XDP Algorithm Overview

• XDP algorithm is a high-performance, in-kernel packet filtering technology leveraging eBPF to rapidly detect and mitigate DDoS attacks at the network driver level.
• It employs rate-based filtering with fast eBPF hash maps for per-IP packet tracking and immediate drop decisions, minimizing processing overhead on IoT devices.
• Evaluations on Docker and Raspberry Pi 4 show over 97% attack packet drop rates with negligible latency impact on legitimate traffic.

The eXpress Data Path (XDP) algorithm is a high-performance, in-kernel packet filtering technology that integrates with the extended Berkeley Packet Filter (eBPF) framework. XDP operates at the earliest possible location in the Linux networking subsystem—the network driver RX path—enabling fine-grained, programmable, and resource-efficient processing and mitigation of volumetric network attacks directly on the interface, before standard kernel packet allocation mechanisms are invoked. This architecture achieves rapid response and scalability for defending resource-constrained devices, such as IoT edge hardware, against Distributed Denial of Service (DDoS) threats by employing rate-based filtering and blocklist management in-kernel, optionally coordinated with a user-space controller for persistent policy enforcement and alerting (Tolay, 13 Jul 2025).

1. Algorithmic Design Principles and Placement

The XDP rate-based DDoS mitigation algorithm is engineered to address the unique challenges of DDoS detection and mitigation on constrained IoT endpoints. Its principal objectives are:

• Detecting volumetric attacks (e.g., UDP floods, TCP SYN floods) on a per-source-IP basis.
• Dropping identified attack packets immediately, prior to any socket buffer (sk_buff) allocation, to minimize processing overhead.
• Maintaining a lightweight CPU and memory footprint, ensuring feasibility on low-power platforms such as the Raspberry Pi 4.
• Enabling a hybrid in-kernel/user-space control model: immediate drop and rate estimation in XDP, with extended blocklist management and alerting offloaded to user-space processes.

Functionally, XDP attaches to the earliest RX hook in the driver path, making it possible to analyze, filter, or drop packets at line rate with minimal context switching and memory allocation. This design contrasts with traditional software firewalls or Netfilter/iptables rules, which cannot operate before sk_buff allocation and thus incur significantly more overhead under DDoS conditions (Tolay, 13 Jul 2025).

2. eBPF Map Structures

The XDP algorithm’s state is maintained in fast, in-memory eBPF hash maps, used for per-source rate tracking and optional persistent blocklisting.

Map	Type	Key	Value	Max Entries	Purpose
rate_map	BPF_MAP_TYPE_HASH	__u32 (source IPv4 addr)	flow_metrics {count, start_ns}	1024	Per-IP packet rate/window
block_map	BPF_MAP_TYPE_HASH	__u32 (source IPv4 addr)	__u8 (presence = blocked)	1024	Immediate drop/blocklist

The flow_metrics struct tracks the packet count and the window start timestamp (in nanoseconds) for each source IP. The blocklist (block_map) allows immediate packet dropping for IPs exceeding the permitted rate, supporting persistent blacklisting between the in-kernel and user-space layers (Tolay, 13 Jul 2025).

3. Real-Time Packet Processing Workflow

The end-to-end logic of the XDP DDoS mitigation program can be summarized as follows:

1. Packet Parsing: Extract and validate Ethernet and IPv4 headers to obtain the source address; non-IPv4 packets are immediately passed upwards.
2. Blocklist Check: If the source IP is present in the block_map, the packet is dropped (XDP_DROP).
3. Rate Map Lookup/Insertion: The algorithm queries rate_map for the current IP. Absent entries are initialized with count = 1 and the current timestamp; the packet is passed through (XDP_PASS).
4. Sliding Window Bookkeeping: If the current time minus the stored window start is less than or equal to the configured window size (T_win), the counter is incremented. Otherwise, a new window is started with count reset to 1.
5. Threshold Comparison and Mitigation: If, within the current window, the per-IP packet count exceeds the threshold (θ), the IP is inserted in block_map, a log message is emitted, and the packet is dropped. Otherwise, the updated metrics are saved and the packet is accepted.
6. User-Space Postprocessing (optional): A daemon periodically traverses the eBPF maps to flush entries that have grown stale, typically identified by window timestamps older than a configurable period (now - start_ns > 2·T_win) (Tolay, 13 Jul 2025).

The core mathematical formulation is: an IP $i$ will be dropped if $count_i > θ$, where $count_i$ is the number of packets seen from IP $i$ in the past $T_{win}$ nanoseconds.

4. Parameterization and System Tuning

Critical performance and accuracy factors are determined by key algorithmic parameters:

• Window Size (T_WIN_NS): Typically set to $1 \times 10^9$ ns (1 s). Shorter windows increase reactivity but can penalize bursty benign traffic; longer windows may introduce delayed mitigation.
• Packet Threshold (PKT_THRESH): Empirically chosen as 800 packets per second (pps) to exceed typical IoT workflow rates by a factor of 2–3, ensuring tolerance for routine bursts.
• Map Sizes: Both rate_map and block_map are defaulted to 1024 entries, accommodating expected concurrency while limiting overall memory footprint.
• Cleanup Policy: Map cleanup is triggered by timestamp expiration in user-space, ensuring no indefinite state buildup. Memory usage for 1024 rate entries is approximately 20 KB; the blocklist map requires about 5 KB (Tolay, 13 Jul 2025).

A plausible implication is that the specific threshold and window parameters should be calibrated based on empirical measurements of legitimate traffic patterns in the intended deployment environment to minimize false positives.

5. Performance Evaluation and Empirical Results

Extensive evaluation of the XDP rate-limiting algorithm was performed both in Docker-based simulation and physical Raspberry Pi 4 scenarios. Key findings include:

• Mitigation Efficacy: Under a 100 kpps UDP flood (Docker), XDP dropped approximately 99% of malicious traffic within less than 1 s, while on Raspberry Pi 4 hardware, a 100 Mbps (∼30 kpps) attack was mitigated at over 97% drop rate.
• System Responsiveness: Even under attack, the device remained fully responsive, with one CPU core reaching around 85% utilization (Raspberry Pi 4) for the XDP drop path, and negligible impact on user-space processes.
• Legitimate Traffic Impact: End-to-end added latency was less than 1 ms; legitimate flows were unaffected.
• Resource Overhead: Host CPU utilization for filtering dropped from ∼45% to ∼12% in simulation, demonstrating the efficiency of the early-drop model (Tolay, 13 Jul 2025).

Metric	Docker Simulation	Raspberry Pi 4 Hardware
Attack Rate	~100 kpps	100 Mbps ≈ 30 kpps
Malicious Packets Dropped	~99%	>97%
Device Responsiveness (mitig.)	Fully Responsive	Fully Responsive
CPU Overhead (filtering)	~12% of host CPU	~85% of one core
Impact on Legitimate Traffic	Negligible	≲1 ms latency

6. Implementation Summary

The entire workflow is implemented as a single eBPF/XDP program in C, compiled with clang/LLVM, and loaded onto the desired interface via the XDP attach mechanism. The logic, including map structures, packet parsing, per-IP counters, threshold enforcement, and blocklist management, is self-contained and non-blocking. Deployment on a Raspberry Pi 4 with these settings yielded high efficacy and reliability for in-situ IoT DDoS defense (Tolay, 13 Jul 2025).

A plausible implication is that similar XDP-based algorithms may be generalized to other edge-networking scenarios requiring low-latency, programmable packet handling, with appropriate adaptation of the window and threshold logic to the domain-specific threat model.