eBPF-DSA Security Monitoring Framework

Introduction

Real-time security monitoring architecture combining eBPF kernel observability and Data Structures & Algorithms (DSA) for scalable threat detection.

Overview

This project presents a next-generation security monitoring framework designed for modern systems such as AI-driven applications, cloud infrastructure, and distributed environments.

The architecture integrates:

eBPF (Extended Berkeley Packet Filter) for kernel-level telemetry collection Data Structures & Algorithms (DSA) for scalable and deterministic analysis

Unlike traditional security systems that rely on static logs or rule-based detection, this framework transforms security into a data-driven, algorithmic problem.

• Kernel Event Capture (eBPF Instrumentation): Use the eBPF program to intercept bot-related events (e.g., network connections, authentication calls, etc.) in real time at the kernel level. For example, tracepoint:syscalls:sys_enter_connect or kprobe/tcp_connect to monitor connection attempts to the bot server, and send events to user space via BPF_PERF_OUTPUT 1 2. Use a BPF_HASH map inside the kernel to store session information by PID, You can accumulate the number of authentication attempts.

// trace_connect.c (eBPF): Detecting connect() calls and sending events to userspace

#include <uapi/linux/ptrace.h>
BPF_PERF_OUTPUT(events);

struct data_t {
    u32 pid;
    u64 ts;
};

int trace_connect(struct pt_regs *ctx) {
    struct data_t evt = {};
    evt.pid = bpf_get_current_pid_tgid();
    evt.ts  = bpf_ktime_get_ns();
    events.perf_submit(ctx, &evt, sizeof(evt));
    return 0;
}

• User space detection (DSA-based analysis): Listen to events sent from the kernel using BCC/PyBPF, etc. In user space, track session status (e.g., the number of authentication failures by PID) with a hash map, and detect repeated attempts over a short period of time with a sliding window. For example, if a session has more than 5 connection attempts within 10 seconds, it can trigger an alert. By introducing a graph data structure, you can model the client-server-API flow as nodes/trunks and detect abnormal paths (bypass, lateral movement). In addition, the State Machine can define the "before/after" state to block access to key functions without authentication, and implement allow and block logic according to the type of request with a simple rule engine.

from bcc import BPF
from collections import defaultdict

# Load eBPF C code into Python BCC
bpf_text = open("trace_connect.c").read()
b = BPF(text=bpf_text)
b.attach_kprobe(event="__arm64_sys_connect", fn_name="trace_connect")
# __arm64_sys_connect can be altered by __x64_sys_connect

# sessions = {}   # {pid: [timestamp_list]}
sessions = defaultdict(list)   # Hash Map (key: pid, value: timestamp list)   

def handle_event(cpu, data, size):
    evt = b["events"].event(data)
    pid = evt.pid; ts = evt.ts / 1e9  # ns -> s
    sessions.setdefault(pid, []).append(ts)
    # Warning for 5 attempts within 10 seconds
    window = [t for t in sessions[pid] if ts - t < 10]
    if len(window) >= 5:
        print(f"!! Detect duplicate connections
: PID={pid}, Count={len(window)}")
b["events"].open_perf_buffer(handle_event)

while True:
    b.kprobe_poll()

• Linked design: eBPF is responsible for real-time data collection with low overhead, while DSA (Data Structure and Algorithm) efficiently analyzes the collected data. For example, by quickly processing kernel events obtained by eBPF with in-memory hashmaps and sliding windows, it can detect anomalies (abnormal authentication, suspicious network flows) of bots with ultra-low latency [1]. This combination enables the implementation of a comprehensive security monitoring system that spans from the kernel/network level to application logic.

[1] [2] Security monitoring case using eBPF
________________________________________
[1] [2] eBPF and Security — Watching Over the Kernel | by Samiksha Khadka | Medium
https://medium.com/@swabhimankhadka2001/ebpf-and-security-watching-over-the-kernel-9e8c24e7dac5