Global CCISO Forum 2018 | AI vs Malware 2018
•Download as PPTX, PDF•
3 likes•64 views
This document provides a high-level overview of an AI-based malware detection system. The system uses a multilayer perceptron neural network with over 5 billion nodes that analyzes files and extracts features to determine if files are malicious or clean. It continuously trains and improves itself by processing large volumes of known malware and benign files. The system also has a separate process for introducing new unknown malware samples in a controlled way to generate new features without disrupting the existing network.
Recommended
More Related Content
Similar to Global CCISO Forum 2018 | AI vs Malware 2018
Similar to Global CCISO Forum 2018 | AI vs Malware 2018 (20)
More from EC-Council
More from EC-Council (20)
Recently uploaded
Recently uploaded (20)
Global CCISO Forum 2018 | AI vs Malware 2018
- 1. © Copyright Fortinet Inc. All rights reserved. AI – A Self Evolving Detection System Q3 / 2018
- 2. 2Fortinet - Confidential Introduction 21 years military »USAF – nuclear weapons »USA – FC/test pilot, threat officer, various leadership roles 20 years security management and consulting »NASA JPL, World Bank, CME, Delta Air Lines, Humana, HECO, Honda NA, USAA, Corning, many others »Consulting, Practice Manager, Director of Security, CISO Currently: Strategist at Fortinet »Technology + resource management + business needs = CISO success
- 3. 3Fortinet - Confidential Bingo Time! Deep Analytics Crowdculture Virtual / Augmented Reality Big / Real Big / Way Big Data Holistic Eco_______ Artificial Intelligence Machine Learning Deep learning
- 4. 4Fortinet - Confidential LEVEL II » Content Pattern Recognition » Looks at wrappers and payload for repeats » Handles large volumes of permutations » Proactive in nature, but manually created Malicious File Packed/Encrypted Run time/memory Pack Run Headers 1111010101010 Code 0010101010101 1010101010101 10111101010111 Data 1010101010111 1010101010101 1010101010101 Headers 1111010101010 Code 0010101010101 1010101010101 10111101010111 Data 1010101010111 1010101010101 1010101010101 Headers 1111010101010 Code 0010101010101 1010101010101 10111101010111 Data 1010101010111 1010101010101 1010101010101 Antivirus Evolution LEVEL I » Simple MD5 / SHA 56 computations » Resulted in large DBs for file comparisons » One signature – one piece of malware » Reactive and non-responsive to mutations C:Md5sum malware.exe 5e3830ee3282a53920e00784fec44cfd (malware.exe) Cfac6385a0cdd5f09b2e38c833c93c9d 5ae8c55fbc7b8f5bafa1af1675478cba 1af8e09e41fc850e15ffc4ea0be68c21 ce1ff097a3f0afec3bd5c5f0fb57cfda 80f27e4d562dc4f55e38f4088251e83c bf6ba9baa2e0dcb8d175a4ff594dccd9 5e3830ee3282a53920e00784fec44cfd Malware Found
- 5. 5Fortinet - Confidential AI Visions Facebook ‘emergency shutdown Fear over planet being “Zuckerburged”’ Reality Was different
- 6. 6Fortinet - Confidential Alan Turing called an infant’s mind an ‘unorganized machine’ in 1930s Created early definitions of machine learning » First type (A) consists of simple NAND (negative – AND gates » Second type (B) is combination of A types with modifiers added – results in weighted input/variable output method » Saw the need for: Seeded solution set of accurate or known potential output Population of variably weighted pieces or functions A method for removing the worst solutions while retaining the best Early AI Defined Major inhibitor of his research – was far ahead of available capabilities in terms of computing power.
- 7. 7Fortinet - Confidential Artificial Intelligence – Nearing a Century History of AI Outside Cyber Security Industry 1930s Turing, Kleene and Church propose machine learning solution 1943 McCullouch and Pitts create formal design of Turing’s ‘artificial neurons’ 1956 AI research formally founded as a discipline at Dartmouth College 1960s AI research heavily funded by the U. S. military 1974 The First AI Winter Difficulty resulted in funding cuts in US and Britain 1980s Proliferation of Expert Systems Lisp vs. PC 1990s AI applied to data mining, medical diagnosis with increased CPU power 1997 IBM Deep Blue beats Grand Master Kasparov in chess 2003 Deep learning is achieved using faster computing, large data structures 2011 IBM Watson defeats Jeopardy champions Brad Rutter and Ken Jennings 2013 IBM Watson application for management decisions of lung cancer treatment 2015 2,700+ AI projects in place at Google 2017 Elon Musk calls for the regulation of AI before we hit 100 years
- 8. 8Fortinet - Confidential Supervised Learning – Using known solution sets to embed proper functions and create proper output. » Reinforcement – action on an environment triggers an observation resulting in a defined state. Unsupervised Learning – unknown solution sets » Clustering – group according to similarities. » Dimensionality Reduction – deductive reasoning. » Structured Prediction – random fields are analyzed to predict according to defined output probabilities. » Anomaly Detection – input does not match expectations. Types of Problem Solving Artificial Neural Networks (ANNs) Large collections of simple interconnected nodes (neurons), each with a weighted input and output value. ?!
- 9. 9Fortinet - Confidential Consists of three or more layers » Input layer » One or more hidden layers » Output layer Layers are made of up nodes » Connected to every node in the previous and subsequent layer » Provide discrete processing of input information (files and features) » Produces an output value based on inputs, function, and weighted valuation Type of AI – Artificial Neural Network (Multilayer Perceptron) The Multilayer Perceptron approach provides deep machine learning capabilities. MP behavior is similar to human neurons - if input is strong enough, signal is passed according to weighted value Input layer Hidden layer 1 Hidden layer 2 Output layer Inputs Weights Sum Output Input 1 Input 2 Input 3 YES/NO decision ∑
- 10. 10Fortinet - Confidential Point observable characteristics based on code blocks » Files are parsed when entering the system 1 : 1 relationship with nodes Features are maintained in a knowledgebase repository (features, pattern recognition sigs, other information) Quality is critical » Provides more accurate determination of file status » We leveraged internal legacy samples (~.5PB) to create features from samples Each feature is weighted to assist in decisions Feature weighting can change over time Features f - feature w - weight Func(f1*w1+f2*w2+...+fn*wn) -> {0,1} Feature/Node Algorithm
- 11. 11Fortinet - Confidential System is fed initial data sets for analysis » Supervised machine learning approach Files are broken into code blocks Information (features) extracted during the learning phase. » Binary present within the blocks » Represents behavioral activation The system learns by weight features based on » Surety of indicators (‘tells’) » Frequency of observation Layers + Nodes + Features = Learning Source accuracy of the population input A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
- 12. 12Fortinet - Confidential Features, Nodes & Weights – Single Instance 70 FEATURE 100 wt. NODE +90 FEATURE 100 wt. NODE -20File Result INPUT LAYER HIDDEN LAYER HIDDEN LAYER OUTPUT LAYER File 1. We start with an input file – malicious or clean 2. Feature presence is calculated, re-weighted and passed forward to the next node 3. The analysis is repeated using the next layer feature, then passed to the next node 4. Result – the overall probability based on a score of feature presence
- 13. 13Fortinet - Confidential Features, Nodes & Weights – Multiple Instance FEATURES FEATURES Result INPUT LAYER MALICIOUS LAYER CLEAN LAYER OUTPUT LAYER File
- 14. 14Fortinet - Confidential AI Operational Example - Machine vs. Malware 1 = process the input file 2 = 2.3 billion nodes analyzing potential malicious features 3 = 3.2 Billion nodes analyzing files for clean features 4 = output or decision layer (1 = malicious , 0 = clean) 4 Layer Architecture Consists of separate layers for either malicious or clean feature processing Mathematical models compare samples and features to decide output Input layer Malicious Layer Clean Layer Output layer FortiGuard AI Output is a result of 2.3B x 3.2B individual node computations. Initial capability – 58 samples per second
- 15. 15Fortinet - Confidential System Training FEATURES REPOSITORY TRAINING FILES 1. We start with AI and an empty feature repository 2. A training set of files are input, consisting of clean and malicious files. Files are labeled for initial training 3. Files are input and broken into code blocks. AI logic builds a set of features. 4. Features are modified as the system learns (weighting values, next phase) AI
- 16. 16Fortinet - Confidential System Testing MALICIOUS CLEAN FEATURES REPOSITORY TESTING FILES OUTPUT1. Test samples are selected and input to the system 2. Using the feature repository, samples are analyzed 3. As this occurs, existing features may get modified or others added 4. The system determines clean or malicious output 5. Output is compared to expected results. If not accurate, reset to known point and retrain. RESULTS EXAMINED AI
- 17. 17Fortinet - Confidential AI in Operation MALICIOUS CLEAN OUTPUT INPUT RAW SAMPLES Feature Set Improvements Quality Stabilized Number Weighting Confidence Continued Accuracy to a High Degree of Confidence FEATURESQuantity Quality
- 18. 18Fortinet - Confidential Has to integrate to support current systems Must use features (code blocks), create signatures Continuous, seamless operational support Managing New Malware ?! Problem - how to introduce new samples and create new features New features are naturally regressed in weight – no history Requires false weighting to have value in decisions Cannot subvert existing weighted features Cannot use single samples / false weight Would cause heavy skewing of results Introduces potentially large errors in main system
- 19. 19Fortinet - Confidential New Malware Management System – Part I AI 130+ Threat Feeds Global Sensors CTA 1 New Files 2 Sandbox Non-matching Feature Sets (all code blocks) A ‘rolling’ input is used – last batch + new AI is fast – millions per day Sandbox is slower – 10s/K per day Features created as in original training Features are relative to AI – consist of code blocks, which are also used by CPR AI 3 4
- 20. 20Fortinet - Confidential New Malware Management System – Part II (AI Side) 1 2 3 4 5 6 Knowledgebase Repository Production Feature Set
- 21. 21Fortinet - Confidential New Malware Management System – Part III 2 4 5 Knowledgebase Repository Production CPR Signature Set CB CB CB CB CB CB CB CB CB CB 7Customer Enterprise 1 3 6 CPR Engine 8
- 22. 22Fortinet - Confidential Results • Significantly Increases cybercriminal cost burden for limited return • Obtain unsurpassed accuracy not possible through manual methods • Eliminate manual labor/potential errors • Create autonomous system – operational and continued learning
- 23. 23Fortinet - Confidential Take-Aways • AI as a business solution is not trivial • Large effort to implement • Example ~ 8 years to date • AI should ease technical resource demands • Reallocate to more critical tasks • Hybrid approaches cause more labor Anyone pitching AI without some level of detail is probably BS Bingo selling • Should not have to watch it • Tell it right from wrong? • Lack of trust = poor implementation
- 24. Thanks for your time!
Editor's Notes
- We present a hybrid data mining approach to detect malicious executables. In this approach we identify important features of the malicious and benign executables. These features are used by a classifier to learn a classification model that can distinguish between malicious and benign executables. We construct a novel combination of three different kinds of features: binary n-grams, assembly n-grams, and library function calls. Binary features are extracted from the binary executables, whereas assembly features are extracted from the disassembled executables. The function call features are extracted from the program headers. We also propose an efficient and scalable feature extraction technique. We apply our model on a large corpus of real benign and malicious executables. We extract the abovementioned features from the data and train a classifier using Support Vector Machine. This classifier achieves a very high accuracy and low false positive rate in detecting malicious executables. Our model is compared with other feature-based approaches, and found to be more efficient in terms of detection accuracy and false alarm rate.
- Watson used 200 million pages of structured & unstructured content, 4 terabytes
- Clustering - Task based, used to define probabilities. Walls + air + constant temperature = room. Air + flat surface does not equal room. Dimensionality - determine solution by eliminating noise. Structured prediction - Used for language modeling Anomaly detection - Think bank fraud detection techniques. Reinforcement learning - Used extensively in game theory. Good action = good result, bad action = bad result.
- Different file types are sent to different nodes within each layer, for instance a pdf file will be sent to a different layer than a exe. If we are dealing with EXE files, one of the features that we extract is the code block (assembly code snippets) which should then be normalised (eg extracting patterns that could change between malwares) before hashing them (reduced representation of features)
- Different file types are sent to different nodes within each layer, for instance a pdf file will be sent to a different layer than a exe. If we are dealing with EXE files, one of the features that we extract is the code block (assembly code snippets) which should then be normalised (eg extracting patterns that could change between malwares) before hashing them (reduced representation of features)
- Different file types are sent to different nodes within each layer, for instance a pdf file will be sent to a different layer than a exe. If we are dealing with EXE files, one of the features that we extract is the code block (assembly code snippets) which should then be normalised (eg extracting patterns that could change between malwares) before hashing them (reduced representation of features)
- Training Phase – Malicious and Clean files , flagged as so, are passed through classifier in a feature extraction (collection and selection). We have two sets of data, one that is used for training and one other for testing Both should be the most accurate representation of malware families and behavior to start off with a good initial footprint.
- We ingest new samples from a large array of sources, to include 120 threat feeds (such as STIX, FS-ISAC, VirusTotal, CTA, etc.) and our more than 3 million deployed global sensors. We follow the same methodology as when we trained the larger system by breaking files into code blocks using technologies similar to IDA. The difference is that now we will feed them into 2 smaller AI systems with much smaller nodes and feature set capacity. The first system is imply an AI and we get features created as we did when we trained the larger system. This particular instance is very fast – it can process millions of files per day. The second system uses a sandbox to detonate the file and associated code blocks to analyze behavioral results. The features are built much as they are in the simpler AI system, with the exception that the malware was activated resulting in different features than the non-detonated sample. Basically we do this for each new sample we want to analyze. The feature sets fill within both systems, but they are different.
- We first look at the AI side. The features we created are compared with the larger production feature set to determine if there are duplicates. If so, the production feature is weighted due to the hit, then removed from the smaller AI feature set. Next, all feature weights are added together, and the sum is divided by the number of features. This provides an average. The features below the average are removed, those above the average are retained. The AI features now only have the best of what was created during this filtering process. Now we reintroduce the malware and test the features to determine performance. We note which features produce the best results as far as speed, malware identification, and zero false positives. Only those features that contribute significantly are retained. We then artificially pad the new features so they will be significant within the production feature set. We did not lose significant features when we did this – after 5 years the system still has hundreds of thousands of features that are insignificant. As the new features are integrated into the analysis process, they are treated the same as any other feature – their weight value may increase or decrease over time. The features we previously extracted have a weighted value at this point. We then check the current production features located in the knowledgebase repository. If duplicates exist, we add some weighting to the production features and remove them from the smaller AI feature set. We then add the AI feature weights in and divide that by the number of features. This gives us an average weight. We then cull out the lower ones and retain the higher weighted features. The system is reset and the are new features are placed in it. The malware is untagged (so the system does not know) and fed into the system. The features that provided the most speed, accuracy of detection, and lowest false positives are retained. The features are given additional weight value The features are added to the knowledge base where the production AI uses them to continue analysis of incoming files
- Next we look at the CPRL side, which is very similar to the smaller AI but with a couple of key differences. As in the AI version, the features we previously extracted have a weighted value at this point. We then check the current production features located in the knowledgebase repository. If duplicates exist, we add some weighting to the production features and remove them from the smaller sandbox/AI feature set. We add the sandbox/AI feature weights in and divide that by the number of features. This gives us an average weight. We then cull out the lower ones and retain the higher weighted features. The system now has the best of the features loaded in the feature set. The malware is untagged (so the system does not know) and fed into the system. The features that provided the most speed, accuracy of detection are transformed back into the code block they originated from. The code blocks are clustered according to how well all possible combinations did during testing. CPRL signatures are created and uploaded to the knowledgebase repository. The signatures are distributed to Fortinet customer Secure Fabric implementations.
- Next we look at the CPRL side, which is very similar to the smaller AI but with a couple of key differences. As in the AI version, the features we previously extracted have a weighted value at this point. We then check the current production features located in the knowledgebase repository. If duplicates exist, we add some weighting to the production features and remove them from the smaller sandbox/AI feature set. We add the sandbox/AI feature weights in and divide that by the number of features. This gives us an average weight. We then cull out the lower ones and retain the higher weighted features. The system now has the best of the features loaded in the feature set. The malware is untagged (so the system does not know) and fed into the system. The features that provided the most speed, accuracy of detection are transformed back into the code block they originated from. The code blocks are clustered according to how well all possible combinations did during testing. CPRL signatures are created and uploaded to the knowledgebase repository. The signatures are distributed to Fortinet customer Secure Fabric implementations.
- Next we look at the CPRL side, which is very similar to the smaller AI but with a couple of key differences. As in the AI version, the features we previously extracted have a weighted value at this point. We then check the current production features located in the knowledgebase repository. If duplicates exist, we add some weighting to the production features and remove them from the smaller sandbox/AI feature set. We add the sandbox/AI feature weights in and divide that by the number of features. This gives us an average weight. We then cull out the lower ones and retain the higher weighted features. The system now has the best of the features loaded in the feature set. The malware is untagged (so the system does not know) and fed into the system. The features that provided the most speed, accuracy of detection are transformed back into the code block they originated from. The code blocks are clustered according to how well all possible combinations did during testing. CPRL signatures are created and uploaded to the knowledgebase repository. The signatures are distributed to Fortinet customer Secure Fabric implementations.