🔒Cybersecurity and Cryptography Unit 6 – Malware Analysis & Intrusion Detection

What's the Big Deal?

• Malware and intrusions pose significant threats to individuals, organizations, and critical infrastructure
• Financial losses due to malware and intrusions reached billions of dollars annually (global cybercrime costs projected to reach $10.5 trillion by 2025)
• Malware can steal sensitive data, disrupt operations, and damage reputation
  • Data breaches caused by malware can lead to identity theft and financial fraud
  • Ransomware attacks can encrypt critical files and demand payment for decryption
• Intrusions allow unauthorized access to systems and networks, enabling attackers to steal data or launch further attacks
• Early detection and analysis of malware and intrusions are crucial for minimizing damage and preventing future incidents
• Understanding the tactics, techniques, and procedures (TTPs) of adversaries helps improve defensive strategies
• Malware analysis and intrusion detection skills are in high demand in the cybersecurity industry

Key Concepts and Terminology

• Malware: malicious software designed to harm, disrupt, or gain unauthorized access to computer systems
• Intrusion: unauthorized access or activity on a computer system or network
• Indicators of Compromise (IOCs): forensic artifacts that indicate a system has been compromised by malware or an intrusion
• Static analysis: examining malware code without executing it to understand its functionality and characteristics
• Dynamic analysis: executing malware in a controlled environment to observe its behavior and effects on a system
• Sandbox: an isolated environment used to safely run and analyze malware without risking infection of production systems
• Signature-based detection: identifying malware or intrusions based on known patterns or characteristics
• Anomaly-based detection: identifying malware or intrusions based on deviations from normal system behavior
• False positive: an alert or detection that incorrectly identifies benign activity as malicious
• False negative: a failure to detect actual malicious activity

Types of Malware: Know Your Enemy

• Viruses: self-replicating malware that spreads by infecting other files or programs
  • File infector viruses attach themselves to executable files and spread when the infected file is run
  • Boot sector viruses infect the master boot record (MBR) or boot sector of a drive
• Worms: self-replicating malware that spreads independently across networks without requiring human interaction
• Trojans: malware disguised as legitimate software, tricking users into installing and executing it
  • Remote Access Trojans (RATs) provide attackers with remote control over infected systems
• Ransomware: malware that encrypts files and demands payment for decryption
  • Locky and WannaCry are examples of notorious ransomware campaigns
• Spyware: malware that stealthily collects information about users and their activities
• Adware: malware that displays unwanted advertisements and can redirect users to malicious websites
• Rootkits: malware designed to hide its presence and provide privileged access to attackers
• Botnets: networks of compromised devices controlled by attackers to launch coordinated attacks or distribute malware

Malware Analysis Techniques

• Static analysis techniques:
  • String analysis: examining plaintext strings in malware code for clues about its functionality
  • File format analysis: inspecting the structure and headers of malware files for anomalies or suspicious characteristics
  • Disassembly: converting malware binary code into human-readable assembly language for analysis
• Dynamic analysis techniques:
  • Behavioral analysis: observing the actions and effects of malware on a system during execution
  • Network analysis: monitoring the network traffic generated by malware for suspicious connections or data exfiltration
  • Memory analysis: examining the memory of an infected system to identify malware artifacts and behavior
• Hybrid analysis: combining static and dynamic analysis techniques for a more comprehensive understanding of malware
• Automated analysis: using tools and sandboxes to perform large-scale analysis of malware samples
• Manual analysis: in-depth examination of malware by skilled analysts for complex or evasive samples

Intrusion Detection Systems (IDS)

• Network-based IDS (NIDS): monitor network traffic for signs of malicious activity or policy violations
  • Placed at strategic points within a network to inspect traffic between devices
  • Can detect attacks such as port scans, denial-of-service attempts, and exploitation of network vulnerabilities
• Host-based IDS (HIDS): monitor activity on individual hosts or devices for signs of intrusion or malware
  • Analyze system logs, file modifications, and process behavior for suspicious activity
  • Can detect attacks such as privilege escalation, unauthorized access attempts, and malware execution
• Signature-based IDS: compare network traffic or system activity against a database of known attack patterns
  • Effective at detecting known threats but may miss novel or modified attacks
• Anomaly-based IDS: establish a baseline of normal behavior and alert on deviations from that baseline
  • Can potentially detect previously unknown or "zero-day" attacks
  • May generate more false positives compared to signature-based approaches
• IDS placement: strategic deployment of IDS sensors to maximize visibility and minimize blind spots
  • Network perimeter, critical network segments, and key hosts are common placement points

Tools of the Trade

• Disassemblers and debuggers: tools for analyzing malware code and behavior (IDA Pro, OllyDbg, WinDbg)
• Network analyzers: tools for capturing and inspecting network traffic generated by malware (Wireshark, tcpdump)
• Sandboxes: isolated environments for safely executing and analyzing malware (Cuckoo Sandbox, Joe Sandbox)
• Reverse engineering frameworks: integrated toolsets for malware analysis and reverse engineering (Ghidra, Radare2)
• Memory forensics tools: tools for analyzing memory dumps of infected systems (Volatility, Rekall)
• Intrusion detection systems: software or appliances for monitoring network and host activity (Snort, Suricata, OSSEC)
• Threat intelligence platforms: services that provide information on emerging threats and IOCs (VirusTotal, AlienVault OTX)
• Malware classification and clustering tools: tools for grouping similar malware samples based on shared characteristics (YARA, ssdeep)

Real-World Examples and Case Studies

• Stuxnet: a sophisticated worm that targeted industrial control systems, specifically Iranian nuclear centrifuges
  • Demonstrated the potential for malware to cause physical damage in the real world
  • Highlighted the importance of securing critical infrastructure and industrial systems
• WannaCry: a global ransomware attack that exploited a vulnerability in Windows SMB protocol
  • Spread rapidly across networks, encrypting files and demanding bitcoin payments for decryption
  • Emphasized the need for timely patching and maintaining secure backups
• Target data breach: a high-profile intrusion that compromised millions of customer credit card details
  • Attackers gained initial access through a third-party HVAC vendor's credentials
  • Underscored the importance of supply chain security and prompt detection of intrusions
• Carbanak: a cybercriminal group that targeted financial institutions, stealing over $1 billion
  • Used targeted malware and social engineering to infiltrate banks' networks and manipulate transactions
  • Demonstrated the sophistication and persistence of modern threat actors

Staying One Step Ahead: Future Trends

• Increasing use of machine learning and AI in malware development and detection
  • Malware authors leveraging AI to create more evasive and adaptive malware
  • Defenders applying machine learning techniques to improve malware classification and anomaly detection
• Growth of Internet of Things (IoT) devices as targets for malware and intrusions
  • Many IoT devices have weak security controls and are not regularly updated
  • Compromised IoT devices can be used for DDoS attacks, data exfiltration, and as entry points into networks
• Continued evolution of ransomware tactics and targets
  • Ransomware-as-a-Service (RaaS) models lowering the barrier to entry for attackers
  • Targeting of critical infrastructure, healthcare, and other high-value sectors for higher ransom demands
• Emphasis on proactive threat hunting and incident response
  • Shifting from purely reactive detection to proactively searching for signs of compromise
  • Developing incident response plans and conducting regular exercises to improve preparedness
• Need for collaboration and information sharing among defenders
  • Participating in threat intelligence sharing communities and initiatives (ISACs, CERTs)
  • Leveraging shared knowledge and resources to better defend against evolving threats