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Increased network buffer size to 33792 bytes.

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Signed-off-by: Toni Uhlig <matzeton@googlemail.com>
This commit is contained in:
Toni Uhlig
2022-10-24 21:22:01 +02:00
parent 2d14509f04
commit 805aef5de8
4 changed files with 13 additions and 10 deletions
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2
config.h
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@@ -11,7 +11,7 @@
* NOTE: Buffer size needs to keep in sync with other implementations
* e.g. dependencies/nDPIsrvd.py
*/
#define NETWORK_BUFFER_MAX_SIZE 32768u /* 8192 + 8192 + 8192 + 8192 */
#define NETWORK_BUFFER_MAX_SIZE 33792u /* 8192 + 8192 + 8192 + 8192 + 1024 */
#define NETWORK_BUFFER_LENGTH_DIGITS 5u
#define NETWORK_BUFFER_LENGTH_DIGITS_STR "5"

10
dependencies/nDPIsrvd.py vendored
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@@ -21,7 +21,7 @@ DEFAULT_PORT = 7000
DEFAULT_UNIX = '/tmp/ndpid-distributor.sock'
NETWORK_BUFFER_MIN_SIZE = 6 # NETWORK_BUFFER_LENGTH_DIGITS + 1
NETWORK_BUFFER_MAX_SIZE = 32768 # Please keep this value in sync with the one in config.h
NETWORK_BUFFER_MAX_SIZE = 33792 # Please keep this value in sync with the one in config.h
PKT_TYPE_ETH_IP4 = 0x0800
PKT_TYPE_ETH_IP6 = 0x86DD
@@ -417,7 +417,13 @@ class nDPIsrvdSocket:
index = 0
for received_line in self.lines:
try:
json_dict = json.loads(received_line[0].decode('ascii', errors='replace'), strict=True)
except json.decoder.JSONDecodeError as err:
sys.stderr.write('\nFATAL: JSON decode failed at line "{}"\n'.format(received_line[0].decode('ascii', errors='replace')))
sys.stderr.write('\n{}\n'.format(str(err)))
retval = False
instance = self.flow_mgr.getInstance(json_dict)
if instance is None:
retval = False
@@ -474,7 +480,7 @@ def validateAddress(args):
address = None
if args.host is None:
address_tcpip = (DEFAULT_HOST, DEFAULT_PORT)
address_tcpip = (DEFAULT_HOST, args.port)
else:
address_tcpip = (args.host, args.port)
tcp_addr_set = True

7
examples/README.md
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@@ -37,15 +37,12 @@ Prints prettyfied information about flow events.
Use sklearn together with CSVs created with **c-analysed** to train and predict DPI detections.
Try it with: `./examples/py-machine-learning/sklearn-ml.py --csv ./ndpi-analysed.csv --proto-class tls.youtube --proto-class tls.github --proto-class tls.spotify --proto-class tls.facebook --proto-class tls.instagram --proto-class tls.doh_dot --proto-class quic --proto-class icmp`
Try it with: `./examples/py-machine-learning/sklearn_random_forest.py --csv ./ndpi-analysed.csv --proto-class tls.youtube --proto-class tls.github --proto-class tls.spotify --proto-class tls.facebook --proto-class tls.instagram --proto-class tls.doh_dot --proto-class quic --proto-class icmp`
This way you should get 9 different classification classes.
You may notice that some classes e.g. TLS protocol classifications may have a higher false-negative rate.
Unfortunately, I can not provide any datasets due to some privacy concerns.
But you can use a [pre-trained model](https://drive.google.com/file/d/1KEwbP-Gx7KJr54wNoa63I56VI4USCAPL/view?usp=sharing) with `--load-model` using python-joblib.
Please send me your CSV files to improve the model. I will treat those files confidential.
They'll only be used for the training process and purged afterwards.
But you can use a [pre-trained model](https://drive.google.com/file/d/1KEwbP-Gx7KJr54wNoa63I56VI4USCAPL/view?usp=sharing) with `--load-model` and the aformentioned parameters.
## py-flow-dashboard

2
nDPId-test.c
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@@ -1227,7 +1227,7 @@ int main(int argc, char ** argv)
if (THREADS_RETURNED_ERROR() != 0)
{
char const * which_thread = "Unknown";
int thread_errno;
int thread_errno = 0;
if (nDPId_return.thread_return_value.val != 0)
{