Saad Malik's Projects
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
This repo is all about Andriod/iOS native app development. I will be using Flutter framework with Andriod Studio IDE, language I will be using is Dart.
If you can find the pattern for expected or "normal" data, then you can also find those data points that don't fit the pattern. Companies in industries as diverse as financial services, healthcare, retail and manufacturing regularly employ a variety of data science methods to identify anomalies in their data for uses such as fraud detection, custom
A set of code samples for the Azure Maps web control.
About this Course This course will give you an overview of client-side web UI frameworks, in particular Bootstrap 4. You will learn about grids and responsive design, Bootstrap CSS and JavaScript components. You will learn about CSS preprocessors, Less and Sass. You will also learn the basics of Node.js and NPM and task runners like Grunt and Gulp. At the end of this course, you will be able to a)Set up, design and style a web page using Bootstrap 4 and its components, b) Create a responsive web page design, and c) Make use of web tools to setup and manage web sites. This course also includes an honors track that enables you to work on your own project developing a website using Bootstrap 4. Students enrolling in this course should have prior good working knowledge of HTML, CSS and JavaScript.
These python scripts capture Real-time stream and dump it into pcap file through "Dumpcap" and then convert the pcap files to csv files with the help of "Flowmeter" , after that we merge those csv's into 1 csv file and apply Normalization and preprocessing techniques to make it suitable to feed ML / DL Model.
CICFlowmeter-V4.0 (formerly known as ISCXFlowMeter) is an Ethernet traffic Bi-flow generator and analyzer for anomaly detection that has been used in many Cybersecurity datsets such as Android Adware-General Malware dataset (CICAAGM2017), IPS/IDS dataset (CICIDS2017), Android Malware dataset (CICAndMal2017) and Distributed Denial of Service (CICDDoS2019).
Codes of Assembly Language MASM-32 BIT
Documentation
Use machine learning to classify malware. Malware analysis 101. Set up a cybersecurity lab environment. Learn how to tackle data class imbalance. Unsupervised anomaly detection. End-to-end deep neural networks for malware classification. Create a machine learning Intrusion Detection System (IDS). Employ machine learning for offensive security. Learn how to address False Positive constraints. Break a CAPTCHA system using machine learning.
Malicious Activity Detection System. Final Year Project. Deep Learning-based solution, which analyses Network Activity sequences to classify whether the certain node is Malicious or Benign. Devising a tool/software which will detect malicious Network Activity Detection using Deep Learning Model. Tools: Python, Neural Network (BERT), Google Colaboratory, PyTorch, Kaggle, Tensorflow, and Flowmeter,
This is the finance tracker app
Yet another GitHub profile readme :smiley:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent