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Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
Software flaw detection using multimodal deep learning models has been d...
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On the Feasibility of Transfer-learning Code Smells using Deep Learning
Context: A substantial amount of work has been done to detect smells in ...
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The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models
In this paper, we introduce the TrojAI software framework, an open sourc...
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Recent Trends in Deep Learning Based Personality Detection
In the recent times, automatic detection of human personality traits has...
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AST-Based Deep Learning for Detecting Malicious PowerShell
With the celebrated success of deep learning, some attempts to develop e...
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Live Trojan Attacks on Deep Neural Networks
Like all software systems, the execution of deep learning models is dict...
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Building Program Vector Representations for Deep Learning
Deep learning has made significant breakthroughs in various fields of ar...
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Multimodal Deep Learning for Flaw Detection in Software Programs
We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.
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