Hanlin Niu

27 papers A* 1Journal 16Unranked 10
YearRankTypeTitle / Venue / Authors
2025 J jnl
Big Data Cogn. Comput.
Wenxing Liu, Ipek Caliskanelli, Hanlin Niu, Kaiqiang Zhang, Robert Skilton
2025 J jnl
IEEE Trans. Veh. Technol.
Wenxing Liu, Hanlin Niu, Kefan Wu, Wei Pan, Ze Ji, Robert Skilton
2024 J jnl
CoRR
Michal Staniaszek, Tobit Flatscher, Joseph Rowell, Hanlin Niu, Wenxing Liu, Yang You, Robert Skilton, Maurice F. Fallon, Nick Hawes
2024 conf
ICIT
Wenxing Liu, Hanlin Niu, Ipek Caliskanelli, Zhengjia Xu, Robert Skilton
2024 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Runqi Chai, Hanlin Niu, Joaquín Carrasco, Farshad Arvin, Hujun Yin, Barry Lennox
2024 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Wenxing Liu, Hanlin Niu, Inmo Jang, Guido Herrmann, Joaquín Carrasco
2023 J jnl
Remote. Sens.
Hanlin Niu, Xiao-Ming Hu, Lunyu Shang, Xianhong Meng, Shaoying Wang, Zhaoguo Li, Lin Zhao, Hao Chen, Mingshan Deng, Danrui Sheng
2023 A* conf
ICRA
Wenxing Liu, Hanlin Niu, Wei Pan, Guido Herrmann, Joaquín Carrasco
2023 J jnl
CoRR
Wenxing Liu, Hanlin Niu, Wei Pan, Guido Herrmann, Joaquín Carrasco
2023 conf
TAROS
Wenxing Liu, Hanlin Niu, Robert Skilton, Joaquín Carrasco
2023 J jnl
CoRR
Wenxing Liu, Hanlin Niu, Robert Skilton, Joaquín Carrasco
2022 J jnl
IEEE Trans. Veh. Technol.
Seongin Na, Hanlin Niu, Barry Lennox, Farshad Arvin
2022 conf
TAROS
Erwin Jose Lopez Pulgarin, Hanlin Niu, Guido Herrmann, Joaquín Carrasco
2022 J jnl
Remote. Sens.
Mingshan Deng, Xianhong Meng, Yaqiong Lu, Zhaoguo Li, Lin Zhao, Hanlin Niu, Hao Chen, Lunyu Shang, Shaoying Wang, Danrui Sheng
2021 conf
SII
Hanlin Niu, Ze Ji, Zihang Zhu, Hujun Yin, Joaquín Carrasco
2021 J jnl
CoRR
Hanlin Niu, Ze Ji, Zihang Zhu, Hujun Yin, Joaquín Carrasco
2021 J jnl
Robotics
Salvador Pacheco Gutierrez, Hanlin Niu, Ipek Caliskanelli, Robert Skilton
2021 conf
SII
Hanlin Niu, Ze Ji, Farshad Arvin, Barry Lennox, Hujun Yin, Joaquín Carrasco
2021 J jnl
CoRR
Hanlin Niu, Ze Ji, Farshad Arvin, Barry Lennox, Hujun Yin, Joaquín Carrasco
2021 conf
SII
Hanlin Niu, Ze Ji, Pietro Liguori, Hujun Yin, Joaquín Carrasco
2021 J jnl
CoRR
Hanlin Niu, Ze Ji, Pietro Liguori, Hujun Yin, Joaquín Carrasco
2021 conf
SII
Hanlin Niu, Ze Ji, Al Savvaris, Antonios Tsourdos, Joaquín Carrasco
2021 J jnl
CoRR
Hanlin Niu, Ze Ji, Al Savvaris, Antonios Tsourdos, Joaquín Carrasco
2021 conf
TAROS
Feiqiang Lin, Ze Ji, Changyun Wei, Hanlin Niu
2021 conf
SII
Inmo Jang, Hanlin Niu, Emily C. Collins, Andrew Weightman, Joaquín Carrasco, Barry Lennox
2020 J jnl
IEEE Trans. Veh. Technol.
Junyan Hu, Hanlin Niu, Joaquín Carrasco, Barry Lennox, Farshad Arvin
2019 conf
TAROS (2)
Junshen Chen, Ze Ji, Hanlin Niu, Rossitza Setchi, Chenguang Yang
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"