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zq command line utility function
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README.md

Project Status: Active – The project has reached a stable, usablestate and is being activelydeveloped. Signedby Signed commit% Linux buildStatus
Minimal RVersion License

brimr

Tools to Work with Brim and zqd

Description

Brim (https://github.com/brimsec/brim) enables efficient query operations on large packet captures and log sources, such as Zeek. Tools are provided to with with Brim components, including the Brim zqd query back-end.

What’s Inside The Tin

The following functions are implemented:

  • brim_ast: Turn a Brim ZQL query into an abstract syntax tree
  • brim_host: Retrieve the Brim host URL
  • brim_search_raw: Post a ZQL query to the given Brim instance and retrieve results in raq ZJSON format
  • brim_search: Post a ZQL query to the given Brim instance and retrieve processed results
  • brim_spaces: Retrieve active Brim spaces from the specified Brim instance
  • tidy_brim: Turn Brim/zqd search results into a data frame
  • zq_cmd: Execute a zq command line

Installation

remotes::install_git("https://git.rud.is/hrbrmstr/brimr.git")
# or
remotes::install_gitlab("hrbrmstr/brimr")
# or
remotes::install_bitbucket("hrbrmstr/brimr")
# or
remotes::install_github("hrbrmstr/brimr")

NOTE: To use the ‘remotes’ install options you will need to have the {remotes} package installed.

Usage

library(brimr)
library(tibble)

# current version
packageVersion("brimr")
## [1] '0.1.0'

Available Brim “spaces”

brim_spaces()
##                               id                                                            name
## 1 sp_1p6pwLgtsESYBTHU9PL9fcl2iBn 2021-02-17-Trickbot-gtag-rob13-infection-in-AD-environment.pcap
##                                                                                              data_path storage_kind
## 1 file:///Users/hrbrmstr/Library/Application%20Support/Brim/data/spaces/sp_1p6pwLgtsESYBTHU9PL9fcl2iBn    filestore

Sample ZQL query

# Z query to fetch Zeek connection data to create our network connection graph
zql1 <- '_path=conn | count() by id.orig_h, id.resp_h, id.resp_p | sort id.orig_h, id.resp_h, id.resp_p'

cat(
  substr(jsonlite::toJSON(jsonlite::fromJSON(brim_ast(zql1)), pretty = TRUE), 1, 100), "..."
)
## {
##   "op": ["SequentialProc"],
##   "procs": [
##     {
##       "op": "FilterProc",
##       "filter": {
##         ...

Let’s execute the query

space <- "2021-02-17-Trickbot-gtag-rob13-infection-in-AD-environment.pcap"

r1 <- brim_search(space, zql1)

r1
## ZQL query took 0.0000 seconds; 384 records matched; 1,082 records read; 238,052 bytes read

(r1 <- as_tibble(tidy_brim(r1)))
## # A tibble: 74 x 4
##    orig_h      resp_h       resp_p count
##    <chr>       <chr>        <chr>  <int>
##  1 10.2.17.2   10.2.17.101  49787      1
##  2 10.2.17.101 3.222.126.94 80         1
##  3 10.2.17.101 10.2.17.1    445        1
##  4 10.2.17.101 10.2.17.2    53        97
##  5 10.2.17.101 10.2.17.2    88        27
##  6 10.2.17.101 10.2.17.2    123        5
##  7 10.2.17.101 10.2.17.2    135        8
##  8 10.2.17.101 10.2.17.2    137        2
##  9 10.2.17.101 10.2.17.2    138        2
## 10 10.2.17.101 10.2.17.2    389       37
## # … with 64 more rows

Let’s try one that processes the Suricata alerts

# Z query to fetch Suricata alerts including the count of alerts per source:destination 
zql2 <- "event_type=alert | count() by src_ip, dest_ip, dest_port, alert.severity, alert.signature | sort src_ip, dest_ip, dest_port, alert.severity, alert.signature"

r2 <- brim_search(space, zql2)

r2
## ZQL query took 0.0000 seconds; 47 records matched; 870 records read; 238,660 bytes read

(r2 <- (as_tibble(tidy_brim(r2))))
## # A tibble: 35 x 6
##    src_ip     dest_ip    dest_port severity signature                                                              count
##    <chr>      <chr>          <int>    <int> <chr>                                                                  <int>
##  1 10.2.17.2  10.2.17.1…     49674        3 SURICATA Applayer Detect protocol only one direction                       1
##  2 10.2.17.2  10.2.17.1…     49680        3 SURICATA Applayer Detect protocol only one direction                       1
##  3 10.2.17.2  10.2.17.1…     49687        3 SURICATA Applayer Detect protocol only one direction                       1
##  4 10.2.17.2  10.2.17.1…     49704        3 SURICATA Applayer Detect protocol only one direction                       1
##  5 10.2.17.2  10.2.17.1…     49709        3 SURICATA Applayer Detect protocol only one direction                       1
##  6 10.2.17.2  10.2.17.1…     49721        3 SURICATA Applayer Detect protocol only one direction                       1
##  7 10.2.17.2  10.2.17.1…     50126        3 SURICATA Applayer Detect protocol only one direction                       1
##  8 10.2.17.1… 3.222.126…        80        2 ET POLICY curl User-Agent Outbound                                         1
##  9 10.2.17.1… 36.95.27.…       443        1 ET HUNTING Suspicious POST with Common Windows Process Names - Possib…     1
## 10 10.2.17.1… 36.95.27.…       443        1 ET MALWARE Win32/Trickbot Data Exfiltration                                1
## # … with 25 more rows
library(igraph)
library(ggraph)
library(tidyverse)

gdf <- count(r1, orig_h, resp_h, wt=count)

count(gdf, node = resp_h, wt=n, name = "in_degree") %>% 
  full_join(
    count(gdf, node = orig_h, name = "out_degree")
  ) %>% 
  mutate_at(
    vars(in_degree, out_degree),
    replace_na, 1
  ) %>% 
  arrange(in_degree) -> vdf

g <- graph_from_data_frame(gdf, vertices = vdf)

ggraph(g, layout = "linear") +
  geom_node_point(
    aes(size = in_degree), shape = 21
  ) +
  geom_edge_arc(
    width = 0.125, 
    arrow = arrow(
      length = unit(5, "pt"),
      type = "closed"
    )
  )

Use zq directly

zq_cmd(
  c(
    '"* | cut ts,id.orig_h,id.orig_p"', # note the quotes
    system.file("logs", "conn.log.gz", package = "brimr")
   )
 )
##           id.orig_h id.orig_p                          ts
##   1:  10.164.94.120     39681 2018-03-24T17:15:21.255387Z
##   2:    10.47.25.80     50817 2018-03-24T17:15:21.411148Z
##   3:    10.47.25.80     50817 2018-03-24T17:15:21.926018Z
##   4:    10.47.25.80     50813 2018-03-24T17:15:22.690601Z
##   5:    10.47.25.80     50813 2018-03-24T17:15:23.205187Z
##  ---                                                     
## 988: 10.174.251.215     33003 2018-03-24T17:15:21.429238Z
## 989: 10.174.251.215     33003 2018-03-24T17:15:21.429315Z
## 990: 10.174.251.215     33003 2018-03-24T17:15:21.429479Z
## 991:  10.164.94.120     38265 2018-03-24T17:15:21.427375Z
## 992: 10.174.251.215     33003 2018-03-24T17:15:21.433306Z

brimr Metrics

Lang # Files (%) LoC (%) Blank lines (%) # Lines (%)
R 5 0.42 180 0.39 71 0.33 86 0.32
Rmd 1 0.08 53 0.11 37 0.17 47 0.18
SUM 6 0.50 233 0.50 108 0.50 133 0.50

clock Package Metrics for brimr

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.