Xiaokai Chen

35 papers A* 1A 3Journal 26Unranked 5
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Xiaokai Chen, Ilya A. Kuruzov, Gesualdo Scutari
2026 J jnl
J. Mach. Learn. Res.
Tianyu Cao, Xiaokai Chen, Gesualdo Scutari
2025 conf
CDC
Xiaokai Chen, Ilya A. Kuruzov, Gesualdo Scutari, Alexander V. Gasnikov
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Xiaokai Chen, Fenling Li, Qingrui Chang, Yuxin Miao, Chao Wang, Weilong Qin, Kang Yu
2025 J jnl
CoRR
Yongjia Ma, Donglin Di, Xuan Liu, Xiaokai Chen, Lei Fan, Wei Chen, Tonghua Su
2025 J jnl
CoRR
Xiaoyu Wang, Yue Zhao, Qingqing Gu, Zhonglin Jiang, Xiaokai Chen, Yong Chen, Luo Ji
2025 J jnl
CoRR
Tianyu Cao, Xiaokai Chen, Gesualdo Scutari
2025 J jnl
Comput. Electron. Agric.
Xiaokai Chen, Fenling Li, Qingrui Chang, Yuxin Miao, Kang Yu
2025 J jnl
Comput. Electron. Agric.
Chao Wang, Xiaobin Yan, Yan Bai, Jiaxue Zhang, Xinhui Guo, Xiaomei Zhang, Meichen Feng, William Rickard, Yu Zhao, Fangzhou Li, Chenbo Yang, Xiaokai Chen, Wude Yang, Xingxing Qiao
2025 J jnl
CoRR
Xiaokai Chen, Xiao Lin, Changcheng Li, Peng Jiang
2025 A conf
CIKM
Xiaokai Chen, Xiao Lin, Changcheng Li, Peng Jiang
2024 conf
WWW (Companion Volume)
Xiao Lin, Xiaokai Chen, Chenyang Wang, Hantao Shu, Linfeng Song, Biao Li, Peng Jiang
2024 J jnl
CoRR
Xiaokai Chen, Tianyu Cao, Gesualdo Scutari
2024 J jnl
Remote. Sens.
Huiling Miao, Xiaokai Chen, Yiming Guo, Qi Wang, Rui Zhang, Qingrui Chang
2024 J jnl
J. Frankl. Inst.
Qin Li, Hongwen He, Xiaokai Chen, Jianping Gao
2024 J jnl
CoRR
Xiaoyu Wang, Ningyuan Xi, Teng Chen, Qingqing Gu, Yue Zhao, Xiaokai Chen, Zhonglin Jiang, Yong Chen, Luo Ji
2024 J jnl
CoRR
Xiaokai Chen, Xuan Liu, Donglin Di, Yongjia Ma, Wei Chen, Tonghua Su
2023 J jnl
Remote. Sens.
Xiaokai Chen, Fenling Li, Qingrui Chang
2023 J jnl
CoRR
Xiao Lin, Xiaokai Chen, Chenyang Wang, Hantao Shu, Linfeng Song, Biao Li, Peng Jiang
2023 J jnl
Remote. Sens.
Xiaokai Chen, Fenling Li, Botai Shi, Qingrui Chang
2023 A* conf
KDD
Xiao Lin, Xiaokai Chen, Linfeng Song, Jingwei Liu, Biao Li, Peng Jiang
2023 J jnl
CoRR
Xiao Lin, Xiaokai Chen, Linfeng Song, Jingwei Liu, Biao Li, Peng Jiang
2023 J jnl
Remote. Sens.
Qi Wang, Xiaokai Chen, Huayi Meng, Huiling Miao, Shiyu Jiang, Qingrui Chang
2022 J jnl
Frontiers Comput. Neurosci.
Bin Shi, Xiaokai Chen, Zan Yue, Feixiang Zeng, Shuai Yin, Benguo Wang, Jing Wang
2022 J jnl
Remote. Sens.
Kai Fan, Fenling Li, Xiaokai Chen, Zhenfa Li, David J. Mulla
2021 conf
WWW (Companion Volume)
Xiaokai Chen, Xiaoguang Gu, Libo Fu
2020 J jnl
CoRR
Xiaokai Chen, Xiaoguang Gu, Libo Fu
2020 J jnl
Neural Process. Lett.
Jiarong Dong, Ke Gao, Xiaokai Chen, Juan Cao
2019 J jnl
CoRR
Jiarong Dong, Ke Gao, Xiaokai Chen, Junbo Guo, Juan Cao, Yongdong Zhang
2019 A conf
ICME
Xiaokai Chen, Ke Gao, Juan Cao
2018 J jnl
IEEE Access
Xiaokai Chen, Hao Lei, Rui Xiong
2018 J jnl
CoRR
Xiaokai Chen, Ke Gao
2018 A conf
BMVC
Jiarong Dong, Ke Gao, Xiaokai Chen, Junbo Guo, Juan Cao, Yongdong Zhang
2011 conf
BMEI
Tingting Wang, Xiaokai Chen, Yi Lin
2010 conf
FSKD
Yong Chen, Xiaokai Chen, Yi Lin
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"