Kalpana Mahalingam

68 papers B 3C 6Journal 44Unranked 14
YearRankTypeTitle / Venue / Authors
2026 J jnl
Adv. Appl. Math.
Kalpana Mahalingam
2025 J jnl
Nat. Comput.
Kalpana Mahalingam
2025 J jnl
Discret. Math. Theor. Comput. Sci.
Kalpana Mahalingam, Anuran Maity
2023 J jnl
Discret. Appl. Math.
Kalpana Mahalingam, Anuran Maity, Palak Pandoh
2022 J jnl
Inf. Comput.
Kalpana Mahalingam, Anuran Maity, Palak Pandoh
2022 J jnl
Inf. Comput.
Kalpana Mahalingam, Palak Pandoh
2022 J jnl
Int. J. Found. Comput. Sci.
Kalpana Mahalingam, Palak Pandoh
2022 J jnl
J. Autom. Lang. Comb.
Lila Kari, Kalpana Mahalingam, Palak Pandoh, Zihao Wang
2022 J jnl
Discret. Appl. Math.
Kalpana Mahalingam, Helda Princy Rajendran
2022 J jnl
Comput. J.
Ujjwal Kumar Mishra, Kalpana Mahalingam, Raghavan Rama
2022 conf
CAI
Lila Kari, Kalpana Mahalingam
2022 J jnl
CoRR
Kalpana Mahalingam, Anuran Maity
2021 J jnl
Theor. Comput. Sci.
Kalpana Mahalingam, Anuran Maity, Palak Pandoh, Rama Raghavan
2021 J jnl
CoRR
Kalpana Mahalingam, Palak Pandoh
2021 J jnl
CoRR
Ujjwal Kumar Mishra, Kalpana Mahalingam, Rama Raghavan
2021 J jnl
CoRR
Ujjwal Kumar Mishra, Kalpana Mahalingam, Rama Raghavan
2021 J jnl
J. Autom. Lang. Comb.
Lila Kari, Manasi S. Kulkarni, Kalpana Mahalingam, Zihao Wang
2021 J jnl
SN Comput. Sci.
Kalpana Mahalingam, Prithwineel Paul
2021 J jnl
AKCE Int. J. Graphs Comb.
Kalpana Mahalingam, Helda Princy Rajendran
2020 J jnl
Fundam. Informaticae
Somnath Bera, Kalpana Mahalingam
2020 J jnl
J. Autom. Lang. Comb.
Kalpana Mahalingam, Prithwineel Paul
2020 J jnl
Theor. Comput. Sci.
Kalpana Mahalingam, Palak Pandoh, Kamala Krithivasan
2020 J jnl
Int. J. Found. Comput. Sci.
Somnath Bera, Rodica Ceterchi, Kalpana Mahalingam, K. G. Subramanian
2020 conf
TPNC
Kalpana Mahalingam, Palak Pandoh, Anuran Maity
2020 J jnl
CoRR
Kalpana Mahalingam, Palak Pandoh, Anuran Maity
2020 J jnl
Int. J. Found. Comput. Sci.
Kalpana Mahalingam, Ujjwal Kumar Mishra, Rama Raghavan
2019 C conf
TAMC
Manasi S. Kulkarni, Kalpana Mahalingam, Sivasankar Mohankumar
2019 conf
CALDAM
Kalpana Mahalingam, Helda Princy Rajendran
2019 C conf
LATA
Kalpana Mahalingam, Palak Pandoh
2019 conf
TPNC
Kalpana Mahalingam, Hirapra Ravi
2019 J jnl
CoRR
Kalpana Mahalingam, Palak Pandoh
2019 C conf
TAMC
Kalpana Mahalingam, Rama Raghavan, Ujjwal Kumar Mishra
2018 J jnl
Iran J. Comput. Sci.
Williams Sureshkumar, Kalpana Mahalingam, Raghavan Rama
2018 J jnl
Acta Cybern.
Kalpana Mahalingam, Prithwineel Paul, Erkki Mäkinen
2018 J jnl
Int. J. Found. Comput. Sci.
Somnath Bera, Kalpana Mahalingam, K. G. Subramanian
2018 J jnl
J. Autom. Lang. Comb.
Lila Kari, Manasi S. Kulkarni, Kalpana Mahalingam
2017 conf
BIC-TA
Kalpana Mahalingam, Prithwineel Paul, Bosheng Song, Linqiang Pan, K. G. Subramanian
2017 J jnl
CoRR
Williams Sureshkumar, Kalpana Mahalingam, Raghavan Rama
2017 C conf
LATA
Manasi S. Kulkarni, Kalpana Mahalingam
2017 conf
TPNC
Manasi S. Kulkarni, Kalpana Mahalingam, Ananda Chandra Nayak
2016 J jnl
Int. J. Found. Comput. Sci.
Somnath Bera, Kalpana Mahalingam
2016 J jnl
Math. Comput. Sci.
Somnath Bera, Kalpana Mahalingam
2015 conf
Int. Conf. on Membrane Computing
Williams Sureshkumar, Kalpana Mahalingam, Raghavan Rama
2014 conf
Discrete Mathematics and Computer Science
Adrian Atanasiu, Kalpana Mahalingam, K. G. Subramanian
2013 J jnl
Int. J. Found. Comput. Sci.
K. G. Subramanian, Kalpana Mahalingam, Rosni Abdullah, Atulya K. Nagar
2012 conf
TPNC
Daniela Genova, Kalpana Mahalingam
2012 J jnl
Int. J. Found. Comput. Sci.
Kalpana Mahalingam, K. G. Subramanian
2012 J jnl
Theor. Comput. Sci.
Mark Daley, Helmut Jürgensen, Lila Kari, Kalpana Mahalingam
2011 C conf
IWCIA
K. G. Subramanian, Kalpana Mahalingam, Rosni Abdullah, Atulya K. Nagar
2011 conf
BIC-TA
K. G. Subramanian, Ibrahim Venkat, Kalpana Mahalingam
2011 J jnl
Nat. Comput.
Mark Daley, Ian McQuillan, James M. McQuillan, Kalpana Mahalingam
2010 J jnl
Nat. Comput.
Mark Daley, Kalpana Mahalingam
2010 conf
BIC-TA
Kalpana Mahalingam, K. G. Subramanian
2010 J jnl
Nat. Comput.
Lila Kari, Kalpana Mahalingam
2009 J jnl
Theor. Comput. Sci.
Lila Kari, Kalpana Mahalingam, Shinnosuke Seki
2008 J jnl
Fundam. Informaticae
Natasa Jonoska, Lila Kari, Kalpana Mahalingam
2008 J jnl
Int. J. Found. Comput. Sci.
Lila Kari, Kalpana Mahalingam
2007 J jnl
Int. J. Found. Comput. Sci.
Lila Kari, Kalpana Mahalingam
2007 J jnl
Acta Informatica
Lila Kari, Kalpana Mahalingam, Gabriel Thierrin
2007 B conf
DNA
Lila Kari, Kalpana Mahalingam
2006 B conf
DNA
Lila Kari, Kalpana Mahalingam
2006 conf
Nanotechnology: Science and Computation
Lila Kari, Kalpana Mahalingam
2006 C conf
Developments in Language Theory
Natasa Jonoska, Lila Kari, Kalpana Mahalingam
2005 J jnl
Nat. Comput.
Natasa Jonoska, Kalpana Mahalingam, Junghuei Chen
2004
Kalpana Mahalingam
2004 conf
Aspects of Molecular Computing
Natasa Jonoska, Kalpana Mahalingam
2003 B conf
DNA
Natasa Jonoska, Kalpana Mahalingam
2002 conf
GECCO Late Breaking Papers
Natasa Jonoska, David Kephart, Kalpana Mahalingam
README.md
← Index README.md markdown
# redb
RationalEdge Samples DB

A malware analysis framework that extracts features from binary files (PE, ELF, Mach-O, APK) and stores them in ClickHouse for analysis.

## Quick Start

```bash
# Setup
source venv/bin/activate
pip install -r requirements.txt

# Process local files
python start.py --path /path/to/samples --repo test --index_prefix redb
```

## Usage Modes

### Local Mode
Process files from local filesystem:

```bash
# Single file or directory
python start.py --path /path/to/binary --repo test --index_prefix redb

# From a text file with paths (one per line)
python start.py --path /path/to/filelist.txt --repo test --index_prefix redb
```

### S3 Mode
Process samples from S3 storage based on catalog queries:

```bash
# By repository
python start.py --s3 --repo bazaar --index_prefix redb

# By repository with notes filter
python start.py --s3 --repo vx-itw --s3-notes "ITW.0138" --index_prefix redb

# By filetype (magika) - all ELF samples across all repos
python start.py --s3 --magika elf --index_prefix redb

# By filetype with repository filter
python start.py --s3 --repo bazaar --magika elf --index_prefix redb
```

### Date-Based Mode
Process samples by first_seen date from catalog:

```bash
# Single date (all samples first seen on Jan 15, 2025)
python start.py --date 2025-01-15 --index_prefix redb

# Date with repository filter
python start.py --date 2025-01-15 --repo bazaar --index_prefix redb

# Date range (inclusive)
python start.py --range 2025-01-01 2025-01-31 --index_prefix redb

# Date range with repository and notes filters
python start.py --range 2025-01-01 2025-01-31 --repo malshare --s3-notes "batch1" --index_prefix redb

# Date range with filetype filter
python start.py --range 2025-01-01 2025-01-31 --magika pebin --index_prefix redb
```

### S3-Solo Mode
Process a single sample by S3 key:

```bash
python start.py --s3-solo "09/f7/09f7d02a...hash.zip" --index_prefix redb
```

## Analysis Options

### Feature Extraction (default)
Runs all extractors to extract features from binaries:

```bash
python start.py --s3 --repo bazaar --index_prefix redb
```

### Specific Modules
Run only specific extractors:

```bash
python start.py --path /path/to/binary --repo test --index_prefix redb \
    --modules "BasicPropertiesExtractor,PEFeaturesExtractor,HashExtractor"
```

Available modules:
- **General**: `BasicPropertiesExtractor`, `HashExtractor`, `DIEExtractor`, `CAPAExtractor`
- **PE**: `PEFeaturesExtractor`, `PEImportExtractor`, `PEResourceExtractor`, `PEOverlayExtractor`, `PESectionExtractor`, `PESignatureExtractor`, `PEDotNetExtractor`, `PEInconstistencyTestsExtractor`, `PEExtraFindings`
- **ELF**: `ELFFeaturesExtractor`, `ELFSegmentExtractor`, `ELFSectionExtractor`, `ELFDependencyExtractor`, `ELFSymbolExtractor`, `ELFImportExtractor`, `ELFExportExtractor`, `ELFRelocationExtractor`, `ELFNotesExtractor`
- **Mach-O**: `MachOFeaturesExtractor`, `MachOSegmentExtractor`, `MachOImportExtractor`, `MachOExportExtractor`, `MachODylibExtractor`, `MachOSignatureExtractor`, `MachOSimilarityHashExtractor`
- **APK**: `APKFeaturesExtractor`, `APKManifestExtractor`, `APKPermissionsExtractor`, `APKSignatureExtractor`, `APKDexExtractor`, `APKResourceExtractor`, `APKNativeLibExtractor`, `APKInconsistencyTestsExtractor`
- **JavaScript**: `JSFeaturesExtractor`, `JSSuspiciousAPIsExtractor`, `JSStringsExtractor`, `JSDeobfuscationExtractor`, `JSContentExtractor`

**Note:** Using `--modules` with specific extractors respects the normal deduplication check. Add `--force` to reprocess samples already in the database.

### Analyzed Samples Mode
Process samples that are already in the database (from `basic_properties`). Useful for decompiling or re-running specific modules on previously analyzed samples:

```bash
# Decompile all already-analyzed samples that haven't been disassembled yet
python start.py --analyzed --index_prefix redb --decompile

# Decompile only ELF samples that were already analyzed
python start.py --analyzed --magika elf --index_prefix redb --decompile

# Re-run a specific extractor on already-analyzed samples
python start.py --analyzed --index_prefix redb --modules "MachOFeaturesExtractor"

# Force decompile ALL analyzed samples (even already-disassembled ones)
python start.py --analyzed --index_prefix redb --decompile --force

# Re-run a specific decompiler module on only already-disassembled samples
python start.py --analyzed --index_prefix redb --decompile --rerun --decompile-modules cfg
```

When combined with `--decompile`, the `--analyzed` flag has three behaviors:

| Flags | Source | Description |
|-------|--------|-------------|
| `--analyzed --decompile` | `basic_properties` minus `disassembled` | New samples only (first-time decompilation) |
| `--analyzed --decompile --force` | All of `basic_properties` | Re-run everything from scratch (e.g., new binja version) |
| `--analyzed --decompile --rerun` | Only `disassembled` table | Re-run on already-disassembled samples only (e.g., updated CFG module) |

The `--rerun` flag is particularly useful with `--decompile-modules` to selectively re-run a single module without reprocessing the full pipeline.

### Force Reprocessing
By default, samples already in the database are skipped. Use `--force` to reprocess them:

```bash
# Force full reprocessing of all samples
python start.py --s3 --repo bazaar --index_prefix redb --force

# Re-run a specific extractor on already-processed samples
python start.py --s3 --repo bazaar --index_prefix redb --modules "MachOFeaturesExtractor" --force

# Force YARA rescan (e.g., after updating rules)
python start.py --s3 --magika elf --index_prefix redb --yara --force
```

`--force` works across all modes: feature extraction, decompilation, and YARA scanning. ReplacingMergeTree handles deduplication, so reprocessed data cleanly replaces existing rows.

### Decompilation Mode
Run Binary Ninja decompilation only:

```bash
python start.py --s3 --repo bazaar --index_prefix redb --decompile
```

#### Selective Decompiler Modules
Run only specific decompiler sub-modules instead of the full pipeline:

```bash
# Run only strings extraction (fastest - skips per-function analysis)
python start.py --s3 --repo bazaar --index_prefix redb --decompile --decompile-modules strings

# Run disassembly and CFG analysis only
python start.py --s3 --repo bazaar --index_prefix redb --decompile --decompile-modules disassembly,cfg

# Run multiple modules
python start.py --s3 --repo bazaar --index_prefix redb --decompile --decompile-modules decompilation,disassembly,llil
```

Available decompiler modules:
- **decompilation** — High-level IL (HLIL) decompiled output → `code_binja_decompiled_functions_*` tables
- **disassembly** — Low-level assembly representation → `code_binja_disassembled_functions_*` tables
- **cfg** — Control flow graph analysis → `code_binja_cfg_functions` table
- **llil** — Low-level intermediate language → `code_binja_llil_functions_*` tables
- **strings** — Binary string extraction → `code_binja_strings_raw` table

**IOC extraction** runs automatically when `decompilation` or `strings` is selected (it consumes their in-memory results). It is skipped for modules like `cfg` or `disassembly` that don't produce IOC-relevant data.

Default is `all` (runs every module). Requires `-d/--decompile` flag.

### YARA Scanning
Run YARA rules against samples:

```bash
# YARA scanning only (skips already-scanned samples by default)
python start.py --s3 --magika elf --index_prefix redb --yara

# Force rescan all samples (e.g., after updating YARA rules)
python start.py --s3 --magika elf --index_prefix redb --yara --force

# Feature extraction + YARA scanning combined
python start.py --s3 --repo bazaar --index_prefix redb --with-yara
```

By default, `--yara` skips samples that already have matches in the `yara_matches` table. Use `--force` to rescan everything (e.g., after updating YARA rules).

### Dry Run Mode
Print results instead of uploading to database:

```bash
python start.py --path /path/to/binary --repo test --index_prefix redb --dry-run
```

## Environment Variables

See `.env.example` for all configuration options:

| Variable | Description |
|----------|-------------|
| `CLICKHOUSE_HOST` | ClickHouse server host |
| `CLICKHOUSE_PORT` | ClickHouse server port (default: 8123) |
| `CLICKHOUSE_USER` | ClickHouse username |
| `CLICKHOUSE_PASSWORD` | ClickHouse password |
| `S3_ENDPOINT` | S3/MinIO endpoint |
| `S3_ACCESS_KEY` | S3 access key |
| `S3_SECRET_KEY` | S3 secret key |
| `S3_BUCKET` | S3 bucket name |
| `INDEX_PREFIX` | Table prefix for ClickHouse (default: redb) |
| `SUPPORTED_FORMATS` | File formats to query (default: `['pebin']`) |
| `BATCH_SIZE` | Files per batch (default: 1000) |
| `REDB_TIMEOUT` | Worker timeout in seconds (default: 600) |
| `DECOMPILE_WORKER_TIMEOUT` | Decompile timeout (default: 2700) |

## Filtering Options Summary

| Option | Description | Standalone | With --repo | With --date/--range |
|--------|-------------|------------|-------------|---------------------|
| `--repo` | Filter by repository | Required for --s3 (unless --magika) | - | Optional |
| `--s3-notes` | Filter by notes field | No | Yes | Yes |
| `--magika` | Filter by filetype | Yes (queries all repos) | Yes | Yes |
| `--date` | Filter by single date | Yes | Yes | - |
| `--range` | Filter by date range | Yes | Yes | - |
| `--analyzed` | Process already-analyzed samples | Yes | N/A | N/A |