Integrating Deep Learning with Cyber Forensics Bringing Deep

Integrating Deep Learning with Cyber Forensics (Bringing Deep Learning into Cyber Forensics) Presented By : Dr. Nickson M. Karie 1

Dr. Nickson M. Karie - (Ph. D) Department of Computer Science Private Bag No. 4, Kwaluseni M 201. Swaziland. (Phone : +268 765 34638 - Email : nkarie@uniswa. sz) Presented By : Dr. Nickson M. Karie 2

Introduction Every time an individual needs to make a decision in one way or the other you have to engage your brain. A lot goes on in ones brain (Computation) before arriving at a decision. A lot of time is also used when the brain has to go through a lot of data available in order to make only one decision. In the computing word this would be analogous to going through what we now call Big Data. Presented By : Dr. Nickson M. Karie 3

Introduction This is to mean that, your Brain has to analyse this “Big Data” to help you arrive at a particular decision. Remember Big data often comes from multiple sources (like social media, internet search engines, e-commerce platforms, online cinemas, etc. ) and arrives (in your Brain) in multiple formats. Eventually (because of too much data to analyse) you might end up taking a wrong decision as a result of Confusion. Anyone Ever seen How a Confused Brain looks Like? Presented By : Dr. Nickson M. Karie 4

Introduction So. . What did you See First ? ? A duck or Rabbit Presented By : Dr. Nickson M. Karie 5

Confused Brain looks like it. . Now you are Here…. Asking yourself if you are Confused? Presented By : Dr. Nickson M. Karie 6

Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning (DL) is a subset of machine learning (ML) in Artificial Intelligence (AI) that has networks which are capable of learning unsupervised from data that is unstructured or unlabelled. Presented By : Dr. Nickson M. Karie 7

Deep Learning can therefore be thought of as the cutting-edge of the cutting-edge. ML takes some of the core ideas of AI and focuses them on solving realworld problems with neural networks designed to mimic our own decisionmaking. Deep Learning, on the other hand, focuses even more narrowly on a subset of ML tools and techniques, and applies them to solving just about any problem which requires “thought” – human or artificial. Deep Learning has, thus, evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world (social media, internet search engines, e-commerce platforms, online cinemas, etc) Presented By : Dr. Nickson M. Karie 8

Deep Learning The increase in the amount of data available presents us with both opportunities and problems. One of the problems is that, this data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. To help solve this problem of processing Big Data, One of the most common AI techniques used is Machine Learning. Presented By : Dr. Nickson M. Karie 9

Deep Learning Machine learning is a self-adaptive algorithm that gets better and better analysis and patterns with experience or with new added data. Deep learning, however, is a subset of machine learning which utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. Presented By : Dr. Nickson M. Karie 10

Where Deep Learning Meets Cyber Forensics is a branch of forensic science encompassing the recovery and investigation of material found in digital devices, often in relation to cyber crimes. If a digital payments company, For example, wants to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm (Deep Learning) employed will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern. Presented By : Dr. Nickson M. Karie 11

Where Deep Learning Meets Cyber Forensics On a normal day, Minus deep leaning algorithms, the traditional approach to detecting fraud or money laundering in an organisation might rely on the amount of transaction that have happened within a particular period of time. However, with deep learning, a system is able to weed out a fraudulent transaction and will consider more things than just the amount of transactions that have happened. This include: time, geographic location, IP address, type of retailer, and any other feature that is likely to make up a fraudulent activity. Presented By : Dr. Nickson M. Karie 12

Where Deep Learning Meets Cyber Forensics The first layer of the deep learning algorithms is able to process raw data input like the amount of transaction that have happened within a particular period of time and passes it on to the next layer as output. The second layer picks the First layers output information as its input and processes this information by including additional information like the user's IP address and passes on its result to the next layer. 13

Where Deep Learning Meets Cyber Forensics The next layer takes the second layer’s information and includes raw data like geographic location and makes the machine’s pattern even better. This continues across all levels of the neuron network until the best and final output is determined. Remember, any added data like retailer, sender, user, social media events, credit score, IP address, and many other features may take years to connect together if processed by a human being. Not so with Deep Learning. 14

Real Life Applications of Deep Learning Deep learning is used across all industries for a number of different tasks. Examples: Image recognition Medical research tools that explore the possibility of reusing drugs for new ailments Cyber Crimes and/or fraud detection Google voice and image recognition Netflix and Amazon use it to decide what you want to watch or buy next researchers at MIT use it to predict future events or occurrences. Presented By : Dr. Nickson M. Karie 15

Short Demo Presented By : Dr. Nickson M. Karie 16