Irina Paraschivoiu

17 papers A 1B 2Misc 3Journal 3Unranked 8
YearRankTypeTitle / Venue / Authors
2025 J jnl
Comput. Support. Cooperative Work.
Irina Paraschivoiu, Robert Steiner, Judith Wieser, Alexander Meschtscherjakov
2023 J jnl
Multimodal Technol. Interact.
Elise Hodson, Teija Vainio, Michel Nader Sayún, Martin Tomitsch, Ana Jones, Meri Jalonen, Ahmet Börütecene, Md. Tanvir Hasan, Irina Paraschivoiu, Annika Wolff, Sharon Yavo-Ayalon, Sari Yli-Kauhaluoma, Gareth W. Young
2023 conf
C&T
Irina Paraschivoiu, Marta Dziabiola, Alexander Meschtscherjakov
2022 conf
onference on Designing Interactive Systems (Companion Volume)
Irina Paraschivoiu
2022 conf
MindTrek
Irina Paraschivoiu, Janset Shawash, Marta Dziabiola, Narmeen Marji, Alexander Meschtscherjakov, Mattia Thibault
2022 J jnl
Interactions
Linda Hirsch, Eléni Economidou, Irina Paraschivoiu, Tanja Döring
2021 Misc conf
CHI PLAY
Irina Paraschivoiu, Josef Buchner, Robert Praxmarer, Thomas Layer-Wagner
2021 B conf
TEI
Linda Hirsch, Eléni Economidou, Irina Paraschivoiu, Tanja Döring, Andreas Butz
2021 Misc conf
CHI PLAY
Irina Paraschivoiu, Thomas Layer-Wagner
2021 Misc conf
MuC
Philipp Wagner, Anna Winkler, Irina Paraschivoiu, Alexander Meschtscherjakov, Magdalena Gärtner, Manfred Tscheligi
2020 conf
BCSS@PERSUASIVE
Irina Paraschivoiu, Thomas Layer-Wagner, Alexander Meschtscherjakov, Nina Möstegl, Petra Stabauer
2020 A conf
Conference on Designing Interactive Systems (Companion Volume)
Irina Paraschivoiu, Anna Winkler, Alexander Meschtscherjakov
2020 conf
ICMI Companion
Irina Paraschivoiu, Jakub Sypniewski, Artur Lupp, Magdalena Gärtner, Nadejda Miteva, Zlatka Gospodinova
2020 conf
PERSUASIVE (Adjunct)
Irina Paraschivoiu, Thomas Layer-Wagner, Alexander Meschtscherjakov, Nina Möstegl, Petra Stabauer
2020 conf
PERSUASIVE (Adjunct)
Irina Paraschivoiu
2019 B conf
PERSUASIVE
Irina Paraschivoiu, Alexander Meschtscherjakov, Magdalena Gärtner, Jakub Sypniewski
2019 conf
CHI Extended Abstracts
Irina Paraschivoiu, Alexander Meschtscherjakov, Manfred Tscheligi
redb/extractors/js_extractors/js_content.py
← Index redb/extractors/js_extractors/js_content.py python
"""Persists raw + normalised text into the generic `code_text_content` table.

Reads the raw source and the deobfuscation result directly from the shared
JSContext so no extra compute happens here — both values are computed once
per sample (the source at JSContext construction, the deobfuscation lazily
on first access) and reused by any extractor that needs them.

`text_normalized` is left NULL when the deobfuscation pass produced no
output, so analysts can distinguish "we tried and got nothing" from
"normalisation succeeded".
"""

import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.js_extractor import JSExtractor


class JSContentExtractor(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.content_row = None
        self.log.debug(inspect.currentframe().f_code.co_name)

    def tag(self):
        return Tag.JS_CONTENT.value

    def extract(self):
        src = self.js_source
        if not src:
            return None

        deobfuscated, normalizer_used = self._context.deobfuscated

        self.content_row = {
            "content_type": self._context.content_type,
            "text_raw": src,
            "text_normalized": deobfuscated,  # may be None
            "normalizer_used": normalizer_used,  # may be None
        }
        return self.content_row

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type != "ClickHouseExporter":
            return None
        if not self.content_row:
            return None

        r = self.content_row
        data = [[
            self.sha256,
            r["content_type"],
            r["text_raw"],
            r["text_normalized"],
            r["normalizer_used"],
            datetime.now(timezone.utc),
        ]]

        column_names = [
            "sha256",
            "content_type",
            "text_raw",
            "text_normalized",
            "normalizer_used",
            "analysis_date",
        ]

        column_type_names = [
            "FixedString(64)",
            "LowCardinality(String)",
            "String",
            "Nullable(String)",
            "Nullable(String)",
            "DateTime64(3, 'UTC')",
        ]

        return (data, column_names, column_type_names)

    def get_clickhouse_table(self) -> str:
        return "code_text_content"