Kai Hakala

21 papers Journal 10Unranked 11
YearRankTypeTitle / Venue / Authors
2025 conf
NoDaLiDa/Baltic-HLT
Sander Bijl de Vroe, George Stampoulidis, Kai Hakala, Aku Rouhe, Mark van Heeswijk, Jussi Karlgren
2022 J jnl
Int. J. Medical Informatics
Leena Uronen, Sanna Salanterä, Kai Hakala, Jaakko Hartiala, Hans Moen
2022 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Kai Hakala, Suwisa Kaewphan, Jari Björne, Farrokh Mehryary, Hans Moen, Martti Tolvanen, Tapio Salakoski, Filip Ginter
2020 J jnl
J. Biomed. Semant.
Hans Moen, Kai Hakala, Laura-Maria Peltonen, Hanna-Maria Matinolli, Henry Suhonen, Kirsi Terho, Riitta Danielsson-Ojala, Maija Valta, Filip Ginter, Tapio Salakoski, Sanna Salanterä
2020 J jnl
J. Am. Medical Informatics Assoc.
Hans Moen, Kai Hakala, Laura-Maria Peltonen, Henry Suhonen, Filip Ginter, Tapio Salakoski, Sanna Salanterä
2019 conf
BioNLP-OST@EMNLP-IJNCLP
Kai Hakala, Sampo Pyysalo
2019 J jnl
CoRR
Kai Hakala, Aleksi Vesanto, Niko Miekka, Tapio Salakoski, Filip Ginter
2018 J jnl
J. Am. Medical Informatics Assoc.
Abeed Sarker, Maksim Belousov, Jasper Friedrichs, Kai Hakala, Svetlana Kiritchenko, Farrokh Mehryary, Sifei Han, Tung Tran, Anthony Rios, Ramakanth Kavuluru, Berry de Bruijn, Filip Ginter, Debanjan Mahata, Saif M. Mohammad, Goran Nenadic, Graciela Gonzalez-Hernandez
2018 conf
Louhi@EMNLP
Hans Moen, Kai Hakala, Laura-Maria Peltonen, Henry Suhonen, Petri Loukasmäki, Tapio Salakoski, Filip Ginter, Sanna Salanterä
2018 J jnl
Database J. Biol. Databases Curation
Suwisa Kaewphan, Kai Hakala, Niko Miekka, Tapio Salakoski, Filip Ginter
2017 conf
BioNLP
Hans Moen, Kai Hakala, Farrokh Mehryary, Laura-Maria Peltonen, Tapio Salakoski, Filip Ginter, Sanna Salanterä
2017 conf
BioNLP
Farrokh Mehryary, Kai Hakala, Suwisa Kaewphan, Jari Björne, Tapio Salakoski, Filip Ginter
2017 conf
SMM4H@AMIA
Kai Hakala, Farrokh Mehryary, Hans Moen, Suwisa Kaewphan, Tapio Salakoski, Filip Ginter
2016 J jnl
J. Biomed. Semant.
Farrokh Mehryary, Suwisa Kaewphan, Kai Hakala, Filip Ginter
2016 conf
BioNLP@ACL
Kai Hakala, Suwisa Kaewphan, Tapio Salakoski, Filip Ginter
2015 J jnl
BMC Bioinform.
Kai Hakala, Sofie Van Landeghem, Tapio Salakoski, Yves Van de Peer, Filip Ginter
2015 conf
ACL (System Demonstrations)
Sampo Pyysalo, Jorge Campos, Juan Miguel Cejuela, Filip Ginter, Kai Hakala, Chen Li, Pontus Stenetorp, Lars Juhl Jensen
2015 conf
SemEval@NAACL-HLT
Kai Hakala
2014 conf
SemEval@COLING
Suwisa Kaewphan, Kai Hakala, Filip Ginter
2013 conf
BioNLP@ACL (Shared Task)
Kai Hakala, Sofie Van Landeghem, Tapio Salakoski, Yves Van de Peer, Filip Ginter
2012 J jnl
Adv. Bioinformatics
Sofie Van Landeghem, Kai Hakala, Samuel Rönnqvist, Tapio Salakoski, Yves Van de Peer, Filip Ginter
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"