Imran Latif

22 papers B 6C 1Misc 1Journal 6Unranked 7
YearRankTypeTitle / Venue / Authors
2025 conf
e-Energy
Chengyi Nie, Anna Xing, Imran Latif, Zhenhua Liu
2025 J jnl
IEEE Trans. Cloud Comput.
Imran Latif, Muhammad Mubashar Ashraf, Umaima Haider, Gemma Reeves, Alexandrina Untaroiu, Fábio Coelho, Denis Browne
2025 B conf
MASCOTS
Chengyi Nie, Anna Xing, Imran Latif, Zhenhua Liu
2025 J jnl
CoRR
Imran Latif, Muhammad Ali Shafique, Hayat Ullah, Alex C. Newkirk, Xi Yu, Arslan Munir
2025 J jnl
CoRR
Alex C. Newkirk, Jared Fernandez, Jonathan Koomey, Imran Latif, Emma Strubell, Arman Shehabi, Constantine Samaras
2025 J jnl
IEEE Access
Imran Latif, Alex C. Newkirk, Matthew R. Carbone, Arslan Munir, Yuewei Lin, Jonathan Koomey, Xi Yu, Zhihua Dong
2024 J jnl
CoRR
Imran Latif, Alex C. Newkirk, Matthew R. Carbone, Arslan Munir, Yuewei Lin, Jonathan Koomey, Xi Yu, Zhiuha Dong
2024 J jnl
IEEE Access
Muhammad Ali Shafique, Arslan Munir, Imran Latif
2018 C conf
MODELSWARD
Andrea Enrici, Julien Lallet, Imran Latif, Ludovic Apvrille, Renaud Pacalet, Adrien Canuel
2018 B conf
Networking
Fred Aklamanu, Sabine Randriamasy, Eric Renault, Imran Latif, Abdelkrim Hebbar, Alberto Conte, Bilal Al Jamal, Warda Hamdaoui
2018 conf
GLOBECOM Workshops
Fred Aklamanu, Sabine Randriamasy, Eric Renault, Imran Latif, Abdelkrim Hebbar
2018 conf
MODELSWARD (Revised Selected Papers)
Andrea Enrici, Julien Lallet, Renaud Pacalet, Ludovic Apvrille, Karol Desnos, Imran Latif
2017 conf
WorldCIST (2)
Muhammad Shahid Bhatti, Syed Asad Hussain, Abdul Qayyum, Imran Latif, Muhammad Hasnain, Sajid Ibrahim Hashmi
2013 conf
SimuTools
Imran Latif, Florian Kaltenberger, Navid Nikaein, Raymond Knopp
2013 B conf
WiOpt
Imran Latif, Florian Kaltenberger, Raymond Knopp, Joan J. Olmos
2013 Misc conf
ACSSC
Florian Kaltenberger, Imran Latif, Raymond Knopp
2013
Imran Latif
2013 conf
SocialNLP@IJCNLP
Imran Latif, Syed Waqar Jaffry
2012 B conf
WCNC
Imran Latif, Florian Kaltenberger, Raymond Knopp
2012 conf
WCNC Workshops
Andrea F. Cattoni, H. T. Nguyen, Jonathan Duplicy, Deepaknath Tandur, Biljana Badic, RajaRajan Balraj, Florian Kaltenberger, Imran Latif, Ankit Bhamri, Guillaume Vivier, I. Z. Kovacsk, Péter Horváth
2011 B conf
PIMRC
Imran Latif, Florian Kaltenberger, Rizwan Ghaffar, Raymond Knopp, Dominique Nussbaum, H. Callewaert, Gaël Scot
2010 B conf
PIMRC
Onurcan Iscan, Imran Latif, Christoph Hausl
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"