Igor B. Furtat

56 papers C 8Journal 28Unranked 20
YearRankTypeTitle / Venue / Authors
2026 J jnl
IEEE Trans. Autom. Control.
Igor B. Furtat
2025 J jnl
CoRR
Igor B. Furtat, N. V. Kuznetsov
2025 J jnl
CoRR
Igor B. Furtat
2024 conf
CDC
Igor B. Furtat
2024 J jnl
Eur. J. Control
Igor B. Furtat, Pavel A. Gushchin, Ba Huy Nguyen
2023 J jnl
Autom. Remote. Control.
Igor B. Furtat, Pavel A. Gushchin, Ba Huy Nguyen
2023 J jnl
Autom. Remote. Control.
Igor B. Furtat
2023 J jnl
IEEE Trans. Autom. Control.
Artem N. Nekhoroshikh, Denis V. Efimov, Andrei Polyakov, Wilfrid Perruquetti, Igor B. Furtat
2022 J jnl
IEEE Access
Igor B. Furtat, Pavel A. Gushchin
2022 conf
CDC
Artem N. Nekhoroshikh, Denis V. Efimov, Andrey E. Polyakov, Wilfrid Perruquetti, Igor B. Furtat
2022 J jnl
Autom.
Artem N. Nekhoroshikh, Denis V. Efimov, Emilia Fridman, Wilfrid Perruquetti, Igor B. Furtat, Andrei Polyakov
2021 J jnl
Autom. Remote. Control.
Igor B. Furtat, Pavel A. Gushchin
2021 conf
CDC
Artem N. Nekhoroshikh, Denis V. Efimov, Andrey E. Polyakov, Wilfrid Perruquetti, Igor B. Furtat
2021 J jnl
Autom. Remote. Control.
Igor B. Furtat, Artem N. Nekhoroshikh, Pavel A. Gushchin
2021 J jnl
Autom. Remote. Control.
Igor B. Furtat, Pavel A. Gushchin
2021 J jnl
IEEE Access
Igor B. Furtat, Pavel A. Gushchin
2020 conf
MED
Sergey A. Vrazhevsky, Dmitry E. Konovalov, Igor B. Furtat, Artem S. Kremlev
2020 J jnl
Int. J. Control
Igor B. Furtat
2020 J jnl
Autom. Remote. Control.
Boris R. Andrievsky, Igor B. Furtat
2020 J jnl
Autom. Remote. Control.
Boris R. Andrievsky, Igor B. Furtat
2020 J jnl
Autom. Remote. Control.
Igor B. Furtat
2020 conf
CDC
Artem N. Nekhoroshikh, Denis V. Efimov, Andrey E. Polyakov, Wilfrid Perruquetti, Igor B. Furtat
2020 C conf
CoDIT
Alexey A. Peregudin, Igor B. Furtat
2020 conf
ECC
Dmitry E. Konovalov, Sergey A. Vrazhevsky, Igor B. Furtat, Artem S. Kremlev, Martin Cech
2020 conf
MED
Igor B. Furtat, Pavel A. Gushchin
2019 J jnl
Autom. Remote. Control.
Igor B. Furtat, Pavel A. Gushchin
2019 C conf
CoDIT
Igor B. Furtat, Pavel A. Gushchin, Artem N. Nekhoroshikh, Sergey A. Vrazhevsky, Mikhail S. Tarasov, Julia V. Chugina
2019 C conf
CoDIT
Igor B. Furtat, Pavel A. Gushchin, Artem N. Nekhoroshikh, Sergey A. Vrazhevsky, Julia V. Chugina
2019 conf
ICINCO (1)
Dmitry E. Konovalov, Sergey A. Vrazhevsky, Igor B. Furtat, Artem S. Kremlev
2018 J jnl
Autom. Remote. Control.
Igor B. Furtat
2018 conf
ICUMT
Igor B. Furtat, Julia V. Chugina
2017 J jnl
Autom. Remote. Control.
Alexey A. Margun, Alexey A. Bobtsov, Igor B. Furtat
2017 conf
CDC
Igor B. Furtat
2017 J jnl
Autom. Remote. Control.
Igor B. Furtat
2017 C conf
CoDIT
Igor B. Furtat
2017 conf
ICUMT
Igor B. Furtat, Sergey A. Vrazevsky, Artem S. Kremlev, Pavel A. Gushchin
2017 C conf
CoDIT
Igor B. Furtat, Evgeny A. Tupichin
2017 conf
MED
Igor B. Furtat, Artem N. Nekhoroshikh
2017 conf
ICUMT
Sergey A. Vrazevsky, Julia V. Chugina, Igor B. Furtat, Artem S. Kremlev
2016 conf
MED
Igor B. Furtat, Vladislav S. Gromov
2016 J jnl
Autom. Remote. Control.
Igor B. Furtat, Evgeny A. Tupichin
2016 J jnl
CoRR
Igor B. Furtat
2016 J jnl
CoRR
Igor B. Furtat, Artem N. Nekhoroshikh
2016 C conf
ACC
Igor B. Furtat, Evgeny A. Tupichin
2016 conf
ICUMT
Sergey A. Vrazevsky, Julia V. Chugina, Igor B. Furtat, Artem S. Kremlev
2015 J jnl
Autom.
Igor B. Furtat, Alexander L. Fradkov, Daniel Liberzon
2015 conf
ECC
Igor B. Furtat, Julia V. Chugina, Alexander L. Fradkov
2015 J jnl
Autom. Remote. Control.
Igor B. Furtat
2014 conf
ICUMT
Alexey A. Bobtsov, Maxim V. Faronov, Igor B. Furtat, Anton A. Pyrkin, Sergey A. Arustamov
2014 J jnl
Autom. Remote. Control.
Igor B. Furtat
2014 C conf
CCA
Igor B. Furtat, Evgeny A. Tupichin
2014 C conf
CCA
Igor B. Furtat
2014 conf
ICUMT
Igor B. Furtat, Evgeny A. Tupichin
2014 conf
MED
Igor B. Furtat
2014 conf
ICUMT
Igor B. Furtat
2013 J jnl
Autom. Remote. Control.
Alexander L. Fradkov, Igor B. Furtat
redb/extractors/js_extractors/js_suspicious_apis.py
← Index redb/extractors/js_extractors/js_suspicious_apis.py python
import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.js_extractor import JSExtractor
from redb.extractors.js_extractors.js_patterns import CATEGORIES, PATTERNS


# Backwards-compatible export: `{category: [(raw_pattern_string, api_name), ...]}`
# in canonical PATTERNS insertion order (code_execution, network, filesystem,
# process, registry, crypto_encoding, dom_manipulation). Kept so external
# callers (notably JSDeobfuscationExtractor pre-cleanup) keep working until
# they are migrated to PATTERNS directly.
SUSPICIOUS_APIS: "dict[str, list[tuple[str, str]]]" = {}
for _name, _compiled in PATTERNS.items():
    SUSPICIOUS_APIS.setdefault(CATEGORIES[_name], []).append((_compiled.pattern, _name))


class JSSuspiciousAPIsExtractor(JSExtractor):

    def __init__(
        self, filepath, log, exporters=None, index_prefix=None,
        known_benign=False, known_malicious=False, source=None, context=None,
    ):
        super().__init__(
            filepath, log, exporters, index_prefix,
            known_benign, known_malicious, source, context=context,
        )
        self.api_findings = None
        self.log.debug(inspect.currentframe().f_code.co_name)

    def tag(self):
        return Tag.JS_SUSPICIOUS_APIS.value

    def _get_context_snippet(self, line, max_len=200):
        """Get a truncated context snippet around a match."""
        line = line.strip()
        if len(line) > max_len:
            return line[:max_len] + "..."
        return line

    def extract(self):
        src = self.js_source
        if not src:
            return None

        # Pass 1: shared per-sample scan over the raw source. The dict contains
        # entries for both PATTERNS and FEATURE_PATTERNS; the loop below only
        # consults PATTERNS keys, so feature-only entries are ignored.
        raw_scan = self._context.scan or {}
        raw_lines = self.lines

        # Pass 2: same patterns over the deobfuscated text, when the
        # deobfuscator produced something meaningfully different. APIs hidden
        # behind one obfuscation layer (Vjw0rm-style array.join + eval,
        # Dean-Edwards packers, jjencode, ...) only surface here. The scan is
        # cached on JSContext so JSDeobfuscationExtractor (which computes the
        # new_apis_found diff) reuses the same result.
        deobf_scan = self._context.scan_deobfuscated
        if deobf_scan:
            deobf_text, _ = self._context.deobfuscated
            deobf_lines = deobf_text.splitlines()
        else:
            deobf_lines = []

        findings = []
        # Iterate PATTERNS in canonical order so output is deterministic and
        # matches the historical category/pattern ordering. For each api_name,
        # raw findings take precedence; if an API is found only in the
        # deobfuscated text, we surface it as a row tagged revealed_by_deobf=1
        # with line numbers / snippets pulled from the deobfuscated source.
        for api_name in PATTERNS:
            raw_info = raw_scan.get(api_name)
            if raw_info:
                line_numbers = raw_info["lines"]
                lines_for_snippets = raw_lines
                revealed_by_deobf = 0
            else:
                deobf_info = deobf_scan.get(api_name)
                if not deobf_info:
                    continue
                line_numbers = deobf_info["lines"]
                lines_for_snippets = deobf_lines
                revealed_by_deobf = 1

            snippets = [
                self._get_context_snippet(lines_for_snippets[ln - 1])
                for ln in line_numbers[:3]
                if 0 < ln <= len(lines_for_snippets)
            ]
            findings.append({
                "api_name": api_name,
                "api_category": CATEGORIES[api_name],
                # Historical semantics: count = number of unique lines with a
                # match, not total in-source match count.
                "call_count": len(line_numbers),
                "line_numbers": line_numbers,
                "context_snippet": " | ".join(snippets),
                "revealed_by_deobf": revealed_by_deobf,
            })

        if not findings:
            return None

        self.api_findings = findings
        return findings

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type == "ClickHouseExporter":
            if not self.api_findings:
                return None

            current_time = datetime.now(timezone.utc)
            data = []
            for f in self.api_findings:
                data.append([
                    self.sha256,
                    f['api_name'],
                    f['api_category'],
                    f['call_count'],
                    f['line_numbers'],
                    f['context_snippet'],
                    f['revealed_by_deobf'],
                    current_time,
                ])

            column_names = [
                "sha256", "api_name", "api_category",
                "call_count", "line_numbers", "context_snippet",
                "revealed_by_deobf",
                "analysis_date",
            ]

            column_type_names = [
                "FixedString(64)", "String", "LowCardinality(String)",
                "UInt32", "Array(UInt32)", "String",
                "UInt8",
                "DateTime64(3, 'UTC')",
            ]

            return (data, column_names, column_type_names)

    def get_clickhouse_table(self) -> str:
        return "redb_js_suspicious_apis"