Detecting Denial of Service (DoS) attacks is a crucial aspect of cybersecurity, especially in an era where services are expected to be available 24/7. While DoS attacks may not be as distributed or complex as DDoS (Distributed Denial of Service) attacks, they can still wreak havoc.
DoS detection typically revolves around monitoring network traffic for abnormal patterns or spikes that deviate from the baseline “normal” behavior.
Techniques like rate-based thresholds are commonly employed, where the number of incoming requests from a single source is limited within a given timeframe.
Anomaly detection algorithms can also play a role, learning from historical data to recognize what constitutes normal and abnormal traffic.
Signature-based detection can flag known attack patterns, essentially using past data to predict and identify future threats.
While the approaches may be similar to DDoS detection, the scale is usually smaller, which can sometimes make mitigation a bit simpler.
Additionally, firewall rules, challenge-response tests like CAPTCHAs, and IP blacklisting or whitelisting can also be part of the DoS detection arsenal.
Here’s a rundown of some common DDoS detection strategies:
1. Traffic Analysis: Monitoring network traffic in real-time to identify unusual spikes or patterns. This is typically the first line of defense.
2. Rate-based Thresholds: Setting limits on the number of requests from a single IP address or range. Exceeding this threshold could flag the traffic as potentially malicious.
3. Behavioral Analysis: Instead of just looking at volume, this method considers the behavior of incoming traffic. For instance, if a user is making too many requests that result in errors, it’s likely not legitimate traffic.
4. Challenge-Response Tests: Things like CAPTCHAs can be used to challenge suspected bots. Legitimate users can pass these tests, while automated bots usually cannot.
5. Whitelisting and Blacklisting: Predefined lists of trusted and untrusted IPs can help filter out known bad actors.
6. Anomaly Detection: Statistical models can be used to learn what “normal” traffic looks like, and alert admins when deviations occur.
7. Signature-based Detection: Known attack patterns can be detected by monitoring the unique “signature” characteristics of the attack.
8. Correlation Analysis: Sometimes you need to pull in data from multiple sources like firewalls, IDS, and server logs to get a full picture and identify sophisticated attacks.
9. Third-Party Monitoring Services: Several companies offer DDoS detection as a service, which can provide an additional layer of expertise and tools.
10. Honeypots: These are decoy servers that are set up to attract attackers. Activity on these servers is usually a strong indication of a DDoS attempt (honey pot blog here )
Detection Strategies:
Denial of Service detections is all about leveraging the platform’s robust capabilities to monitor, alert, and analyze real-time data.
You can utilize Kusto Query Language (KQL) to create custom queries that monitor for telltale signs of a DoS attack, like unusual spikes in network traffic or an abnormal number of requests from a single IP.
Microsoft Sentinel’s alerting features can then notify you the moment these predefined conditions are met. What’s cool is that you can also use the platform to correlate this data with other security events, helping you distinguish between false alarms and genuine threats.
All this allows you to react swiftly, perhaps even automating some responses, and gives you a comprehensive toolset for identifying and mitigating DoS attacks as they happen.
Detecting DoS Attack:
For detecting DoS attacks, you can use Azure Sentinel’s Kusto Query Language (KQL) to write custom queries that identify unusually high levels of traffic or resource utilization. Here’s a simple example:
Heartbeat
| summarize count() by RemoteIPCountry, bin(TimeGenerated, 1h)
| where count_ > threshold_value_here
| project TimeGenerated, RemoteIPCountry, count_In this example, you’re counting the number of “heartbeats” from each remote IP by the country and summarizing this per hour. If the count crosses a predetermined threshold, it could be indicative of a DoS attack.
Detecting Data Exfiltration:
For detecting potential data exfiltration, you could look for large or unusual outbound data transfers:
CommonSecurityLog
| where DestinationIP != "Internal_IP_here"
| summarize DataVolume=sum(SentBytes + ReceivedBytes) by DestinationIP, bin(TimeGenerated, 1h)
| where DataVolume > data_threshold_here
| project TimeGenerated, DestinationIP, DataVolume
In this query, you’re summing the sent and received bytes for each external destination IP and flagging any volumes that exceed a given threshold.
Correlating DoS and Data Exfiltration:
One of the strengths of Sentinel is its ability to correlate disparate pieces of data. You could, for instance, write a query that looks for instances where both a potential DoS attack and data exfiltration appear to be occurring within a certain timeframe:
let dos_detection = Heartbeat
| summarize DoSCount=count() by RemoteIPCountry, bin(TimeGenerated, 1h)
| where DoSCount > dos_threshold_here;
let exfil_detection = CommonSecurityLog
| where DestinationIP != "Internal_IP_here"
| summarize DataVolume=sum(SentBytes + ReceivedBytes) by DestinationIP, bin(TimeGenerated, 1h)
| where DataVolume > exfil_threshold_here;
dos_detection
| join kind=inner (exfil_detection) on TimeGenerated
| project TimeGenerated, RemoteIPCountry, DoSCount, DestinationIP, DataVolumeThis query joins the two detection queries on the TimeGenerated field, allowing you to identify timeframes where both a DoS attack and data exfiltration might be happening.
Alerting and Automation:
Once you’ve refined your queries, you can set up Microosft Sentinel alerts to notify you when these patterns are detected. Additionally, you could automate responses using Azure Logic Apps — like isolating a compromised machine, disabling user accounts, or updating firewall rules.
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