Vassilios Petridis

55 papers A* 3A 4B 8Misc 1Journal 34Unranked 4
YearRankTypeTitle / Venue / Authors
2016 J jnl
CoRR
Nikos Zikos, Vassilios Petridis
2015 J jnl
J. Intell. Robotic Syst.
Nikos Zikos, Vassilios Petridis
2015 J jnl
Eng. Appl. Artif. Intell.
Vasilis G. Giannoglou, Dimitris G. Stavrakoudis, John B. Theocharis, Vassilios Petridis
2012 B conf
FUZZ-IEEE
Vasilis G. Giannoglou, Dimitris G. Stavrakoudis, John B. Theocharis, Vassilios Petridis
2012 J jnl
IEEE Trans. Geosci. Remote. Sens.
Serafeim P. Moustakidis, Giorgos Mallinis, Nikos Koutsias, John B. Theocharis, Vassilios Petridis
2011 J jnl
Inf. Sci.
Vassilis Syrris, Vassilios Petridis
2011 A* conf
ICRA
Nikos Zikos, Vassilios Petridis
2011 J jnl
Expert Syst. Appl.
Charalampos A. Dimoulas, George Papanikolaou, Vassilios Petridis
2010 B conf
IJCNN
Vassilis Syrris, Vassilios Petridis
2010 B conf
IJCNN
Vassilios Petridis, Nikos Zikos
2008 Misc conf
ICASSP
Iordanis Mpiperis, Sotiris Malassiotis, Vassilios Petridis, Michael G. Strintzis
2008 J jnl
Fuzzy Sets Syst.
Nikolaos E. Mitrakis, John B. Theocharis, Vassilios Petridis
2008 J jnl
Expert Syst. Appl.
Charalampos Dimoulas, George Kalliris, George Papanikolaou, Vassilios Petridis, Antonios Kalampakas
2008 B conf
IJCNN
Vassilis Syrris, Vassilios Petridis
2007 ch.
Computational Intelligence Based on Lattice Theory
Vassilios Petridis, Vassilis Syrris
2007 conf
ICTAI (2)
Pavlina Fragkou, Vassilios Petridis
2006 B conf
FUZZ-IEEE
Vassilios Petridis, John B. Theocharis, Vassilis Syrris
2006 B conf
IJCNN
Vassilios Petridis, Stavros Petridis
2006 B conf
IJCNN
Anastasios-Antonios Toulkeridis, Vassilios Petridis
2005 B conf
IJCNN
Vassilios Petridis, Anastasios-Antonios Toulkeridis
2004 J jnl
J. Intell. Inf. Syst.
Pavlina Fragkou, Vassilios Petridis, Athanasios Kehagias
2003 J jnl
J. Intell. Inf. Syst.
Athanasios Kehagias, Vassilios Petridis, Vassilis G. Kaburlasos, Pavlina Fragkou
2003 J jnl
IEEE Trans. Educ.
Vassilios Petridis, Spiridon A. Kazarlis, Vassilis G. Kaburlasos
2003 J jnl
J. Mach. Learn. Res.
Vassilios Petridis, Vassilis G. Kaburlasos
2003 A conf
EACL
Athanasios Kehagias, Pavlina Fragkou, Vassilios Petridis
2002 J jnl
Math. Comput. Simul.
Vassilis G. Kaburlasos, Vasilis A. Spais, Vassilios Petridis, Loukas Petrou, Spiridon A. Kazarlis, N. Maslaris, A. Kallinakis
2002 A conf
PPSN
Panagiotis Adamidis, Vassilios Petridis
2002 J jnl
IEEE Trans. Neural Networks
Athanasios Kehagias, Vassilios Petridis
2001 J jnl
J. Intell. Robotic Syst.
Vassilios Petridis, Athanasios Kehagias, Loukas Petrou, Anastasios G. Bakirtzis, S. Kiartzis, H. Panagiotou, N. Maslaris
2001 J jnl
Fuzzy Sets Syst.
Paris A. Mastorocostas, Ioannis B. Theocharis, Vassilios Petridis
2001 J jnl
IEEE Trans. Knowl. Data Eng.
Vassilios Petridis, Vassilis G. Kaburlasos
2001 J jnl
IEEE Trans. Evol. Comput.
Spiridon A. Kazarlis, Stelios E. Papadakis, Ioannis B. Theocharis, Vassilios Petridis
2000 J jnl
Neural Networks
Vassilis G. Kaburlasos, Vassilios Petridis
1999 J jnl
IEEE Trans. Inf. Technol. Biomed.
Vassilis G. Kaburlasos, Vassilios Petridis, Peter N. Brett, Dave A. Baker
1999 J jnl
IEEE Trans. Fuzzy Syst.
Vassilios Petridis, Vassilis G. Kaburlasos
1998 J jnl
Autom.
Vassilios Petridis, Athanasios Kehagias
1998 J jnl
IEEE Trans. Neural Networks
Vassilios Petridis, E. Paterakis, Athanasios Kehagias
1998 J jnl
IEEE Trans. Neural Networks
Vassilios Petridis, Vassilis G. Kaburlasos
1998 A conf
PPSN
E. Paterakis, Vassilios Petridis, Athanasios Kehagias
1998 A* conf
ICRA
Vassilis G. Kaburlasos, Vassilios Petridis, Peter N. Brett, Dave A. Baker
1998 J jnl
Int. J. Comput. Math.
Panagiotis Adamidis, Vassilios Petridis
1998 A conf
PPSN
Spiridon A. Kazarlis, Vassilios Petridis
1998 J jnl
IEEE Trans. Syst. Man Cybern. Part B
Vassilios Petridis, Spiridon A. Kazarlis, Anastasios G. Bakirtzis
1997 J jnl
Neural Networks
Athanasios Kehagias, Vassilios Petridis
1997 J jnl
IEEE Trans. Fuzzy Syst.
Vassilios Petridis, Athanasios Kehagias
1997 J jnl
IEEE Robotics Autom. Mag.
Nikolaos Fahantidis, K. Paraschidis, Vassilios Petridis, Zoe Doulgeri, Loukas Petrou, Georgios Hasapis
1997 J jnl
Neural Comput.
Athanasios Kehagias, Vassilios Petridis
1996 J jnl
Neural Comput.
Vassilios Petridis, Athanasios Kehagias
1996 conf
International Conference on Evolutionary Computation
Panagiotis Adamidis, Vassilios Petridis
1996 conf
ICECS
S. Kiartzis, C. E. Zoumas, Anastasios G. Bakirtzis, Vassilios Petridis
1996 J jnl
IEEE Trans. Neural Networks
Vassilios Petridis, Athanasios Kehagias
1995 A* conf
ICRA
K. Paraschidis, Nikolaos Fahantidis, V. Vassiliadis, Vassilios Petridis, Zoe Doulgeri, Loukas Petrou, Georgios Hasapis
1995 J jnl
IEEE Trans. Neural Networks
Vassilios Petridis, K. Paraschidis
1994 conf
International Conference on Evolutionary Computation
Vassilios Petridis, Spiridon A. Kazarlis
1984 J jnl
Inf. Sci.
John B. Theocharis, Vassilios Petridis
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` |