One Article Review
| Source |  AlienVault Blog |
|---|---|
| Identifiant | 2109331 |
| Date de publication | 2020-12-18 06:01:00 (vue: 2020-12-18 07:06:07) |
| Titre | What is next gen antivirus? NGAV explained |
| Texte | This blog was written by a third party author. What is next gen antivirus (NGAV) and how does it work? In contrast to legacy antivirus technology, next generation antivirus (NGAV) advances threat detection on the endpoint by finding all symptoms of malicious behavior across an endpoint system rather than fixating on looking for known malware file attributes. NGAV uses artificial intelligence (AI) and machine learning algorithms to examine files, processes, applications, network activity, and user behavior to identify atypical activity that could indicate malicious attacks are unfolding on the endpoint. The AI that NGAV depends upon is constantly learning from historical and ongoing system behavior to develop baselines for what 'normal' looks like on a given activity. These baselines can then be used to compare real-time activity. The predictive analytics get better over time at pinpointing anomalies that are likely to track to malicious behavior. This approach makes it possible to block new attack techniques in real-time, before they've ever been identified and catalogued by security researchers. Comparing NGAV vs. legacy antivirus NGAV development progressed in response to the shortcomings on traditional file-based signatures and heuristics, which depend upon previous knowledge about malware characteristics and behaviors to flag potential infections. Attackers learned a long time ago how to evade such signature-based detection methods by creating polymorphic malware and otherwise changing up attributes of their malware on a consistent basis so that the life of a malware signature is so short as to make it ineffective nearly instantly. According to recent figures, some 70% of all malware attacks today involve zero-day malware that evade signature detection with previously undocumented characteristics or behaviors. NGAV picks up on emerging threats like these because it doesn't require creating complicated rule sets in advance of the attack. Instead, it seeks out differences between the activity and the baseline to spot new behavior that's suspicious because it is outside the norm. Additionally, cybercriminals also increasingly utilize fileless attack techniques to avoid leaving tracks that could be detected through signatures. This includes utilizing macros, scripting engines and platforms like PowerShell, in-memory attacks, and other 'living off the land' attacks that don't require dropping files to carry out malicious ends. According to a recent analysis, the most common critical-severity cybersecurity threat to endpoints was fileless malware, followed closely by dual-use PowerShell tools that are used in exploitation and post-exploitation behavior. All told, those make up 54% of threat tactics, compared to traditional malware like worms, banking trojans, and remote access tools (RATs), which all together only comprised 14% of tactics. Utilizing NGAV makes it possible to pick up on behavior from fileless attacks since it is not tied just to what the malware drops on the system, but instead keeps tabs on how the entire system is working. Why NGAV matters to cybersecurity programs According to Ponemon Institute, the average economic loss of a single endpoint breach now adds up to $8.94 million. More than five in 10 organizations say that their endpoint security solutions can't detect advanced attacks—respondents estimate that their legacy AV products miss an average of 60% of attacks. Respondents are also increasingly unsatisfied with traditional antivirus not only for what they don't detect, |
| Envoyé | Oui |
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| Tags | Malware Threat |
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