UTLParser

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Towards Unified Semantic Log Parsing Framework for Temporal Causal Graph Construction

Features

• correlate data from multiple sources (network traffic, system/applications/service logs, process execution status)
• automatically recognize log format, and calculate depth and similarity threshold
• extract the entities (obj, sub, action) with depedency relationships from events (both structured and unstructured logs)
• provenance graph construction from multi-source logs
• measure the delay for log fusion
• interfaces for optimized temporal graph query and graph community detection

Structure

• core:

  • entity_reco: custom entity extraction from unifited output

  • graph_create: the module block to build causal graphs

  • graph_label: labelling temporal graph

  • logparse: multiple log parsers

  • pattern: the rule to build unifited output and graph

• eval: benchmark testing

• eval_data: the code to generate evaluation data

• src: the running main interface

• unit_test: the unit testing for core modules

• utils: util functions to support processing

• config: the config file including regexes, defined poi, etc

Running

• preprepration

# avoid python version conflict --- pyenv
brew install pyenv-virtualenv
brew install pyenv
pyenv install 3.10
pyenv global 3.10
pyenv virtualenv 3.10 UTLParser
# activate the environment
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"
pyenv local UTLParser
pyenv activate UTLParser
pip3 install -r requirements.txt
# download large language library
python -m spacy download en_core_web_lg

• how to use

# single log source processing
python3 main.py -a dns -i /xxx/UTLParser/unit_test/data/dns.log

# multiple log sources processing --- fused graph
python3 main.py -f True -al 'dns,error,access,audit'

# temporal graph query
python3 main.py -al 'dns,error,access,audit' -t "2022-Jan-15 10:17:01.246000"

# assign labels to fused graphs
python3 main.py -l True 

• custom running

  • add poi and iocs for custom logs inside config.py
  • repeat above steps

Output Format

• IOCs:

  Timestamp, Src_IP, Dst_IP, Proto or Application, Domain, PacketSize, ParaPair (tuple)

Explaination of Dataset

• AIT (fox) --- pure unstructured logs:

  • used for intrusion detection systems, federated learning, alert aggregation

  • include logs from all hosts, apache, error, authentication, DNS/VPN, audit, network traffic, syslog, system monitoring logs

  • ground truth labels for events

  • details:

    • host log: gather/ host name / logs
    • labels directory: labelling information
    • rules directory: how the labels are assigned

  • launched attacks:

    • Scans
    • Webshell upload --- apache
    • password cracking
    • privilege escalation --- dnsmasq, apache, audit (internal_server), system.cpu
    • remote command execution --- dnsmasq,apache, audit (internal_server), system.cpu
    • data exfiltration --- dnsmasq, audit (internal_share),

• Sysdig Process:

  # follow the format like: evt.num, evt.time, evt.cpu, proc.name, thread.tid, evt.dir, evt.type, evt.args
  - 123 23:40:09.105899621 3 httpd (28599) > switch next=0 pgft_maj=3 pgft_min=619 vm_size=442720 vm_rss=668 vm_swap=7004
  

• IoT23 (structured logs) --- network traffic:

  • label information

    • attack (part of APT): indictors that there was some type of attack from the infected device to another host
    • C & C (part of APT): the infected device was connnected to a CC server
    • DDoS: ddos attack is being executed by the infected device
    • FileDownload (part of APT): a file is being downloaded to the infected device
    • HeartBeat (periodic similar connections) packets sent on this connection are used to keep a track on the infected host
    • Mirai (botnet) similar patterns
    • Okiru (botnet) same parameters
    • PortScan (part of APT)
    • Torii (botnet) same parameters

  • related field and its number

    • id.resp_h (5) ----> C & C
    • id.resp_p (6) ----> Malware, HeartBeat, Port Scan
    • conn_state (12) ----> Port Scan

  • choosen fields to extract features

    • ts? -- time series --- dynamic beyasian network
    • id.orig_h, id.orig_p, id.resp_h, id.resp_p
    • resp_bytes ---- filedownload
    • conn_state ---- port scan
    • feature analysis? --- other features

Next Plan

• Build Temporal Graph Neural Networks

  • reduce the graph size to some extent: suitable for low-memory cost training
  • capable of process heterogeneous graph attributes
  • capable of capture the changes between temporal graphs
  • capable of measuring normal and abnormal behaviour in unsupervised way