Irena Pletikosa Cvijikj

23 papers B 3C 2Journal 4Unranked 13
YearRankTypeTitle / Venue / Authors
2017 B conf
ICWE
Yiea-Funk Te, Irena Pletikosa Cvijikj
2016 B conf
SERVICES
Daniel Muller, Funk Te, Irena Pletikosa Cvijikj
2016 conf
SummerSim
Raquel Rosés Brüngger, Cristina Kadar, Irena Pletikosa Cvijikj
2016 conf
CHI Extended Abstracts
Cristina Kadar, Yiea-Funk Te, Raquel Rosés Brüngger, Irena Pletikosa Cvijikj
2016 conf
ECIS
Funk Te, Cristina Kadar, Raquel Rosés Brüngger, Irena Pletikosa Cvijikj
2015 conf
IEEE BigData
Daniel Muller, Stefan Mau, Irena Pletikosa Cvijikj
2015 J jnl
IEEE Internet Things J.
Michele Nitti, Luigi Atzori, Irena Pletikosa Cvijikj
2015 C conf
ICIS
Irena Pletikosa Cvijikj, Cristina Kadar, Bogdan Ivan, Yiea-Funk Te
2015 conf
WISE (1)
Cristina Kadar, Grammatiki Zanni, Thijs Vogels, Irena Pletikosa Cvijikj
2015 conf
UbiComp/ISWC Adjunct
Irena Pletikosa Cvijikj, Cristina Kadar, Bogdan Ivan, Yiea-Funk Te
2014 B conf
MUM
Cristina Kadar, Irena Pletikosa Cvijikj
2014 conf
ECIS
Tobias Kowatsch, Wolfgang Maass, Irena Pletikosa Cvijikj, Dirk Büchter, Björn Brogle, Anneco Dintheer, Dunja Wiegand, Dominique Durrer-Schutz, Runhua Xu, Yves Schutz, Dagmar L'Allemand-Jander
2014 conf
ECIS
Irena Pletikosa Cvijikj, Tobias Kowatsch, Dirk Büchter, Björn Brogle, Anneco Dintheer, Dunja Wiegand, Dominique Durrer-Schutz, Dagmar L'Allemand-Jander, Yves Schutz, Wolfgang Maass
2014 conf
WF-IoT
Michele Nitti, Luigi Atzori, Irena Pletikosa Cvijikj
2014 conf
AmI
Runhua Xu, Irena Pletikosa Cvijikj, Tobias Kowatsch, Florian Michahelles, Dirk Büchter, Björn Brogle, Anneco Dintheer, Dagmar L'Allemand, Wolfgang Maass
2013 J jnl
Soc. Netw. Anal. Min.
Irena Pletikosa Cvijikj, Erica Dubach Spiegler, Florian Michahelles
2013 J jnl
Soc. Netw. Anal. Min.
Irena Pletikosa Cvijikj, Florian Michahelles
2013 J jnl
Int. J. Soc. Humanist. Comput.
Irena Pletikosa Cvijikj, Florian Michahelles
2011 conf
SocInfo
Irena Pletikosa Cvijikj, Florian Michahelles
2011 C conf
DASC
Irena Pletikosa Cvijikj, Florian Michahelles
2011 conf
SocialCom/PASSAT
Irena Pletikosa Cvijikj, Erica Dubach Spiegler, Florian Michahelles
2011 ch.
Architecting the Internet of Things
Irena Pletikosa Cvijikj, Florian Michahelles
2011 conf
MindTrek
Irena Pletikosa Cvijikj, Florian Michahelles
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"