Controlling my Air Purifier

The air pollution in Sofia is really high during the winter, so I decided to buy an air purifier for my home. After some short research, I bought Philips AC2729:

it can purify and humidify the air at the same time, show particulate matter (PM 2.5) / humidity, and features quiet sleep mode. It also has a Wi-Fi interface which allows remote control with a mobile app. Being able to control the device with my phone sounds pretty cool but after reading so many stories about the so called Internet of Shit, I thought it’d be a good idea to inspect what kind of data this thing sends and ultimately create my own tool to control it. It’d be a fun side project as well :)

Network analysis

The first step of course is capturing the network traffic that comes in and goes out from the device. There are several ways to do this with a PC and good Wi-Fi adapter but I took advantage of the built-in packet sniffer that my router has and recorded the traffic during the initial setup of the purifier and some interactions with the mobile app. Then I opened the recorded .pcap file in Wireshark and quickly realized couple of things:

  • the device is sending data to a Philips server (, no surprise here …
  • the device runs an HTTP server and is controlled via some REST API from the mobile app
  • it uses plain HTTP with some custom encryption for the request/response body

I can easily disable the phone-home functionality by putting a firewall rule in my router and limit all traffic to my local network. But I also want to understand how the device is controlled and for this I need to decrypt the network traffic between the purifier and the mobile app.

Let’s take a closer look to the initial interaction between those two parties:

PUT /di/v1/products/0/security HTTP/1.1
content-type: application/json
connection: close
User-Agent: Dalvik/2.1.0 (Linux; U; Android 9; Pixel XL Build/PQ1A.181205.002.A1)
Accept-Encoding: gzip
Content-Length: 269


HTTP/1.1 200
Content-Length: 343
Content-Type: application/json
Connection: Close


GET /di/v1/products/1/device HTTP/1.1
content-type: application/json
connection: close
User-Agent: Dalvik/2.1.0 (Linux; U; Android 9; Pixel XL Build/PQ1A.181205.002.A1)
Accept-Encoding: gzip

HTTP/1.1 200
Content-Length: 128
Content-Type: application/octet-stream
Content-Transfer-Encoding: base64
Connection: Close


POST /di/v1/products/1/air HTTP/1.1
content-type: application/json
connection: close
User-Agent: Dalvik/2.1.0 (Linux; U; Android 9; Pixel XL Build/PQ1A.181205.002.A1)
Accept-Encoding: gzip
Content-Length: 65


HTTP/1.1 200
Content-Length: 256
Content-Type: application/octet-stream
Content-Transfer-Encoding: base64
Connection: Close


The first request to /di/v1/products/0/security is plain text and it seems to be a Diffie-Hellman key exchange (truncated by me for readability). The following requests are base64 encoded and decoding them doesn’t produce anything meaningful. So it looks like a secret key is exchanged at the beginning and then used to encrypt further communication. There is only one way to verify this theory – reverse engineering the mobile app!

Reversing the original app

The mobile app is Air Matters and I downloaded its Android version which is an apk file. Then I used the jadx tool to decompile it. With a few greps over the decompiled sources, I found the relevant encryption/decryption classes:


With this I have been able to recover the following steps:

  1. The app and the device establish a shared secret using the Diffie-Hellman algorithm
  2. The device generates a session key and encrypts it with AES using the shared secret from step 1 as a key. The encrypted session key is set in the key property of the response message from PUT /di/v1/products/0/security
  3. The session key from step 2 is used to encrypt/decrypt with AES all further requests and responses

I also found the G and P values used for Diffie-Hellman in

static final String GVALUE = "A4D1CBD5C3FD34126765A442EFB99905F8104DD258AC507FD6406CFF14266D31266FEA1E5C41564B777E690F5504F213160217B4B01B886A5E91547F9E2749F4D7FBD7D3B9A92EE1909D0D2263F80A76A6A24C087A091F531DBF0A0169B6A28AD662A4D18E73AFA32D779D5918D08BC8858F4DCEF97C2A24855E6EEB22B3B2E5";
static final String PVALUE = "B10B8F96A080E01DDE92DE5EAE5D54EC52C99FBCFB06A3C69A6A9DCA52D23B616073E28675A23D189838EF1E2EE652C013ECB4AEA906112324975C3CD49B83BFACCBDD7D90C4BD7098488E9C219A73724EFFD6FAE5644738FAA31A4FF55BCCC0A151AF5F0DC8B4BD45BF37DF365C1A65E68CFDA76D4DA708DF1FB2BC2E4A4371";

However, this is not enough to find the shared secret which is being established. In fact, G and P are public values. Cracking Diffie-Hellman boils down to solving the discrete logarithm problem which is to find a value "a" such that G^a mod P=A. We already have G and P from the decompiled source and we also have A which corresponds to the "diffie" property in the network request. The shared secret is equal to B^a mod P where B corresponds to the "hellman" property in the network response. The value "a" is by definition a big random number which makes solving G^a mod P=A very hard. Let’s see how this random exponent is generated in

public static String generateRandomNum() {
    return String.valueOf((new SecureRandom().nextInt(2147483546) + 1) + 101);

It seems this random number is only 31 bits. It also seems that the person who wrote this didn’t know what he is doing :) Solving the discrete logarithm problem when a is only 31 bits turns out to be not hard at all. In fact, it can be solved for less than a second using the Baby-step Giant-step algorithm. Check out my implementation at

Open source client

After finding the session key and decrypting the entire traffic, I have been able to implement my own fully-featured command line client. Check this out:

$ ./
[pwr]   Power: ON
[pm25]  PM25: 4
[rh]    Humidity: 32
[rhset] Target humidity: 60
[iaql]  Allergen index: 1
[temp]  Temperature: 22
[func]  Function: Purification & Humidification
[mode]  Mode: M
[om]    Fan speed: 2
[aqil]  Light brightness: 100
[wl]    Water level: 100
[cl]    Child lock: False

As an added bonus, it also shows the current temperature which is missing in the original mobile app :)

Future work

As I said the purifier has a phone-home functionality which is using another form of custom encryption but this time implemented in a native library. Reversing this part will take more time but something tells me it may allow doing some crazy stuff ;)

Stay tuned!

UPDATE (Dec 2019): I also reverse engineered how purifiers are controlled over the internet.