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How to Evade Intrusion Detection Systems

Mike Blinkman
9 min readMay 25, 2024

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Photo by Roger Starnes Sr on Unsplash

Explore how Intrusion Detection Systems (IDS) safeguard networks by detecting and reporting cyber threats, and the key types and techniques used.

Intrusion Detection Overview

An Intrusion Detection System (IDS) is a software application or device that monitors a network or systems for malicious activity or policy violations, serving as a digital watchtower continuously scanning for anomalies that may signal an intrusion. It detects potential attacks against applications, networks, or computers and reports them to the administrator, but it does not have the capability to automatically prevent these attacks from occurring. IDS is a listen-only device that analyzes a copy of the traffic stream to avoid interfering with network performance while carrying out its surveillance tasks (Tookitaki, InstaSafe Blog, Palo Alto Networks).

There are six types of IDS which are as follows (ISJ):

  1. Network-based IDS,
  2. Host-based IDS,
  3. Wireless IDS,
  4. Network Behaviour Analysis (NBA),
  5. Signature-based IDS, and
  6. Anomaly-based IDS.

An IDS works by employing predefined algorithms, signatures, and behavioural patterns to detect anomalies or potential security threats in real-time. It monitors incoming and outgoing traffic to identify unauthorized access, malicious activities, or deviations from established norms. The IDS utilizes sophisticated algorithms, predefined rules, and behavioral models to scrutinize network or system activities in real-time, analyzing data packets for deviations from expected patterns, signatures of known threats, or anomalies indicating potential security breaches. IDS can operate through signature-based detection, comparing incoming data packets against a database of known attack signatures, or anomaly-based detection, which establishes a baseline of normal network behavior and raises alerts upon detecting deviations from this baseline (ISJ, InstaSafe Blog, Exeon).

Implementing an effective Intrusion Detection System offers several key benefits, including early threat detection, improvement in incident response, and protection of sensitive data. By continuously monitoring network activities, IDS enables early detection of potential threats, preventing cyber incidents before they escalate. IDS also provides security teams with real-time alerts and valuable insights, facilitating swift and effective incident response to mitigate potential damages. Additionally, IDS plays a vital role in safeguarding sensitive data by identifying unauthorized access attempts, thus preventing data breaches and unauthorized data access (Exeon, Stamus Networks).

Common features of an Intrusion Detection System (IDS) include real-time analysis of network traffic, the ability to analyze incoming and outgoing traffic for potential threats, detection of network anomalies for both device-level threats and threats to the network itself, deployment in cloud environments, and effectiveness against modern cyber threats when using advanced systems with Anomaly-based detection capabilities. Additionally, a multi-faceted hybrid system approach is recommended, incorporating complementary building blocks such as EDR, NDR, specialized security tools for important assets, and integration with a Security Operations Center (SOC) and possibly a SOAR-type tool for orchestration (InstaSafe Blog, LinkedIn Advice).

Types of IDS

Network-based Intrusion Detection Systems (NIDS) monitor network traffic for suspicious activities and potential threats, focusing on the traffic that traverses the network segment they are installed on.

Host-based Intrusion Detection Systems (HIDS) are deployed on individual hosts or devices to monitor and analyze activity on that specific host, looking for signs of unauthorized access or abnormal behavior locally (Institute of Data, Boris Gigovic).

Wireless Intrusion Detection Systems (WIDS) work similarly to traditional Intrusion Detection Systems, but with a focus on monitoring wireless networks. WIDS employ both signature-based detection and anomaly-based detection methods to identify potential threats and malicious activities within wireless networks. Signature-based detection relies on a database of known attack signatures to compare incoming data packets for matches, triggering alerts when threats are detected. Anomaly-based detection establishes a baseline of normal wireless network behavior and raises alerts when deviations from this baseline are detected, indicating potential intrusion attempts or suspicious activities. WIDS continuously monitor wireless network traffic, analyze data packets for anomalies or security threats, and generate alerts to enable timely intervention against potential cyber threats (Exeon, Nmap, ISJ).

Network behavior analysis systems (NBAS) work by employing predefined algorithms, signatures, and behavioral patterns to detect anomalies or potential security threats in real-time. These systems monitor incoming and outgoing traffic to identify unauthorized access, malicious activities, or deviations from established norms. By continuously surveilling network or system activities, network behavior analysis systems scrutinize data packets for any deviations from expected patterns, signatures of known threats, or anomalies indicative of potential security breaches. This meticulous process enables these systems to generate alerts or notifications upon detecting suspicious patterns, facilitating timely intervention (ISJ).

Signature-based Intrusion Detection Systems (SIDS) work by searching for specific patterns or signatures in network traffic or system activity that match known attack patterns or malicious instructions used by malware. These signatures are pre-defined and stored in a database, and the IDS compares incoming data to these signatures to identify any malicious activity (Wikipedia).

Anomaly-based IDS work by establishing a baseline of normal network activity through machine learning and then monitoring network traffic for any deviations from this established baseline. When any activity is detected that significantly deviates from the normal behavior, such as unusual login times or a flood of new IP addresses attempting to connect to the network, the IDS raises an alert as a potential security concern (N-able). This approach is effective in detecting zero-day exploits that signature-based detection may miss, as anomaly-based IDS do not rely on known attack signatures but rather on detecting any abnormal behavior on the network (IBM). However, one of the drawbacks of anomaly-based IDS is the increased likelihood of false positives due to non-malicious behaviors also triggering alerts, which can result in additional time and resources being needed to investigate all the alerts raised (N-able).

Bypassing and Evading IDS

Hackers avoid detection by Intrusion Detection Systems by utilizing techniques such as sending manipulated packets to the target in a way that can evade the IDS, and by employing advanced evasion methods to sidestep security measures. Additionally, hackers may develop systems for detecting and deceiving the IDS, as part of the ongoing battle between network administrators and malicious hackers in the cybersecurity realm (Anmol Singh Yadav, Nmap).

Some evasion techniques used by hackers to bypass Intrusion Detection Systems include packet fragmentation, distributed denial-of-service (DDoS) attacks, spoofing, fragmentation, encryption, and operator fatigue (IBM, Anmol Singh Yadav).

Machine Learning in IDS

Machine learning plays a vital role in intrusion detection systems by leveraging its ability to adapt, learn from vast amounts of data, and identify anomalies. This allows organizations to bolster their cybersecurity defenses, mitigate risks, and stay ahead of potential intruders, ensuring the security of digital environments in the face of evolving cyber threats (Cyber Tech Club). Machine learning algorithms are utilized to analyze system logs, network traffic, and other relevant data to detect suspicious activities and potential threats, revolutionizing intrusion detection (MoldStud). Despite challenges, integrating machine learning into intrusion detection systems is crucial for protecting sensitive data and detecting cyber threats in the ever-evolving digital landscape (Cyber Tech Club).

IDS can be optimized to detect advanced threats by incorporating machine learning algorithms such as:

  • Decision Trees (DT),
  • K Nearest Neighbors (KNN),
  • Neural Networks (NN),
  • Bayesian Networks (BN), and
  • Support Vector Machines (SVM).

These algorithms offer unique characteristics and features that enhance IDS performance and accuracy in detecting network-based threats like R2L and DoS attacks. Additionally, by combining IDS with ML and AI, the detection accuracy can be significantly improved by uncovering hidden threats in real-time and identifying unknown anomalous behaviors in networks. Other strategies for optimizing IDS include selecting the appropriate parameters to improve detection accuracy, utilizing hybrid data optimization based on ML algorithms, implementing data sampling techniques to isolate outliers, and ensuring a proper modeling strategy is in place (ECCouncil).

Challenges and Considerations

The limitations of IDS include false alarms leading to alert fatigue due to the high volume of alerts received by enterprises, limited visibility focusing on the perimeter attack surface, false positives and false negatives which may result in either missing genuine security threats or identifying non-existent threats, dependence on regular signature updates to detect new attack patterns, and the potential incapability of signature-based IDS to effectively detect modern cyber threats such as zero-day exploits. Integrating IDS with existing security infrastructure, ongoing monitoring and analysis of network traffic, and deploying advanced IDS systems with Anomaly-based detection can help overcome some of these limitations (Aria Cybersecurity, Exeon, InstaSafe Blog).

Key considerations when deploying an Intrusion Detection System in a network include selecting a product that meets business requirements, functions correctly within the network infrastructure, and can be supported by current personnel. It is advised to adhere to industry standards by deploying both network-based and host-based IDS to effectively monitor network traffic for malicious activities. Additionally, ensuring that detection tools are linked to the Security Operations Center (SOC) and possibly to a Security Orchestration, Automation, and Response (SOAR) tool for efficient orchestration is crucial. Monitoring the IDS alerts and responses is essential to ensure that the system is not ignored and is utilized effectively in responding to potential threats in real time. Finally, when selecting an IDS, organizations should look for a system that not only detects threats but also evolves with the company to provide long-term security solutions (TechTarget, LinkedIn Advice, MoldStud).

Market and Tools

Popular Intrusion Detection System tools in the market include CrowdStrike Falcon (TrustRadius), Clearnetwork’s Managed SOC Service (ClearNetwork), and Clearnetwork’s Managed CrowdStrike EDR service (ClearNetwork).

CrowdStrike Falcon is one of the top-rated intrusion detection software in 2021, offering the Falcon Endpoint Protection suite with features emphasizing threat detection, machine learning malware detection, and signature-free updating (TrustRadius).

When considering intrusion detection software, it is important to weigh the benefits of standalone, specialized tools versus larger security platforms that bundle intrusion detection with other features like firewalls or SIEM systems. Standalone intrusion detection systems typically start at $1,000–2,000 and can scale up to $10,000+, while the pricing for systems that are part of a larger security suite will vary (TrustRadius). Additionally, it is essential to evaluate the specific features offered by different intrusion detection software, such as threat detection, machine learning malware detection, and intrusion prevention tools (ECCouncil).

Intrusion Detection Systems are pivotal components of cybersecurity, offering comprehensive monitoring and detection of malicious activities and policy violations within network environments. Through the use of diverse types ranging from Network-based to Anomaly-based IDS, and techniques such as machine learning, these systems facilitate early threat detection, enhance incident response, and safeguard sensitive data. Despite facing challenges like false positives and evasion techniques employed by hackers, ongoing advancements and integration with other security measures can augment their effectiveness. As cyber threats evolve, the optimization of IDS via advanced algorithms and hybrid approaches remains crucial. For optimal deployment, organizations must consider IDS configurations that align with their specific security requirements and existing infrastructure, ensuring robust defense mechanisms are in place to combat potential cyber threats effectively.

References

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Machine Learning
AI
Hacking
Cybersecurity
Security

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Mike Blinkman
Mike Blinkman

Written by Mike Blinkman

76 Followers
8 Following

Cybersecurity blogger dissecting vulnerabilities and exploits in well-known and well-used systems to demonstrate both hacking and mitigation strategies.

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