Rmissax Full (2024)

Below we break down what the full mode actually does and why each step is useful. You can either let run_full() do everything automatically, or call the internal functions one‑by‑one for finer control.

rmissax --version
# Expected output, e.g.: rmissax 2.3.1

Performs discovery and vulnerability checks.

# Basic TCP port scan of a CIDR block + banner grab
rmissax scan -t 192.168.1.0/24 -p 1-65535 --plugins portscan,banner
# Full recon: DNS enumeration, SSL cert inspection, CVE lookup
rmissax scan -t example.com \
    --plugins subfinder,crtsh,sslinfo,cve-search \
    --output results.json --format json

| Option | Example | Meaning | |--------|---------|---------| | -t, --targets | -t 10.10.10.0/24,10.10.20.5 | Target IPs or hostnames (comma‑separated). | | -p, --ports | -p 80,443,8080-8090 | Port list/range for the portscan plugin. | | --plugins | --plugins portscan,ssh-brute | Comma‑separated list of plugin identifiers. | | --exclude | --exclude 10.10.10.5 | Omit specific hosts from the scan. | rmissax full

run_full <- function(df,
                     impute_method = "auto",
                     n_imp = 5,
                     seed = NULL,
                     parallel = FALSE,
                     ...)
## 1️⃣ Setup --------------------------------------------------------------
  if (!is.null(seed)) set.seed(seed)
  if (parallel) future::plan(future::multisession)
## 2️⃣ Detect patterns ----------------------------------------------------
  pat <- detect_pattern(df, plot = FALSE)
## 3️⃣ Test missingness mechanisms ----------------------------------------
  mcar_res  <- test_mcar(df)
  mar_res   <- test_mar(df, aux_vars = select_aux(df))
  mnar_res  <- sensitivity_mnar(df)
## 4️⃣ Choose imputation methods -----------------------------------------
  if (identical(impute_method, "auto")) 
    meth_tbl <- select_best_method(df, candidate_methods = c("pmm","rf","knn","norm"))
   else 
    meth_tbl <- impute_method   # assume user supplied a named list/data.frame
## 5️⃣ Multiple imputation ------------------------------------------------
  imp <- impute_multiple(df,
                         method_tbl = meth_tbl,
                         n_imp = n_imp,
                         parallel = parallel,
                         seed = seed)
## 6️⃣ Diagnostics & Plots ------------------------------------------------
  plots <- generate_all_plots(imp, pat)
## 7️⃣ Build the final report ---------------------------------------------
  report_path <- write_report(imp,
                              diagnostics = list(mcar = mcar_res,
                                                 mar = mar_res,
                                                 mnar = mnar_res),
                              plots = plots,
                              output_file = "RmissAX_full_report.html")
## 8️⃣ Return a tidy list --------------------------------------------------
  list(imputed_data = imp$pooled,
       imputed_sets = imp$imputations,
       diagnostics = list(pattern = pat,
                          mcar = mcar_res,
                          mar = mar_res,
                          mnar = mnar_res,
                          imputation = imp$diagnostics),
       plots = plots,
       report = report_path)

Tip: The skeleton above is publicly available in the source (RmissAX:::run_full). Feel free to copy‑paste and adapt it for batch processing or integration with CI pipelines.

| Concern | Recommendation | |---------|----------------| | Legal Authorization | Always obtain written permission before scanning or exploiting any system. | | Rate Limiting | Use --threads and --timeout to throttle aggressive plugins (e.g., masscan). | | Logging | Enable verbose logging (-v -v) and store logs (--log-file rmissax.log) for audit trails. | | Output Sanitization | When exporting to HTML, ensure the template engine escapes user‑provided data to avoid XSS. | | Dependency Updates | Periodically run pip list --outdated and pip install --upgrade rmissax to patch any third‑party library issues. | | Containerization | Running rmissax inside a Docker container (docker run -v $(pwd):/data securelab/rmissax ...) isolates network activity and eases reproducibility. | Below we break down what the full mode

| Resource | URL | |----------|-----| | GitHub Repository | https://github.com/securelab/rmissax | | Issue Tracker | https://github.com/securelab/rmissax/issues | | Documentation Site | https://rmissax.readthedocs.io | | Discord Community | https://discord.gg/rmissax | | Plugin Marketplace | https://rmissax.io/plugins (community‑contributed plugins) |

Contributions are welcomed via pull requests. The project follows a Contributor Covenant code of conduct. Performs discovery and vulnerability checks

rmissax (pronounced “R‑Miss‑A‑X”) is an open‑source reconnaissance and exploitation framework aimed at automating the discovery of misconfigurations, insecure services, and vulnerable endpoints on remote hosts. It combines a modular architecture with a set of built‑in scanners and payload generators, making it useful for penetration testers, red‑team operators, and security researchers who need a fast, scriptable way to enumerate and test a target network.

Key goals of rmissax

Feature Name: Smart Imputation with rmissax

Description: The rmissax package aims to provide comprehensive tools for handling missing values in R. A key feature of this package could be an enhanced, intelligent imputation function that automatically selects the most suitable imputation method based on the data type and distribution of the variables. This feature, named "Smart Imputation," seeks to simplify and streamline the process of dealing with missing values, making it more efficient and accurate.