Intrusion , Host-Based , Adaptive Detection and Honeypots

What are Intruders ?

• In network security, intruders refer to unauthorized individuals or entities who attempt to gain access to computer systems and networks, without permission.
• Example: A hacker uses sophisticated tools and techniques to exploit vulnerabilities in a company's network infrastructure.
• The intruder gains unauthorized access to sensitive data, such as customer information , etc, with the intention of stealing or manipulating the data for malicious purposes.
• Intrusion detection plays a pivotal role in identifying and blocking these intruders before they can cause harm.

Intrusion Detection Systems (IDS)

• Intrusion Detection Systems (IDS) are security mechanisms that monitor network and system activities to detect and respond to potential security threats.
• They play a crucial role in identifying suspicious behavior and known attack patterns within a network.

• IDS can be divided into two main types:

• Host-Based Intrusion Detection (HIDS)
• Network-Based Intrusion Detection (NIDS).

Intrusion Detection Techniques

• Signature-based IDS
• Anomaly-based IDS
• Behavior-based IDS

Signature-based IDS

• Uses predefined signatures or patterns of known attacks to identify malicious activities in network traffic or system logs.
• Example: Suricata is a signature-based IDS that compares network packets against a database of attack signatures, triggering alerts when a match is found.

Anomaly-based IDS

• Establishes a baseline of normal network or system behavior and raises alerts when deviations from the baseline occur, indicating potential intrusions or abnormal activities.
• Example: Cisco Stealthwatch is an anomaly-based IDS that uses machine learning algorithms to detect abnormal network behavior, such as unusual traffic patterns or unusual resource access.

Behavior-based IDS

• Analyzes the behavior and actions of users, applications, or devices to identify suspicious activities or deviations from normal behavior.
• Example: Darktrace is a behavior-based IDS that uses AI and machine learning to detect and respond to emerging cyber threats by analyzing network traffic, user behavior, and endpoint activities.

Benefits of IDS

Early Threat Detection

Enhanced Security Posture

Compliance Requirements

Alert Prioritization

• IDS generates alerts based on the severity of detected incidents, helping security teams prioritize and focus on critical threats first.
• IDS logs and alerts provide valuable data for forensic analysis and post-incident investigations.

Host Based Intrusion Detection (HIDS)

• Host-Based Intrusion Detection focuses on monitoring the activities and behaviors of individual systems or hosts.
• It analyzes log files, system events, and even file changes to detect anomalies.
• By comparing current behaviors to established baselines, HIDS can raise alarms when it identifies activities that deviate from the norm.
• It provides granular visibility into host-level activities, allowing security teams to detect and respond to intrusions specific to individual systems.
• HIDS monitors and analyzes activities within an individual host, such as servers, workstations and endpoints.
• Example: OSSEC is an open-source HIDS that provides host-based intrusion detection capabilities by monitoring file changes, and other host-level activities.

Distributed Host-Based Intrusion Detection

• Distributed Host-Based Intrusion Detection Systems (HIDS) monitor multiple host systems across a network to detect and respond to potential security threats.
• They combine the capabilities of traditional HIDS with distributed monitoring for enhanced network-wide security.
• It centralizes monitoring and management of distributed HIDS sensors or agents,
• allowing security teams to oversee host-level activities and security events from a centralized console.
• Distributed HIDS employs lightweight agents or sensors installed on individual host systems to monitor host-level activities and network traffic.
• for example: Cisco Secure Endpoint

Network-Based Intrusion Detection (NIDS)

• Network-Based Intrusion Detection focuses on monitoring network traffic to identify suspicious patterns or anomalies.
• It inspects packets of data moving across the network, comparing them to established signatures of known threats.
• NIDS is particularly effective for detecting threats that target multiple hosts or systems.
• It provides real-time monitoring of network activities, allowing security teams to identify and respond to security incidents.
• It helps in early detection of network-based threats, such as , denial-of-service (DoS) attacks, brute-force attempts, etc.
• for example: Suricata, Snort

Distributed Adaptive Intrusion Detection

• Distributed Adaptive Intrusion Detection takes a dynamic approach to threat detection.
• It learns from historical data and adapts its detection mechanisms based on emerging patterns.
• This enables it to identify new and evolving threats that might not yet have established signatures.
• DAID (Distributed Adaptive Intrusion Detection) adapts its detection strategies and rules dynamically to address evolving threats and changing network conditions.
• DAID can automate response actions, such as isolating compromised systems, blocking malicious traffic, or alerting security teams for further investigation.
• Examples of DAID: Darktrace , Cisco.

Intrusion Detection Exchange Format (IDMEF)

• IDMEF is a standardized format for sharing information generated by various intrusion detection systems.
• It facilitates the exchange of insights and alerts between different security tools, allowing for a more coordinated response to potential threats.
• IDMEF includes attributes such as source IP, destination IP to describe security events and alerts.
• IDS platforms generate IDMEF-compliant alerts and event data, which can be shared with other security tools for analysis and action.
• Examples of IDMEF Usage: SIEM Integration and intrusion Detection Systems (IDS)

What are Honeypots ?

• Honeypots are like digital traps designed to lure intruders.
• These systems mimic real servers or applications, but they exist solely to gather information about potential threats.
• They gather valuable information about attacker behavior, tactics, providing insights for analysis and defense.
• They divert attackers away from critical assets and production systems, reducing and minimizing potential impact.
• There are two Types of Honeypots: Low-Interaction Honeypots and High interaction honeypots.
• Example: Kippo and Cowrie capture SSH-based attacks and malicious activities, providing detailed insights into attacker behavior.

Virtual Private Network (VPN)

• Virtual Private Networks (VPNs) play a vital role in securing your data as it travels across the internet.
• VPNs play a crucial role in enhancing network security by providing secure communication channels for remote access and privacy.

Conclusion

• In conclusion, understanding intrusion detection is pivotal in the fight against cyber threats.
• By grasping the concepts of intruders, intrusion detection systems, host-based and network-based detection, adaptive techniques, and tools like honeypots and VPNs,
• Individuals and businesses can take proactive steps to protect their digital assets.