by cyberattacks. Company stability begins with proper security, and proper security begins with having control over every aspect of your network.

The post Thanks to Big Data, Threat Detection in Real Time appeared first on Panda Security Mediacenter.


Source: /noitceted-taerht-atad-gib/ytiruces/retnecaidem/moc.ytirucesadnap.www

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