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Initial commit - 611 cybersecurity skills across all subdomains
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---
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name: detecting-mobile-malware-behavior
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description: >
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Detects and analyzes malicious behavior in mobile applications through behavioral analysis,
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permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when
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analyzing suspicious mobile applications for data exfiltration, command-and-control communication,
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credential stealing, SMS interception, or other malware indicators. Activates for requests involving
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mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
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domain: cybersecurity
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subdomain: mobile-security
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author: mahipal
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tags: [mobile-security, android, ios, malware-analysis, owasp-mobile, penetration-testing]
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version: 1.0.0
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license: MIT
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---
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# Detecting Mobile Malware Behavior
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## When to Use
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Use this skill when:
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- Analyzing suspicious mobile applications submitted by users or discovered during incident response
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- Monitoring enterprise mobile fleet for malicious app indicators
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- Performing malware triage on APK/IPA samples
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- Investigating data exfiltration or unauthorized device access from mobile apps
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**Do not use** this skill to create, enhance, or distribute malware. This skill is for defensive analysis only.
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## Prerequisites
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- Isolated analysis environment (dedicated device or emulator, not connected to production networks)
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- MobSF for automated static+dynamic analysis
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- Frida/Objection for runtime behavior monitoring
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- Wireshark/tcpdump for network traffic capture
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- Android emulator (AVD) or Genymotion for safe execution
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- VirusTotal API key for hash lookups
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## Workflow
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### Step 1: Static Indicator Analysis
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```bash
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# Hash the sample
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sha256sum suspicious.apk
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# Check VirusTotal
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curl -s "https://www.virustotal.com/api/v3/files/<SHA256>" \
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-H "x-apikey: <VT_API_KEY>" | jq '.data.attributes.last_analysis_stats'
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# Extract permissions from AndroidManifest.xml
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aapt dump permissions suspicious.apk
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# High-risk permission combinations:
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# READ_SMS + INTERNET = SMS stealer
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# RECEIVE_SMS + SEND_SMS = SMS interceptor/banker trojan
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# ACCESSIBILITY_SERVICE + INTERNET = overlay attack capability
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# CAMERA + RECORD_AUDIO + INTERNET = spyware
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# DEVICE_ADMIN + INTERNET = ransomware capability
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# READ_CONTACTS + INTERNET = contact exfiltration
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```
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### Step 2: MobSF Automated Malware Scan
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```bash
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# Upload to MobSF
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curl -F "file=@suspicious.apk" http://localhost:8000/api/v1/upload \
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-H "Authorization: <API_KEY>"
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# Review malware indicators in report:
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# - Hardcoded C2 server addresses
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# - Dynamic code loading (DexClassLoader)
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# - Reflection-based API calls (to evade static analysis)
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# - Encrypted/obfuscated payloads
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# - Root detection (malware often checks for root)
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# - Anti-emulator checks (malware evades sandbox)
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```
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### Step 3: Network Behavior Monitoring
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```bash
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# Start packet capture on emulator
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tcpdump -i any -w malware_traffic.pcap
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# Or use mitmproxy for HTTP/HTTPS
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mitmproxy --mode transparent
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# Monitor for:
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# - DNS lookups to suspicious/newly registered domains
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# - Connections to known C2 infrastructure
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# - Data exfiltration patterns (large POST requests)
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# - Beaconing behavior (regular interval connections)
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# - Non-standard ports and protocols
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# - Domain Generation Algorithm (DGA) patterns
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```
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### Step 4: Runtime Behavior Monitoring with Frida
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```javascript
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// monitor_malware.js - Comprehensive behavior monitoring
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Java.perform(function() {
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// Monitor SMS access
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var SmsManager = Java.use("android.telephony.SmsManager");
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SmsManager.sendTextMessage.overload("java.lang.String", "java.lang.String",
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"java.lang.String", "android.app.PendingIntent", "android.app.PendingIntent")
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.implementation = function(dest, sc, text, sent, delivery) {
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console.log("[SMS] Sending to: " + dest + " Text: " + text);
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// Allow or block based on analysis needs
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return this.sendTextMessage(dest, sc, text, sent, delivery);
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};
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// Monitor file operations
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var FileOutputStream = Java.use("java.io.FileOutputStream");
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FileOutputStream.$init.overload("java.lang.String").implementation = function(path) {
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console.log("[FILE-WRITE] " + path);
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return this.$init(path);
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};
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// Monitor network connections
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var URL = Java.use("java.net.URL");
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URL.openConnection.overload().implementation = function() {
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console.log("[NET] " + this.toString());
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return this.openConnection();
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};
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// Monitor dynamic code loading
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var DexClassLoader = Java.use("dalvik.system.DexClassLoader");
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DexClassLoader.$init.implementation = function(dexPath, optDir, libPath, parent) {
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console.log("[DEX-LOAD] Loading: " + dexPath);
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return this.$init(dexPath, optDir, libPath, parent);
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};
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// Monitor command execution
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var Runtime = Java.use("java.lang.Runtime");
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Runtime.exec.overload("java.lang.String").implementation = function(cmd) {
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console.log("[EXEC] " + cmd);
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return this.exec(cmd);
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};
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// Monitor camera/audio access
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var Camera = Java.use("android.hardware.Camera");
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Camera.open.overload("int").implementation = function(id) {
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console.log("[CAMERA] Camera opened: " + id);
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return this.open(id);
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};
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// Monitor content provider access (contacts, call log)
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var ContentResolver = Java.use("android.content.ContentResolver");
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ContentResolver.query.overload("android.net.Uri", "[Ljava.lang.String;",
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"java.lang.String", "[Ljava.lang.String;", "java.lang.String")
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.implementation = function(uri, proj, sel, selArgs, sort) {
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console.log("[QUERY] " + uri.toString());
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return this.query(uri, proj, sel, selArgs, sort);
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};
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console.log("[*] Malware behavior monitor active");
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});
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```
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### Step 5: Classify Malware Type
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Based on observed behaviors, classify the sample:
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| Behavior Pattern | Malware Type |
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|-----------------|-------------|
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| SMS interception + C2 communication | Banking Trojan |
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| Camera/mic access + data upload | Spyware/Stalkerware |
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| File encryption + ransom note display | Mobile Ransomware |
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| Ad injection + click fraud traffic | Adware |
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| Root exploit + persistence | Rootkit |
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| Contact harvesting + SMS spam | Worm/SMS Spammer |
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| Overlay attacks + credential capture | Credential Stealer |
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| Crypto mining network activity | Cryptojacker |
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## Key Concepts
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| Term | Definition |
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|------|-----------|
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| **Dynamic Code Loading** | Loading executable code at runtime from external sources, commonly used by malware to evade static analysis |
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| **C2 Beacon** | Regular network check-in from malware to command-and-control server, identifiable by periodic timing patterns |
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| **DGA** | Domain Generation Algorithm creating pseudo-random domain names for resilient C2 infrastructure |
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| **Overlay Attack** | Drawing fake UI over legitimate apps to capture credentials, requiring SYSTEM_ALERT_WINDOW permission |
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| **Anti-Emulator** | Techniques malware uses to detect sandbox/emulator environments and suppress malicious behavior |
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## Tools & Systems
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- **MobSF**: Automated static and dynamic analysis for initial malware triage
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- **VirusTotal**: Multi-engine malware scanning and hash reputation lookup
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- **Frida**: Runtime behavior monitoring through method hooking
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- **Wireshark**: Network traffic analysis for C2 communication patterns
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- **Cuckoo Sandbox / CuckooDroid**: Automated malware analysis sandbox for Android samples
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## Common Pitfalls
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- **Anti-analysis evasion**: Sophisticated malware detects emulators, debuggers, and Frida. Use hardware devices and stealthy Frida configurations for accurate analysis.
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- **Time-delayed payloads**: Some malware activates only after a delay or specific trigger. Monitor for extended periods and simulate various conditions.
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- **Encrypted C2**: Malware using encrypted communications requires TLS interception or memory inspection to observe payload content.
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- **Multi-stage payloads**: Initial APK may be benign; malicious payload downloads later. Monitor for dynamic code loading and file downloads.
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