Jaehoon Sim

15 papers A* 1C 2Journal 7Unranked 5
YearRankTypeTitle / Venue / Authors
2026 J jnl
Health Inf. Sci. Syst.
Byung Jun Kim, Dong Ah Shin, Woo Sang Cho, Soyoon Kwon, Jung Chan Lee, Taegyun Kim, Kyung Su Kim, Gil Joon Suh, Jaehoon Sim, Jaeheung Park
2026 J jnl
CoRR
Junghoon Seo, Hakjin Lee, Jaehoon Sim
2025 J jnl
Int. J. Soc. Robotics
Beomyeong Park, Donghyeon Kim, Daegyu Lim, Suhan Park, Junewhee Ahn, Seungyeon Kim, Jaeyong Shin, Eunho Sung, Jaehoon Sim, Junhyung Kim, Myeong-Ju Kim, Junhyeok Cha, Gyeongjae Park, Hokyun Lee, Seungbin You, Keunwoo Jang, Seung-Hun Kim, Mathew Schwartz, Jaeheung Park
2025 J jnl
CoRR
Hakjin Lee, Junghoon Seo, Jaehoon Sim
2024 C conf
ICCE
Yongjun Lee, Jaehoon Sim, Dong Ho Kim, Dongho You
2024 C conf
ICCE
Jaehoon Sim, Dongho You, Yongjun Lee, Sung-Hoon Kim, Dong Ho Kim
2023 J jnl
IEEE Access
Junewhee Ahn, Suhan Park, Jaehoon Sim, Jaeheung Park
2022 conf
Humanoids
Mathew Schwartz, Jaehoon Sim, Junewhee Ahn, Soonwook Hwang, Yisoo Lee, Jaeheung Park
2021 conf
UR
Seungbin You, Jaehoon Sim, Seungyeon Kim, Eunho Sung, Jaeheung Park
2020 J jnl
IEEE Robotics Autom. Lett.
Seungyeon Kim, Jaehoon Sim, Jaeheung Park
2019 conf
SII
Suhan Park, Jaehoon Sim, Jaeheung Park
2018 A* conf
ICRA
Mingon Kim, Jung Hoon Kim, Sanghyun Kim, Jaehoon Sim, Jaeheung Park
2017 conf
Humanoids
Jaesug Jung, Soonwook Hwang, Yisoo Lee, Jaehoon Sim, Jaeheung Park
2017 J jnl
J. Field Robotics
Sanghyun Kim, Mingon Kim, Jimin Lee, Soonwook Hwang, Joonbo Chae, Beomyeong Park, Hyunbum Cho, Jaehoon Sim, Jaesug Jung, Hosang Lee, Seho Shin, Minsung Kim, Wonje Choi, Yisoo Lee, Sumin Park, Jiyong Oh, Yongjin Lee, Sangkuk Lee, Myunggi Lee, Sangyup Yi, Kyong-Sok K. C. Chang, Nojun Kwak, Jaeheung Park
2015 conf
Humanoids
Sanghyun Kim, Mingon Kim, Jimin Lee, Soonwook Hwang, Joonbo Chae, Beomyeong Park, Hyunbum Cho, Jaehoon Sim, Jaesug Jung, Hosang Lee, Seho Shin, Minsung Kim, Nojun Kwak, Yongjin Lee, Sangkuk Lee, Myunggi Lee, Sangyup Yi, Kyong-Sok K. C. Chang, Jaeheung Park
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"