Jae Min Kim

20 papers A 2C 4Journal 8Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
BMC Medical Imaging
Kwangho Chung, Ji-Hoon Nam, Arailym Dosset, Yong-Gon Koh, Jae Min Kim, Paul Shinil Kim, Jin Woo Lee, Kyoung-Mi Park, Hyuck Min Kwon, Kyoung-Tak Kang
2024 A conf
DATE
Jimin Lee, Sangwoo Park, Junho Huh, Sanghyo Jeong, Inhwan Kim, Jae Min Kim
2023 J jnl
Appl. Soft Comput.
Ji Hyeon Shin, Junyong Bae, Jae Min Kim, Seung Jun Lee
2023 C conf
ICCE
Daecheol You, Jae Min Kim, Taesung Kim, Junho Huh
2023 C conf
ICCE
Jimin Lee, Jae Min Kim, Junho Huh, Jungwoo Kim
2020 C conf
ICCE
Yeonsung Chu, Jae Min Kim, YoonJick Lee, SungHoon Shim, Junho Huh
2019 conf
IHSI
Jae Min Kim, Gyumin Lee, Suckwon Hong, Seung Jun Lee
2019 C conf
ICCE
Jae Min Kim, Jae Won Lee, Kyungsoo Lee, Junho Huh
2018 conf
HCI (28)
Jeeyea Ahn, Jae Min Kim, Seung Jun Lee
2018 conf
SENSORNETS
Seong Ho Jang, Si Bog Park, Sang-bog Moon, Jae Min Kim, Shi-Uk Lee
2018 conf
HCI (5)
Jae Min Kim, Seung Jun Lee
2016 J jnl
Microprocess. Microsystems
Young-Ho Gong, Jae Min Kim, Sung Kyu Lim, Sung Woo Chung
2016 J jnl
J. Inform. and Commun. Convergence Engineering
Jae Min Kim, Jinseok Oh
2016 J jnl
J. Sensors
Jae Min Kim, Hyun-Woo Kim, Song-yi Choi, Sung-Yong Park
2016 conf
EDB
Joo Hong Lee, Jae Min Kim, Yong Suk Choi
2015 A conf
DATE
Young-geun Kim, Minyong Kim, Jae Min Kim, Sung Woo Chung
2015 J jnl
IEEE Trans. Computers
Jae Min Kim, Young Geun Kim, Sung Woo Chung
2014 conf
PATMOS
Jae Min Kim, Minyong Kim, Sung Woo Chung
2011 J jnl
Computer
Jae Min Kim, Minyong Kim, Joonho Kong, Hyung Beom Jang, Sung Woo Chung
2008 J jnl
J. Comput. Aided Mol. Des.
Dong Sun Park, Jae Min Kim, Young Bok Lee, Chang Ho Ahn
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"