Fetching the paper…
Reading the bibliography…
Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos.
Does the wake-sleep algorithm produce good density estimators?
Brendan J Frey, Geoffrey E Hinton, and Peter Dayan · 1995
Earlier work this paper cites.
Web of deception: Misinformation on the Internet
Steve Forbes · 2002
Earlier work this paper cites.
Deception in defense of computer systems from cyber attack
Neil C Rowe · 2007
Earlier work this paper cites.
Automated red teaming: a proposed framework for military application
Chwee Seng Choo, Ching Lian Chua, and Su-Han Victor Tay · 2007
Earlier work this paper cites.
Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains
Eric M Hutchins, Michael J Cloppert, Rohan M Amin, et al · 2011
Earlier work this paper cites.
Analysing web-based malware behaviour through client honeypots
Yaser Alosefer · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Random host mutation for moving target defense
Ehab Al-Shaer, Qi Duan, and Jafar Haadi Jafarian · 2013
Earlier work this paper cites.
Detecting misinformation in online social networks using cognitive psychology
KP Kumar and G Geethakumari · 2014
Earlier work this paper cites.
Fox in the trap: Thwarting masqueraders via automated decoy document deployment
Jonathan Voris, Jill Jermyn, Nathaniel Boggs, and Salvatore Stolfo · 2015
Earlier work this paper cites.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Earlier work this paper cites.
Deepdga: Adversarially-tuned domain generation and detection
Hyrum S Anderson, Jonathan Woodbridge, and Bobby Filar · 2016
Earlier work this paper cites.
Droiddetector: android malware characterization and detection using deep learning
Zhenlong Yuan, Yongqiang Lu, and Yibo Xue · 2016
Earlier work this paper cites.
Using honeynets and the diamond model for ics threat analysis
John Kotheimer, Kyle OMeara, and Deana Shick · 2016
Earlier work this paper cites.
Cybertwitter: Using twitter to generate alerts for cybersecurity threats and vulnerabilities
Sudip Mittal, Prajit Kumar Das, Varish Mulwad, Anupam Joshi, and Tim Finin · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Earlier work this paper cites.
Software vulnerability analysis and discovery using machine-learning and data-mining techniques: A survey
Seyed Mohammad Ghaffarian and Hamid Reza Shahriari · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Earlier work this paper cites.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
Earlier work this paper cites.
Bringing a gan to a knife-fight: Adapting malware communication to avoid detection
Maria Rigaki and Sebastian Garcia · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Automated email generation for targeted attacks using natural language
Avisha Das and Rakesh Verma · 2019
Earlier work this paper cites.
Misinformation in social media: definition, manipulation, and detection
Liang Wu, Fred Morstatter, Kathleen M Carley, and Huan Liu · 2019
Earlier work this paper cites.
Preventing poisoning attacks on ai based threat intelligence systems
Nitika Khurana, Sudip Mittal, Aritran Piplai, and Anupam Joshi · 2019
Earlier work this paper cites.
Steganogan: High capacity image steganography with gans
Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, and Kalyan Veeramachaneni · 2019
Earlier work this paper cites.
Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks
Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, and See-Kiong Ng · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Earlier work this paper cites.
Relext: Relation extraction using deep learning approaches for cybersecurity knowledge graph improvement
Aditya Pingle, Aritran Piplai, Sudip Mittal, Anupam Joshi, James Holt, and Richard Zak · 2019
Earlier work this paper cites.
Bypassing detection of url-based phishing attacks using generative adversarial deep neural networks
Ahmed AlEroud and George Karabatis · 2020
Earlier work this paper cites.
A comprehensive survey and analysis of generative models in machine learning
GM Harshvardhan, Mahendra Kumar Gourisaria, Manjusha Pandey, and Siddharth Swarup Rautaray · 2020
Earlier work this paper cites.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Polymorphic adversarial ddos attack on ids using gan
Ravi Chauhan and Shahram Shah Heydari · 2020
Earlier work this paper cites.
The art of war
Sun Tzu · 2020
Earlier work this paper cites.
Adversarial examples detection for xss attacks based on generative adversarial networks
Xueqin Zhang, Yue Zhou, Songwen Pei, Jingjing Zhuge, and Jiahao Chen · 2020
Earlier work this paper cites.
Stegonet: Turn deep neural network into a stegomalware
Tao Liu, Zihao Liu, Qi Liu, Wujie Wen, Wenyao Xu, and Ming Li · 2020
Earlier work this paper cites.
Flow-based detection and proxy-based evasion of encrypted malware c2 traffic
Carlos Novo and Ricardo Morla · 2020
Earlier work this paper cites.
Tadgan: Time series anomaly detection using generative adversarial networks
Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2020
Cited alongside, same era.
Tanogan: Time series anomaly detection with generative adversarial networks
Md Abul Bashar and Richi Nayak · 2020
Cited alongside, same era.
Improved phishing detection algorithms using adversarial autoencoder synthesized data
Hossein Shirazi, Shashika R Muramudalige, Indrakshi Ray, and Anura P Jayasumana · 2020
Cited alongside, same era.
Generating sentiment-preserving fake online reviews using neural language models and their human-and machine-based detection
David Ifeoluwa Adelani, Haotian Mai, Fuming Fang, Huy H Nguyen, Junichi Yamagishi, and Isao Echizen · 2020
Cited alongside, same era.
Creating cybersecurity knowledge graphs from malware after action reports
Aritran Piplai, Sudip Mittal, Anupam Joshi, Tim Finin, James Holt, and Richard Zak · 2020
Cited alongside, same era.
Android malware detection through generative adversarial networks
Muhammad Amin, Babar Shah, Aizaz Sharif, Tamleek Ali, Ki-Il Kim, and Sajid Anwar · 2022
Later among the works it cites.
An investigation on fragility of machine learning classifiers in android malware detection
Husnain Rafiq, Nauman Aslam, Biju Issac, and Rizwan Hamid Randhawa · 2022
Later among the works it cites.
Explainable intrusion detection systems (x-ids): A survey of current methods, challenges, and opportunities
Subash Neupane, Jesse Ables, William Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, and Maria Seale · 2022
Later among the works it cites.
Creating an explainable intrusion detection system using self organizing maps
Jesse Ables, Thomas Kirby, William Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, and Maria Seale · 2022
Later among the works it cites.
A surrogate-based technique for android malware detectors’ explainability
Martina Morcos, Hussam Al Hamadi, Ernesto Damiani, Sivaprasad Nandyala, and Brian McGillion · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Can machine learning model with static features be fooled: an adversarial machine learning approach
Rahim Taheri, Reza Javidan, Mohammad Shojafar, P Vinod, and Mauro Conti · 2020
Cited alongside, same era.
When explainability meets adversarial learning: Detecting adversarial examples using shap signatures
Gil Fidel, Ron Bitton, and Asaf Shabtai · 2020
Cited alongside, same era.
Using knowledge graphs and reinforcement learning for malware analysis
Aritran Piplai, Priyanka Ranade, Anantaa Kotal, Sudip Mittal, Sandeep Nair Narayanan, and Anupam Joshi · 2020
Cited alongside, same era.
Offensive ai: Unification of email generation through gpt-2 model with a game-theoretic approach for spear-phishing attacks
Hajra Khan, Masoom Alam, Saif Al-Kuwari, and Yasir Faheem · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Later among the works it cites.
irs-partition: An intrusion response system utilizing deep q-networks and system partitions
Valeria Cardellini, Emiliano Casalicchio, Stefano Iannucci, Matteo Lucantonio, Sudip Mittal, Damodar Panigrahi, and Andrea Silvi · 2022
Later among the works it cites.
Bridging automated to autonomous cyber defense: Foundational analysis of tabular q-learning
Andy Applebaum, Camron Dennler, Patrick Dwyer, Marina Moskowitz, Harold Nguyen, Nicole Nichols, Nicole Park, Paul Rachwalski, Frank Rau, Adrian Webster, et al · 2022
Later among the works it cites.
https://openai.com/blog/chatgpt , 2023
Introducing ChatGPT · 2023
Closest in time.
https://bard.google.com/ , 2023
GoogleBrad · 2023
Closest in time.
https://www.microsoft.com/en-us/edge/features/bing-chat?form=MT00D8 , 2023
Bing Chat · 2023
Closest in time.
https://www.pbs.org/newshour/politics/ai-generated-disinformation-poses-threat-of-misleading-voters-in-2024-election , 2023
AI-generated disinformation poses threat of misleading voters in 2024 election · 2023
Closest in time.
A skeptical take on the ai revolution, 2023
G Marcus · 2023
Closest in time.
https://ir.darktrace.com/press-releases/2023/4/3/8b2d6ba25d9d54a1895956a985fe4a7d08d9f42607a112fb17964e4b57fad7d6 , 2023
Press Release · 2023
Closest in time.
https://www.bbc.com/news/technology-60780142 , 2023
Deepfake presidents used in Russia-Ukraine war · 2023
Closest in time.
https://www.bloomberg.com/opinion/articles/2023-05-23/viral-fake-pentagon-explosion-photo-on-twitter-is-an-ai-wake-up-call#xj4y7vzkg , 2023
Don’t Believe Your Lying Eyes in the AI Era · 2023
Closest in time.
https://docs.midjourney.com/ , 2023
Midjourney Documentation · 2023
Closest in time.
https://www.mandiant.com/resources/blog/dissecting-one-ofap , 2023
Dissecting One of APT29’s Fileless WMI and PowerShell Backdoors (POSHSPY) · 2023
Closest in time.
https://www.cisa.gov/sites/default/files/FactSheets/NCCIC%20ICS_FactSheet_WannaCry_Ransomware_S508C.pdf , 2023
WHAT IS WANNACRY/WANACRYPT0R? · 2023
Closest in time.
Generating adversarial malware examples for black-box attacks based on gan
Weiwei Hu and Ying Tan · 2023
Closest in time.
https://www.cisa.gov/news-events/alerts/2017/07/01/petya-ransomware , 2023
Cybersecurity Infrastructure & Security Agency, petya ransomware · 2023
Closest in time.
https://www.cisa.gov/news-events/alerts/2017/06/12/crashoverride-malware , 2023
Cybersecurity Infrastructure & Security Agency, crashoverride malware · 2023
Closest in time.
https://www.darkreading.com/attacks-breaches/sidewinder-strikes-victims-pakistan-turkey-multiphase-polymorphic-attack , 2023
Darkreading, sidewinder strikes victims in pakistan, turkey in multiphase polymorphic attack · 2023
Closest in time.
https://attack.mitre.org/software/S0386/ , 2023
Ursnif · 2023
Closest in time.
https://www.cisa.gov/news-events/alerts/2015/04/09/aaeh , 2023
Cybersecurity Infrastructure & Security Agency aaeh · 2023
Closest in time.
https://attack.mitre.org/versions/v7/software/S0367/ , 2023
Emotet · 2023
Closest in time.
https://twitter.com/ , 2023
Twitter · 2023
Closest in time.
https://www.facebook.com/ , 2023
Facebook · 2023
Closest in time.
https://www.reddit.com/ , 2023
Reddit · 2023
Closest in time.
https://www.deepswap.ai/ , 2023
Deepswap · 2023
Closest in time.
https://www.wombo.ai/ , 2023
Woombo · 2023
Closest in time.
https://github.com/dome272/Instagram-DeepFake-Bot , 2023
Instagram-DeepFake-Bot · 2023
Closest in time.
https://www.intel.com/content/www/us/en/newsroom/news/intel-introduces-real-time-deepfake-detector.html#gs.1afjyz , 2023
Intel Introduces Real-Time Deepfake Detector · 2023
Closest in time.
Maximizing penetration testing success with effective reconnaissance techniques using chatgpt
Sheetal Temara · 2023
Closest in time.
Pruning GHSOM to create an explainable intrusion detection system
Thomas Michael Kirby · 2023
Closest in time.
Twinexplainer: Explaining predictions of an automotive digital twin
Subash Neupane, Ivan A Fernandez, Wilson Patterson, Sudip Mittal, Milan Parmar, and Shahram Rahimi · 2023
Closest in time.
Explaining phishing attacks: An xai approach to enhance user awareness and trust
Francesco Greco, Giuseppe Desolda, and Andrea Esposito · 2023
Closest in time.