Secure your Cloud with StegoSOC AI driven threat detection
This post is about how we are trying to automate the threat detection for an enterprise be it public, private or hybrid cloud setup.
Real time threat detection is imperative with GDPR and other cyberlaws. Threat detection is always been an expensive solution to implement. It requires CSO and Security Analyst to implement the solution.
StegoSOC brings down time to detect threats at 1/10th cost on public clouds with AI and cloud-native technologies.
Security is of paramount significance for any organization having its production servers deployed in its own enterprise infrastructure or in the cloud. Due to unceasing evolution of software vulnerabilities, misconfigured devices in the network and software vulnerabilities already present in the devices in the network, an enterprise is always under a major risk of intrusion by an attack either from inside or outside the organization network. These attackers are highly proficient, have malicious intentions and can cause various kinds of exploits leading to attacks targeting critical resource or tampering the integrity of crucial information present in assets in the network, which might influence a company’s major business decision.
What have we done?
We started off with logs as an input, since application is what is exposed off mostly. But as we all know, logs aren’t structured depending upon the applications you use & mechanism of your logging. To add to this, there is always an ever-increasing list of tools/applications being deployed on servers with a different structure every time.
As “thehackernews” rightly quotes here
The very purpose of IT security is to be proactive and the above measures make it more difficult for someone who attempts to compromise the network. This might just not be enough and you need to able to detect the actual breaches as they are being attempted. This is where log data really help.
To expose an attack or identify the damage caused, you need to analyze the log events on your network in real-time. By collecting and analyzing logs, you can understand what transpires within your network. Each log file contains many pieces of information that can be invaluable, especially if you know how to read them and analyze them. With proper analysis of this actionable data you can identify intrusion attempts, mis-configured equipment, and many more. Also for managing compliance, especially for PCI DSS — you need to retain logs and review them.
I will now describe step-by-step AI coons that we have deployed for customer.
So, our first hurdle. How do we come up with a universal structure for logs ?
I will not go into technical details, but the foremost business requirements of Log-parsers were :-
- Parse a log into meaningful attributes.
- if log format is not supported, log ingestion pipeline should quickly (roughly a few hours — depending upon traffic of unknown log formats) ingest the new format into existing supported formats.
- It should support a enterprise specific logging along with a global engine.
L1 — Rule based detection
This module should support filtration through handwritten rules. The handwritten rules helps to filter out common attacks in Cybersecurity and it also helps us to put enterprise in loop to draft their own rulesets. Since every enterprise has their own set of route rules, firewall rules, change management policies etc, this was really important. To incorporate all of this, it was really important to have global plus local rulesets.
L2 — Anomaly detection
Whatever goes undetected, goes through Anomaly detection, which not only alerts the SOC admin every hours, as to what is happening in their infra but also tells who is producing anomalies in your system.
L3 — Attack Graph
Logs analysed, now what ?
To extend this, we then started an additional effort to model the interaction of vulnerabilities present in the system and the network configuration. The information in the National Vulnerability Database (NVD), the information extracted from machine and network configurations are used as base information for the attack graph engine. We also try to capture the operating system’s behaviour and interaction of various components in the network.
Advisories: Vulnerabilities that exist on the machine
Host Configuration: software and services running on the hosts, and their configurations.
Network Configuration: configurations of the network routers and firewalls
Principles: legitimate users of the company’s network
Interaction: interaction model of the network elements
Policy: permitted policy
Above you can see a sample run of attack graph engine on our AWS account, wherein it shows, how an attacker can reach each machine on account of vulnerabilities that exist in the enterprise and network configuration.
Here’s complete snapshot of what we have done
I know the graph snapshot is hard to understand, so in next post, I will write technical details on this as well as Neo4J Visualization.
Security Lead: Munish Kumar
Product Lead: Mir Adnan