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Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017

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This document discusses using machine learning to identify network attack behavior. It describes classifying different types of attacks, such as DDoS, ransomware, and phishing. Short-term attacks are aimed at overwhelming services, while long-term attacks involve reconnaissance, infrastructure vulnerabilities, and data exfiltration over long periods. Current solutions are limited as they are rule-based and stateless. The document proposes using features and machine learning models to better connect events over time and identify long-term attacks. Examples of useful features for the models include connection lengths and domain name statistics. Both manual and assisted labeling approaches are described to generate training data for the models.

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H2 O.ai Machine Intelligence Identifying Network Attack Behaviour Ashrith Barthur, PhD Principal Security Scientist, H2O.ai Cybersecurity and Fraud
H2 O.ai Machine Intelligence Classification of Attacks • Short Term Goals o DDoS - Layer 2 and above o Physical Attacks • Long Term Goals o Network/Service Reconnaissance o Enterprise Service attacks - attack on infrastructure o Phishing, Spear Phishing (more focussed) o Social Engineering - Out-of-loop •
H2 O.ai Machine Intelligence Anatomy of An Attack - Short Term • Identify Target • Identify Service of Attack • Overwhelm the service • Post-Attack Analysis o Attack mechanism is simple. o Variations occur in source of attack, protocols levels. o Relatively short lived. o Damage quantifiable.
H2 O.ai Machine Intelligence Anatomy of An Attack - Long Term • Identify Target • Reconnaissance • Identify Infrastructure Vulnerability / Or means of phishing • Network Foothold • Lateral movement and service compromises • Data Exfiltration/ Network Squatting, or passive sniffing.
H2 O.ai Machine Intelligence Anatomy of An Attack - Long Term (cont) • Post-Attack Analysis (Usually an Illusion) o Attack might still continue o Variations can occur based on services, new vulnerabilities, new softwares, unused access, network segments without VLANs, un-closed, outdated wall sockets, etc. o Usually very long term o Damage assessment is not usually accurate. •
H2 O.ai Machine Intelligence Does This Mean We Focus on Long Term Attacks?
H2 O.ai Machine Intelligence Why? • Short Term Attack and Long Term Attacks are usually used together. • Short Term Attacks are used as: o A means of Reconnaissance o A method of shielding another attack, or breaking down some basic protection before an attack is launched. o It is also used to shield any detection of data exfiltration • Therefore it is imperative that you look at all the attacks on your networks. • And look at as connected in time and events.
H2 O.ai Machine Intelligence Solutions do exist. Right? 1. Yes. Solutions do exist that correlate events 2. But are limited 3. They are purely rule-based, and mostly stateless. 4. Hardly capable of smartly identifying events related across time. - A must for identifying long term attacks.
H2 O.ai Machine Intelligence Looking at events on the network and interconnecting them gives more information (depth of Info)
H2 O.ai Machine Intelligence “So You want us to get the NSA SoC?”
H2 O.ai Machine Intelligence No, just build smarter Algorithms. Use Machine Learning - or AI (fancy)
H2 O.ai Machine Intelligence CSec Solution Evolution Rule-based Model Feature-base d Model Pure Data Driven Model
H2 O.ai Machine Intelligence Features - (Used in Feature-based Model) 1. Features are meta data (Extracted from the data) 2. They help algorithms capture information from the data. 3. Feature engineering is a form of language translation: Between raw data and the algorithm. 4. Build much better features for your supervised models.
H2 O.ai Machine Intelligence Source of Features 1. Past Attack Behaviour 2. Enterprise Traffic Behaviour 3. Enterprise Application/User Profiles a. Syslogs b. ASA, IDS, IPS, Suricata,Bro, Argus, etc.
H2 O.ai Machine Intelligence Features - Example 1. Average length of connection (too small, too large) 2. Average number of DNS requests (within network/outside network) 3. Average number of new domains 4. Change in MTU ratio vs. Windows/Mac/*Nix machine churn. 5. Packet Utilization - segmentation 6. Window Size 7. Arrival Jitter Variance
H2 O.ai Machine Intelligence Features - Example average tcp connect length 7 Days
H2 O.ai Machine Intelligence Features: Advantages 1. Designed Features Highlight Transactional Behaviour 2. Features Continuously Track Network’s Transactional Behaviour 3. Rules Variables can only Identify Threshold Changes
H2 O.ai Machine Intelligence Feature-based Model: Advantages 1. Uses AI - artificial intelligence 2. AI with features uses a consistent and objective approach 3. Quick classification 4. Low false positive rate - tweaked based on risk appetite.
H2 O.ai Machine Intelligence Successfully Classified 1. DoS 2. Recon 3. Exploits 4. CnC Traffic
H2 O.ai Machine Intelligence I will believe all this after you tell me where you got your labels from!
H2 O.ai Machine Intelligence Labeling Different Methods of Labeling 1. Completely Manual 2. Assisted with Feature Growth and Unsupervised
H2 O.ai Machine Intelligence Manual Labeling Logs Information Analytical Inputs: 1. Behavioural Input 2. Univariate Alert score 3. Threat score Suspicious Not Suspicious
H2 O.ai Machine Intelligence Super slow! Enterprise might not exist by the time all traffic gets tagged.
H2 O.ai Machine Intelligence Assisted Labeling H2O Unsupervised Algorithm 1. Features SoC Analyst Clustering Output Sampling Clustering output labeling Clustering Classification Output Logs/Pcap 1. Algo tuning
H2 O.ai Machine Intelligence Model Deployment Information from rule-based system/Raw Packet Capture Not Suspicious H2O Machine Learning Algorithm Suspicious 1. Traffic logs 2. Pcap Info 3. Alert systems
H2 O.ai Machine Intelligence Model Implementation 1. Currently used to assist IDS/IPS systems in decision making. 2. Also used to alert SoC analysts to further investigate the incident.
H2 O.ai Machine Intelligence Pure Data-driven Model Not a suspicious transaction H2O Machine Learning - Complex Algorithm Suspicious Transaction Logs/Information
H2 O.ai Machine Intelligence Thank You Questions?

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Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017