Kapil Ahuja

54 papers A 2Misc 1Journal 47Unranked 4
YearRankTypeTitle / Venue / Authors
2026 J jnl
J. Comput. Sci.
Saurabh Saini, Kapil Ahuja, Marc C. Steinbach, Thomas Wick
2026 J jnl
Inf. Sci.
Amit Kumar Upadhyay, Kapil Ahuja, Ramesh Dharavath
2026 J jnl
CoRR
Kuldeep Pathak, Kapil Ahuja, Eric de Sturler
2025 J jnl
Robotics Auton. Syst.
Amit Raj, Kapil Ahuja, Yann Busnel
2025 J jnl
CoRR
Saurabh Saini, Kapil Ahuja, Marc C. Steinbach, Thomas Wick
2025 J jnl
CoRR
Priyanshu Singh, Kapil Ahuja
2024 J jnl
CoRR
Amit Raj, Kapil Ahuja, Yann Busnel
2024 J jnl
CoRR
Saurabh Saini, Kapil Ahuja, Siddartha Chennareddy, Karthik Boddupalli
2023 J jnl
CoRR
Mithun Singh, Kapil Ahuja, Milind B. Ratnaparkhe
2023 J jnl
IEEE Access
Rohit Agrawal, Kapil Ahuja, Dhaarna Maheshwari, Mohd Ubaid Shaikh, Mohamed Bouaziz, Akash Kumar
2022 J jnl
J. Comput. Appl. Math.
Kapil Ahuja, Bernhard Endtmayer, Marc Christian Steinbach, Thomas Wick
2022 J jnl
PeerJ Comput. Sci.
Rohit Agrawal, Kapil Ahuja, Marc C. Steinbach, Thomas Wick
2021 J jnl
CoRR
Pramod C. Mane, Kapil Ahuja, Pradeep Singh
2021 J jnl
IEEE Trans. Syst. Man Cybern. Syst.
Chandan Gautam, Aruna Tiwari, Suresh Sundaram, Kapil Ahuja
2021 J jnl
IET Image Process.
Rohit Agrawal, Kapil Ahuja
2021 J jnl
CoRR
Rohit Agrawal, Kapil Ahuja
2021 J jnl
CoRR
Seemandhar Jain, Aditya A. Shastri, Kapil Ahuja, Yann Busnel, Navneet Pratap Singh
2021 J jnl
CoRR
Kapil Ahuja, Bernhard Endtmayer, Marc C. Steinbach, Thomas Wick
2021 A conf
ICCAD
Yasasvi V. Peruvemba, Shubham Rai, Kapil Ahuja, Akash Kumar
2021 J jnl
CoRR
Pramod C. Mane, Nagarajan Krishnamurthy, Kapil Ahuja
2021 J jnl
CoRR
Rohit Agrawal, Kapil Ahuja, Marc C. Steinbach, Thomas Wick
2020 J jnl
IEEE Access
Siddharth Gupta, Salim Ullah, Kapil Ahuja, Aruna Tiwari, Akash Kumar
2020 A conf
DATE
Salim Ullah, Siddharth Gupta, Kapil Ahuja, Aruna Tiwari, Akash Kumar
2020 J jnl
CoRR
Rohit Agrawal, Kapil Ahuja, Dhaarna Maheshwari, Akash Kumar
2020 J jnl
Int. J. Comput. Math.
Navneet Pratap Singh, Kapil Ahuja
2020 J jnl
CoRR
Aditya A. Shastri, Kapil Ahuja, Milind B. Ratnaparkhe, Yann Busnel
2020 J jnl
IEEE Access
Navneet Pratap Singh, Kapil Ahuja
2020 J jnl
CoRR
Navneet Pratap Singh, Kapil Ahuja
2020 J jnl
Ann. Oper. Res.
Pramod C. Mane, Kapil Ahuja, Nagarajan Krishnamurthy
2019 J jnl
Games
Pramod C. Mane, Nagarajan Krishnamurthy, Kapil Ahuja
2019 J jnl
IEEE Access
Rajendra Choudhary, Kapil Ahuja
2019 J jnl
Knowl. Based Syst.
Chandan Gautam, Ramesh Balaji, Sudharsan K, Aruna Tiwari, Kapil Ahuja
2018 Misc conf
COMSNETS
Harshit Jain, Guduru Sai Teja, Pramod Mane, Kapil Ahuja, Nagarajan Krishnamurthy
2018 J jnl
Expert Syst. Appl.
Aditya Shastri, Deepti Tamrakar, Kapil Ahuja
2018 J jnl
CoRR
Pramod C. Mane, Kapil Ahuja, Nagarajan Krishnamurthy
2018 J jnl
CoRR
Chandan Gautam, Ramesh Balaji, Sudharsan K, Aruna Tiwari, Kapil Ahuja
2018 J jnl
CoRR
Rohit Agrawal, Chin Hao Hoo, Kapil Ahuja, Akash Kumar
2018 conf
SSCI
Vishal Nemade, Aditya Shastri, Kapil Ahuja, Aruna Tiwari
2018 J jnl
CoRR
Navneet Pratap Singh, Kapil Ahuja
2018 J jnl
CoRR
Aditya A. Shastri, Kapil Ahuja, Milind B. Ratnaparkhe, Aditya Shah, Aishwary Gagrani, Anant Lal
2017 J jnl
CoRR
Harshit Jain, Guduru Sai Teja, Pramod Mane, Kapil Ahuja, Nagarajan Krishnamurthy
2017 J jnl
CoRR
Deepti Tamrakar, Kapil Ahuja
2017 J jnl
CoRR
Chandan Gautam, Aruna Tiwari, Suresh Sundaram, Kapil Ahuja
2016 J jnl
CoRR
Pramod Mane, Kapil Ahuja, Nagarajan Krishnamurthy
2015 J jnl
SIAM J. Sci. Comput.
Kapil Ahuja, Peter Benner, Eric de Sturler, Lihong Feng
2015 J jnl
J. Comput. Phys.
Amit Amritkar, Eric de Sturler, Katarzyna Swirydowicz, Danesh K. Tafti, Kapil Ahuja
2015 J jnl
CoRR
Amit Amritkar, Eric de Sturler, Katarzyna Swirydowicz, Danesh K. Tafti, Kapil Ahuja
2014 conf
GAMENETS
Pramod Mane, Nagarajan Krishnamurthy, Kapil Ahuja
2012 J jnl
SIAM J. Sci. Comput.
Kapil Ahuja, Eric de Sturler, Serkan Gugercin, Eun R. Chang
2011 J jnl
SIAM J. Sci. Comput.
Kapil Ahuja, Bryan K. Clark, Eric de Sturler, David M. Ceperley, Jeongnim Kim
2008 J jnl
Comput. Optim. Appl.
Kapil Ahuja, Layne T. Watson, Stephen C. Billups
2007 J jnl
Bull. IEEE Tech. Comm. Digit. Libr.
Uma Murthy, Kapil Ahuja, Sudarshan Murthy, Edward A. Fox
2006 conf
JCDL
Uma Murthy, Kapil Ahuja, Sudarshan Murthy, Edward A. Fox
2005 conf
ECDL
Seonho Kim, Uma Murthy, Kapil Ahuja, Sandi Vasile, Edward A. Fox
yara/README.md
← Index yara/README.md markdown
# YARA Rules Directory

This folder contains YARA rules for scanning binary samples.

## Setting Up YARA-Forge Rules

To use the YARA-Forge rules from [https://github.com/YARAHQ/yara-forge](https://github.com/YARAHQ/yara-forge):

```bash
# Download the latest release
cd /path/to/redb/yara
# wget https://github.com/YARAHQ/yara-forge/releases/latest/download/yara-forge-rules-core.zip
wget https://github.com/YARAHQ/yara-forge/releases/latest/download/yara-forge-rules-extended.zip

# Extract rules
# unzip yara-forge-rules-core.zip
unzip yara-forge-rules-extended.zip
```

Available packages:
- `yara-forge-rules-core.zip` - Core rules (~5,000 rules)
- `yara-forge-rules-extended.zip` - Extended rules (~10,000 rules)
- `yara-forge-rules-full.zip` - Full rules (~11,000+ rules)

## Pre-compiling Rules (Recommended for Production)

For large rulesets like YARA-Forge, pre-compiling rules significantly improves startup time:

```bash
# Pre-compile all rules into a single .yarac file
python -m redb.extractors.yara --compile

# Or specify custom paths
python -m redb.extractors.yara --compile --rules-path /path/to/rules --output /path/to/output.yarac
```

This creates `yara/compiled_rules.yarac` which is loaded automatically on subsequent runs.

### Performance Comparison

| Method | First Scan Startup | Subsequent Scans |
|--------|-------------------|------------------|
| Source files (.yar) | ~10-30 seconds (11k rules) | Instant (cached) |
| Pre-compiled (.yarac) | ~1-2 seconds | Instant (cached) |

## Directory Structure

```
yara/
├── README.md
├── .gitkeep
├── compiled_rules.yarac    # (optional) Pre-compiled rules
├── packages/               # YARA-Forge packages
│   └── core/
│       └── *.yar
└── custom/                 # Your custom rules
    └── my_rules.yar
```

Rules are loaded in this priority:
1. `compiled_rules.yarac` (if exists) - fastest
2. All `.yar` and `.yara` files recursively - compiles on first run

## Usage

### Scan with YARA only

```bash
# Scan local files
python start.py --path /path/to/samples -y --repo my_repo --index_prefix redb

# Scan S3 samples
python start.py --s3 --repo bazaar -y --index_prefix redb

# Dry-run (print results instead of storing in ClickHouse)
python start.py --path /path/to/samples -y --dry-run --repo test --index_prefix redb
```

### Scan already-analyzed samples

Run YARA on samples that were previously analyzed (already in `basic_properties`).
Deduplication is handled by the `yara_matches` table — samples already scanned are
automatically excluded before processing begins:

```bash
# Scan all analyzed macho samples with YARA
python start.py --analyzed --magika macho -y --index_prefix redb

# Scan all analyzed PE samples with YARA
python start.py --analyzed --magika pe -y --index_prefix redb

# Scan all analyzed samples (no filetype filter)
python start.py --analyzed -y --index_prefix redb
```

### Partition large YARA runs by date

Combine `--analyzed` with `--range` to partition millions of samples into
manageable batches. Only samples in `basic_properties` AND within the date
range (by `first_seen` in `catalog_samples`) are processed:

```bash
# Scan analyzed PE samples from Feb 2025
python start.py --range 2025-02-01 2025-02-28 --analyzed --magika pebin -y --index_prefix redb

# Scan analyzed PE samples from first week of March 2025
python start.py --range 2025-03-01 2025-03-08 --analyzed --magika pebin -y --index_prefix redb
```

YARA dedup still applies — re-running a range safely skips already-scanned samples.

### Combined Features + YARA

Run feature extraction and YARA scanning together on the same samples:

```bash
# Local files with features + YARA
python start.py --path /path/to/samples --with-yara --repo my_repo --index_prefix redb

# S3 samples with features + YARA
python start.py --s3 --repo bazaar --with-yara --index_prefix redb
```

### Pre-compile Rules

```bash
# Compile and save to default location (yara/compiled_rules.yarac)
python -m redb.extractors.yara --compile

# Compile with custom paths
python -m redb.extractors.yara --compile --rules-path ./my_rules --output ./compiled.yarac
```

### Sync Rules to Database

Before batch scanning, sync rules to ensure all rule metadata is stored:

```bash
# Sync rules to database
python -m redb.extractors.yara --sync-rules

# Sync with custom source collection name
python -m redb.extractors.yara --sync-rules --source-collection yara-forge-core

# Compile and sync in one command
python -m redb.extractors.yara --compile --sync-rules
```

## ClickHouse Table Schema

YARA data uses a **normalized schema** with two tables for efficient storage.

### Matches Table: `yara_matches`

Stores one row per sample-rule match (optimized with binary sha256 and rule_id):

| Column | Type | Description |
|--------|------|-------------|
| sha256 | FixedString(32) | Binary SHA256 (32 bytes, use `hex(sha256)` to display) |
| rule_id | UInt64 | Unique rule identifier (xxHash64 of canonical rule content) |
| rule_name | LowCardinality(String) | YARA rule name (denormalized for convenience) |
| scan_date | DateTime64(3, 'UTC') | Scan timestamp |
| match_strings | Array(String) | Matched string identifiers |

### Rules Table: `yara_rules`

Stores rule metadata once per unique rule (deduplicated by rule_id):

| Column | Type | Description |
|--------|------|-------------|
| rule_id | UInt64 | Unique rule identifier (xxHash64 of canonical rule content) |
| rule_name | String | YARA rule name |
| source_collection | LowCardinality(String) | Source collection (e.g., 'yara-forge-core', 'malpedia') |
| ingested_at | DateTime64(3, 'UTC') | When this rule was ingested |
| rule_text | String | Full rule source code |
| rule_meta | JSON | Rule metadata (author, description, reference, etc.) |
| rule_tags | Array(LowCardinality(String)) | Rule tags |

### Schema Benefits

- **Binary SHA256**: 32 bytes vs 64 bytes (50% storage savings on hash columns)
- **UInt64 rule_id**: Fast joins and lookups via integer key
- **Content-based rule_id**: xxHash64 of canonical rule content (excluding metadata) for deduplication
- **Denormalized rule_name**: Allows queries without joins for common use cases

### Example Queries

```sql
-- Get matches with hex sha256
SELECT
    hex(m.sha256) as sha256,
    m.rule_name,
    m.match_strings
FROM yara_matches m
WHERE m.sha256 = unhex('abc123...')

-- Join with rules for full metadata
SELECT
    hex(m.sha256) as sha256,
    m.rule_name,
    m.match_strings,
    r.rule_meta,
    r.source_collection
FROM yara_matches m
JOIN yara_rules r ON m.rule_id = r.rule_id
WHERE m.sha256 = unhex('abc123...')

-- Find all samples matching a specific rule
SELECT hex(sha256), scan_date
FROM yara_matches
WHERE rule_name = 'APT_Lazarus_Loader'
ORDER BY scan_date DESC
```

## Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `YARA_RULES_PATH` | Override the YARA rules directory | `yara/` |
| `YARA_COMPILED_RULES` | Compiled rules filename | `compiled_rules.yarac` |
| `YARA_SOURCE_COLLECTION` | Default source collection name | `default` |