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Smart cars are not just machines that can drive; they are more like “computers on wheels,” and they can be even more complex than the computers at home. Technologies such as autonomous driving, intelligent navigation, and remote control make cars smarter, but they also provide opportunities for hackers.
Internet of Vehicles Security has become an issue that cannot be ignored, with threats ranging from vehicle hijacking to data leaks. Today, let’s take a look at how AI can serve as a “moat” for Internet of Vehicles security, helping us fend off these potential risks.
AI Detecting Anomalous Behavior: Leaving Hackers Nowhere to Hide
Hackers have various methods of intrusion, which may include hijacking in-vehicle communications, tampering with data, or even directly taking remote control. Traditional security defenses mainly rely on rule matching, such as setting up a “blacklist” to intercept known attacks. However, in the face of ever-changing attack methods, this approach is like using an old map to find a new route, and it will eventually fail.
The Advantage of AI lies in its ability to use machine learning to detect anomalous behavior, not relying on fixed rules, but rather training the system with a large amount of data to discover “abnormal” situations on its own.
For example, if a vehicle’s CAN bus (the vehicle’s nervous system) suddenly receives an unfamiliar command, such as “sudden braking at high speed,” AI can immediately recognize this as an anomalous operation and take defensive measures, such as preventing the command from executing or sending an alert.
Practical Application: AI-Based Intrusion Detection System (IDS)
Automakers have begun using deep learning to train Intrusion Detection Systems (IDS), for example, using LSTM neural networks to analyze vehicle communication data streams. If a certain ECU (Electronic Control Unit) suddenly sends an unusual command, the system can detect this anomaly within milliseconds and prevent potential attacks.
import tensorflow as tf
from tensorflow import keras
import numpy as np
# Assume we have historical communication data from the vehicle network
data = np.random.rand(1000, 10) # 1000 records, each with 10 features
# Simple LSTM model
model = keras.Sequential([
keras.layers.LSTM(64, input_shape=(10, 1)),
keras.layers.Dense(1, activation='sigmoid') # Output whether it is anomalous
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Train the model (the dataset should be replaced with real automotive network data)
model.fit(data, np.random.randint(0, 2, (1000, 1)), epochs=10)
🚨 Friendly Reminder: In a real environment, the IDS needs to continuously update training data to adapt to new attack methods; otherwise, it may be bypassed by “new hacker attacks”.
AI Encrypted Communication: Making Data Incomprehensible to Hackers
The Internet of Vehicles requires constant data exchange, including navigation, autonomous driving, and remote diagnostics… This information needs to be transmitted between vehicles and the cloud. If the data is not encrypted, hackers can easily obtain the vehicle’s real-time location, driving habits, and even forge commands to take remote control of the vehicle by simply “eavesdropping”.
AI can be used for adaptive encryption, allowing data to automatically adjust encryption strength during transmission, ensuring both security and no slowdown in communication speed. AI can select the optimal encryption strategy, such as AES, ECC, etc., based on the current network environment and computing resources.
Practical Application: AI-Optimized Adaptive Encryption
Traditional encryption algorithms have a high computational overhead, which may affect the response speed of in-vehicle systems. However, AI-based adaptive encryption can dynamically adjust encryption strength, for example:
- When the vehicle is driving at high speed, it prioritizes the real-time transmission of commands, using lightweight encryption;
- When the vehicle is stationary or connected to an insecure network, it automatically switches to high-strength encryption to prevent data leaks.
AI can also learn the attack patterns of hackers and automatically upgrade the encryption level when it detects anomalous access, making it difficult for hackers to “decipher” the data.
from cryptography.fernet import Fernet
# AI can dynamically adjust the key based on the network environment
key = Fernet.generate_key()
cipher = Fernet(key)
message = b"Vehicle GPS coordinates: East 121.4737, North 31.2304"
encrypted = cipher.encrypt(message)
decrypted = cipher.decrypt(encrypted)
print("Encrypted:", encrypted)
print("Decrypted:", decrypted.decode())
🔒 Friendly Reminder: No matter how strong the encryption algorithm is, the key must be well protected! If the key is obtained by hackers, even the most complex encryption is useless.
AI Vulnerability Scanning: Detecting “Internet of Vehicles Security Vulnerabilities” in Advance
The software components involved in the Internet of Vehicles are very complex, from operating systems and communication protocols to various API interfaces. A slight oversight may leave a “backdoor”. Hackers love to exploit these vulnerabilities to “pry open” the system, such as remote code execution vulnerabilities, which may allow hackers to take over your car directly.
Traditional vulnerability scanning relies on manual testing, which is slow, costly, and difficult to cover all scenarios. AI can perform automated vulnerability scanning, combining deep learning + reinforcement learning to continuously test the security of the system, detecting and fixing vulnerabilities in advance.
Practical Application: AI-Driven Automated Penetration Testing
Automakers have begun using AI for automated penetration testing, which can simulate hacker attacks and automatically search for vulnerabilities. AI can automatically generate attack samples to test whether the in-vehicle system is susceptible to SQL injection, buffer overflow, and other attacks.
from fuzzingbook.Fuzzer import RandomFuzzer
# Generate random input to simulate attacks
fuzzer = RandomFuzzer(min_length=10, max_length=100)
for _ in range(5):
attack_payload = fuzzer.fuzz()
print("Simulated attack input:", attack_payload)
💡 Friendly Reminder: Vulnerability scanning is just the first step; timely remediation of discovered vulnerabilities is key! Otherwise, no matter how smart AI is, vulnerabilities will still exist.
AI as the “Guardian” of Internet of Vehicles Security
The Internet of Vehicles makes cars smarter, but it also complicates security challenges. Hacking techniques are constantly evolving, and traditional security measures can no longer keep pace. The addition of AI makes Internet of Vehicles security defenses more proactive, intelligent, and efficient, from intrusion detection, data encryption to vulnerability scanning, AI is becoming the “last line of defense” for automotive safety.
In the future, as AI technology advances, smart cars will not only drive themselves but also protect themselves, being able to automatically detect, defend, and even repair against cyber attacks. For the automotive industry, how to better utilize AI technology to build a safer Internet of Vehicles ecosystem will be a key challenge ahead.
If you want to learn more about the application of AI in automotive safety, consider following AI security research to learn more about the latest technologies to combat hacking attacks.
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