Ilias Spais

16 papers B 1Misc 1Journal 4Unranked 8
YearRankTypeTitle / Venue / Authors
2022 J jnl
CoRR
Panagiotis Katrakazas, Aristotelis Ballas, Marco Anisetti, Ilias Spais
2022 ch.
Technologies and Applications for Big Data Value
Andreas Alexopoulos, Yolanda Becerra, Omer Boehm, George Bravos, Vasilis Chatzigiannakis, Cesare Cugnasco, Giorgos Demetriou, Iliada Eleftheriou, Lidija Fodor, Spiros Fotis, Sotiris Ioannidis, Dusan Jakovetic, Leonidas Kallipolitis, Vlatka Katusic, Evangelia Kavakli, Despina Kopanaki, Christoforos Leventis, Mario Maawad Marcos, Ramon Martín de Pozuelo, Miquel Martínez, Nemanja Milosevic, Enric Pages i Montanera, Gerald H. Ristow, Hernan Ruiz-Ocampo, Rizos Sakellariou, Raül Sirvent, Srdjan Skrbic, Ilias Spais, Giorgos Vasiliadis, Michael Vinov
2022 ch.
Technologies and Applications for Big Data Value
Andreas Alexopoulos, Yolanda Becerra, Omer Boehm, George Bravos, Vasilis Chatzigiannakis, Cesare Cugnasco, Giorgos Demetriou, Iliada Eleftheriou, Spiros Fotis, Gianmarco Genchi, Sotiris Ioannidis, Dusan Jakovetic, Leonidas Kallipolitis, Vlatka Katusic, Evangelia Kavakli, Despina Kopanaki, Christoforos Leventis, Miquel Martínez, Julien-Etienne Mascolo, Nemanja Milosevic, Enric Pages i Montanera, Gerald H. Ristow, Hernan Ruiz-Ocampo, Rizos Sakellariou, Raül Sirvent, Srdjan Skrbic, Ilias Spais, Giuseppe Danilo Spennacchio, Dusan Stamenkovic, Giorgos Vasiliadis, Michael Vinov
2022 conf
BigDataService
Panagiotis Katrakazas, Aristotelis Ballas, Marco Anisetti, Ilias Spais
2022 Misc conf
CSR
Tatiana Trantidou, George Bravos, Philippe Valoggia, Ioannis Skourtis, Manolis Falelakis, Kostas Poulios, Ilias Spais, Sotiris Ioannidis, Thomas Oudin, Ruben Costa, Christopher Konialis, Daryl Holkham, Zoe Kasapi, Athanasios Karantjias
2022 J jnl
IEEE Access
Christos Liaskos, Ageliki Tsioliaridou, Konstantinos Georgopoulos, Ioannis Morianos, Sotiris Ioannidis, Iosif Salem, Dionysios Manessis, Stefan Schmid, Dimitrios Tyrovolas, Sotiris A. Tegos, Prodromos-Vasileios Mekikis, Panagiotis D. Diamantoulakis, Alexandros Pitilakis, Nikolaos V. Kantartzis, George K. Karagiannidis, Anna C. Tasolamprou, Odysseas Tsilipakos, Maria Kafesaki, Ian F. Akyildiz, Andreas Pitsillides, Maria Pateraki, Michael Vakalellis, Ilias Spais
2022 J jnl
CoRR
Christos Liaskos, Ageliki Tsioliaridou, Konstantinos Georgopoulos, George Morianos, Sotiris Ioannidis, Iosif Salem, Dionysios Manessis, Stefan Schmid, Dimitrios Tyrovolas, Sotiris A. Tegos, Prodromos-Vasileios Mekikis, Panagiotis D. Diamantoulakis, Alexandros Pitilakis, Nikolaos V. Kantartzis, George K. Karagiannidis, Anna C. Tasolamprou, Odysseas Tsilipakos, Maria Kafesaki, Ian F. Akyildiz, Andreas Pitsillides, Maria Pateraki, Michael Vakalellis, Ilias Spais
2021 conf
ICIMTH
Panagiotis Katrakazas, Theodora Kallipolitou, Ilias Spais
2019 B conf
SERVICES
Ioannis Arapakis, Yolanda Becerra, Omer Boehm, George Bravos, Vasilis Chatzigiannakis, Cesare Cugnasco, Giorgos Demetriou, Iliada Eleftheriou, Julien-Etienne Mascolo, Lidija Fodor, Sotiris Ioannidis, Dusan Jakovetic, Leonidas Kallipolitis, Evangelia Kavakli, Despina Kopanaki, Nicolas Kourtellis, Mario Maawad Marcos, Ramon Martín de Pozuelo, Nemanja Milosevic, Giuditta Morandi, Enric Pages i Montanera, Gerald H. Ristow, Rizos Sakellariou, Raül Sirvent, Srdjan Skrbic, Ilias Spais, Giorgos Vasiliadis, Michael Vinov
2018 conf
SAMOS
Rizos Sakellariou, Jorge Buenabad-Chávez, Evangelia Kavakli, Ilias Spais, Vasilios Tountopoulos
2018 conf
ESOCC Workshops
Giorgos Vasiliadis, Dusan Jakovetic, Ilias Spais, Sotiris Ioannidis
2016 conf
QUDOS@ISSTA
Elisabetta Di Nitto, Pooyan Jamshidi, Michele Guerriero, Ilias Spais, Damian A. Tamburri
2015 conf
MiSE@ICSE
Giuliano Casale, Danilo Ardagna, Matej Artac, Franck Barbier, Elisabetta Di Nitto, Alexis Henry, Gabriel Iuhasz, Christophe Joubert, José Merseguer, Victor Ion Munteanu, Juan Fernando Pérez, Dana Petcu, Matteo Rossi, Craig Sheridan, Ilias Spais, Daniel Vladuic
2012 J jnl
Int. J. Electron. Gov. Res.
Magdalini Kardara, Omri Fuchs, Eleni Kosta, Fotis Aisopos, Ilias Spais, Theodora A. Varvarigou
2011 conf
ICSOC Workshops
Erik Wittern, Christian Zirpins, Nidhi Rajshree, Anshu N. Jain, Ilias Spais, Konstantinos Giannakakis
2004 conf
International Conference on Computational Intelligence
Ilias Spais, George Bafas
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` |