Quantum Classification of Malware. John Seymour
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1 Quantum Classification of Malware John Seymour
2 whoami Ph.D. student at the University of Maryland, Baltimore County (UMBC) Actively studying/researching infosec for about three years (mostly academic) Currently work for CyberPoint International
3 Outline D-Wave Basics The D-Wave Controversy How to play around on a D-Wave Some Machine Learning Background Building a Quantum Malware Classifier
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6 What you might have heard (and why it s wrong) FALSE: The D-Wave can solve NP-Complete problems in polynomial time. PROBABLY FALSE: The current D-Wave chip is already better than classical computing for hard problems.
7 The Current State of Affairs Quantum effects are happening but that might not be interesting We don't know whether the D-Wave uses quantum effects for computation. Regardless, it cannot run Shor's/Grovers/QKD.
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14 D-Wave chips consist of: magnetized niobium loops couplers
15 The D-Wave QUBO Input i a i q i + i,j b ij q i q j Output "Quadratic Unconstrained Binary Optimization"
16 They ve got a website. To do stuff on and stuff.
17 System 6
18 System 13
19 One D-Wave Run Input Output
20 Blackbox/QSage Software, developed by D-Wave Turns arbitrary problems into QUBOs Heuristic-based (problem is NP-Complete) Conversation between classical machine and D-Wave Adds time from network latency
21 Blackbox Python Code
22 So what can D-Wave machines do? D-Wave claims applications: classification, protein-folding models, finding close-tooptimal solutions to NPC problems (e.g. Traveling Salesman)
23 Crash Course in Machine Learning (At least what s relevant to this)
24 Boosting - using combinations of weak classifiers Consider 3 classifiers with 70% accuracy All Correct: 0.7 * 0.7 * 0.7 = Two Correct: 3 * 0.7 * 0.7 * 0.3 = Two Wrong: 3 * 0.3 * 0.3 * 0.7 = All Wrong: 0.3 * 0.3 * 0.3 = Majority vote can increase your accuracy to ! (Hint: add up the top row) Most boosting algorithms also allow weights for these classifiers.
25 Loss Want to minimize: Number of misclassifications Complexity of the model
26 N-Grams Sliding window over text DEADBEEF
27 N-Grams Sliding window over text DEADBEEF
28 N-Grams Sliding window over text DEADBEEF
29 N-Grams Sliding window over text DEADBEEF
30 N-Grams Sliding window over text DEADBEEF Easy to generate Used before in malware with good results Easy to turn into weak classifiers Complex enough to compare classifiers
31 Building the Quantum Malware Classifier
32 QBoost Outperforms Adaboost Robust to label noise Will generally still learn even if training data is mislabeled Good for learning malware: ground truth is hard! Doesn t scale to Google-sized problems Blackbox supposedly can
33 Dataset used Plenty of malicious datasets to choose from Vx Heaven, VirusShare, scraping the web We used Vx Heaven (fairly standard but old) No standard for benign dataset Problematic Windows + Cygwin + Sourceforge Don t do this No adware was used in the making of this classifier
34 (Classical) Preprocessing Resample corpus to be balanced Side-effects: Less time to train, lose information Extract Features (3-gram bytes)
35 At first, our classifier was no better than random chance Next question: how long do we need to let Blackbox run?
36 Previous work says 30 minute timeout Pilot Experiment to find how long it should take Even on small problems, it takes 10 minutes to find decent solutions Larger problems require even more time
37 Limited to 32 features We used 16 malware and 16 benign n-grams Implemented QBoost with 10-fold crossvalidation using Blackbox Both on D-Wave and using a simulator Compared to several models from WEKA Adaboost J48 Decision Tree Random Forest
38 Results
39 Results Interesting Result 1: takes a LOT of time
40 Results Interesting Result 2: Simulator > actual chip
41 What does it mean? Blackbox/D-Wave CAN learn a classifier Probably a bad idea: significant overhead and must restrict problem substantially We don t know if this is due to Blackbox, the D- Wave chip, or some other issue General problem-solving on D-Wave is probably a bad idea Really should ve stuck with QBoost
42 How much better is the next D-Wave chip? Embed directly onto chip, rather than use heuristics Better for another task? e.g. feature preprocessing Machine Learning Standards for Malware Analysis
43 Thank you!
44 References Original thesis: J. Seymour. Quantum classification of malware. Dataset prep: J. Seymour and C. Nicholas. Overgeneralization in feature set selection for classification of malware. Qboost: V. S. Denchev. Binary classification with adiabatic quantum optimization. Qloss: V. S. Denchev. Robust Classification with Adiabatic Quantum Optimization.
45 References Tutorial on Binary Classification using D-Wave: 3/ Evaluating Blackbox: C. C. McGeoch and C. Wang. Experimental evaluation of an adiabiatic quantum system for combinatorial optimization.
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