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Found 14 papers, 2 papers with code
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
Adversarial Malware Generation (AMG), the gen- eration of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense.
Large Language Models for Conducting Advanced Text Analytics Information Systems Research
To assist IS research in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework.
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants
This provides an opportunity for the defenders (i. e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e. g., source code view in addition to the binary view).
Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity
This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution.
Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions
In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools.
Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
Predicting gene functions is a challenge for biologists in the post genomic era.
Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence
In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection.
Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach
Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth.
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach
While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency.
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model
Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis.
Deep Learning for Information Systems Research
Related to this broader goal, this paper makes five timely contributions.
Evaluating the Usefulness of Sentiment Information for Focused Crawlers
Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content.
Detecting Fake Escrow Websites using Rich Fraud Cues and Kernel Based Methods
The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud.
A Statistical Learning Based System for Fake Website Detection
Existing fake website detection systems are unable to effectively detect fake websites.
