Nenad Milosevic

14 papers B 4Journal 9Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
IEEE Access
Goran T. Djordjevic, Milica I. Petkovic, Nenad Milosevic, Bane Vasic
2024 J jnl
Wirel. Pers. Commun.
Jelena A. Anastasov, Aleksandra M. Cvetkovic, Aleksandra S. Panajotovic, Daniela M. Milovic, Dejan N. Milic, Nenad Milosevic
2020 J jnl
Robotics Auton. Syst.
Valentina Nejkovic, Nenad Petrovic, Milorad Tosic, Nenad Milosevic
2018 J jnl
J. Circuits Syst. Comput.
Nenad Milosevic, Caslav Stefanovic, Zorica B. Nikolic, Milos V. Bandjur, Mihajlo C. Stefanovic
2018 J jnl
Wirel. Pers. Commun.
Nenad Milosevic, Mihajlo C. Stefanovic, Zorica B. Nikolic, Petar C. Spalevic, Caslav Stefanovic
2016 conf
EuCNC
Milorad Tosic, Valentina Nejkovic, Filip Jelenkovic, Nenad Milosevic, Zorica B. Nikolic, Nikos Makris, Thanasis Korakis
2013 J jnl
Wirel. Pers. Commun.
Zorica B. Nikolic, Bojan Dimitrijevic, Nenad Milosevic
2012 J jnl
IEEE Trans. Veh. Technol.
Bojan Dimitrijevic, Zorica B. Nikolic, Nenad Milosevic
2009 B conf
PIMRC
Nenad Milosevic, Beatriz Lorenzo, Bojana Z. Nikolic, Savo Glisic
2007 J jnl
IET Commun.
Zorica B. Nikolic, Dorde S. Paunovic, Bojan Dimitrijevic, Nenad Milosevic
2005 B conf
PIMRC
Savo G. Glisic, Zorica B. Nikolic, Nenad Milosevic, Peka Pirinnen
2002 B conf
PIMRC
Savo Glisic, Zorica B. Nikolic, Nenad Milosevic, Fan Wang
2000 J jnl
IEEE J. Sel. Areas Commun.
Savo Glisic, Zorica B. Nikolic, Nenad Milosevic, Ari Pouttu
1998 B conf
PIMRC
Savo Glisic, Zorica B. Nikolic, Nenad Milosevic, Ari Pouttu
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"