References
- Goodfellow I.J., Shlens J., Szegedy C., Explaining and harnessing adversarial examples, arXiv:1412.6572, 2014.
- Farabet C., Couprie C., Najman L., LeCun Y., Learning hierarchical features for scene labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1915–1929, 2013.
- Jin D., Jin Z., Zhou J.T., Szolovits P., Is bert really robust? a strong baseline for natural language attack on text classification and entailment, Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8018–8025, 2020.
- Madry A., Makelov A., Schmidt L., Tsipras D., Vladu A., Towards deep learning models resistant to adversarial attacks, arXiv:1706.06083, 2017.
- Mozes M., Stenetorp P., Kleinberg B., Griffin L.G., Frequency-guided word substitutions for detecting textual adversarial examples, arXiv:2004.05887, 2020.
- Mrkšić N., Séaghdha D.Ó., Thomson B., Gašić M., Rojas-Barahona L., Su P.H., Vandyke D., Wen T.H., Young S., Counter-fitting word vectors to linguistic constraints, arXiv:1603.00892, 2016.
- Zhang H., Yu Y., Jiao J., Xing E., Ghaoui L.E., Jordan M., Theoretically principled trade-off between robustness and accuracy, Proceedings of the 36th International Conference on Machine Learning (PMLR), 97, 7472–7482, 2019.
- Gholami S., Omar M., Can a student large language model perform as well as it’s teacher?, arXiv:2310.02421, 2023.
- Omar M., Machine Learning for Cybersecurity: Innovative Deep Learning Solutions, Springer, USA, 2022.
- Omar M., VulDefend: A novel technique based on pattern-exploiting training for detecting software vulnerabilities using language models, 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 287–293, 2023.
- Omar M., Choi S., Nyang D., Mohaisen D., Robust natural language processing: Recent advances, challenges, and future directions, arXiv:2201.00768, 2022.
- Omar M., Jones R., Burrell D.N., Dawson M., Nobles C., Mohammed D., Bashir A.K., Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware, IGI Global, USA, 2023.
- Omar M., Sukthankar G., Text-defend: Detecting adversarial examples using local outlier factor, 2023 IEEE 17th International Conference on Semantic Computing (ICSC), 01–03 February 2023, California, USA.
- Sun G., Su Y., Qin C., Xu W., Lu X., Ceglowski A., Complete defense framework to protect deep neural networks against adversarial examples, Mathematical Problems in Engineering, 2020(1), 8319249, 2020.
- Tsipras D., Santurkar S., Engstrom L., Turner, A., Madry A., Robustness may be at odds with accuracy, arXiv:1805.12152, 2018.
- Abbasi R., Bashir A.K., Mateen A., Amin F., Ge Y., Omar M., Efficient security and privacy of lossless secure communication for sensor-based urban cities, IEEE Sensors Journal, 24(5), 5549–5560, 2024.
- Ahmed A., Rasheed H., Bashir A.K., Omar M., Millimeter-wave channel modeling in a VANETs using coding techniques, PeerJ Computer Science, 9:e1374, 1–28, 2023.
- Kinoon M.A., Omar M., Mohaisen M., Mohaisen D., Security breaches in the healthcare domain: a spatiotemporal analysis, Computational Data and Social Networks: 10th International Conference, 13, 15–17, 2021.
- Arulappan A., Raja G., Bashir A.K., Mahanti A., Omar M., ZTPM: Zero touch management provisioning algorithm for the on-boarding of cloud-native virtual network functions, Mobile Networks and Applications, 1–13, 2024.
- Burrell D.N., Nobles C., Richardson K., Wright, Jones A.J., Springs D., Brown-Jackson K., Applied Research Approaches to Technology Healthcare and Business, IGI Global, USA, 2023.
- Jones E., Jia R., Raghunathan A., Liang P., Robust encodings: A framework for combating adversarial typos, arXiv:2005.01229, 2020.
- Keller Y., Mackensen J., Eger S., BERT-Defense: A probabilistic model based on BERT to combat cognitively inspired orthographic adversarial attacks, arXiv:2106.01452, 2021.
- Zhou Y., Jiang J.Y., Chang K.W., Wang W., Learning to discriminate perturbations for blocking adversarial attacks in text classification, arXiv:1909.03084, 2019.
- Li D., Zhang Y., Peng H., Chen L., Brockett C., Sun M.T., Dolan B., Contextualized perturbation for textual adversarial attack, arXiv:2009.07502, 2020.
- Pruthi D., Dhingra B., Lipton Z.C., Combating adversarial misspellings with robust word recognition, arXiv preprint arXiv:1905.11268, 2019.
- Wang W., Wang R., Ke J., Wang L., TextFirewall: Omni-defending against adversarial texts in sentiment classification, IEEE Access, 9, 27467–27475, 2021.
- Wang X., Yang Y., Deng Y., He K., Adversarial training with fast gradient projection method against synonym substitution based text attacks, Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 13997–14005, 2021.
- Sakaguchi K., Post M., Durme B.V., Grammatical error correction with neural reinforcement learning, arXiv:1707.00299, 2017.
- Alshawabkeh M., Jang B., Kaeli D., Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems, Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, 104–110, 2010.
- Bai M., Wang X., Xin J., Wang G., An efficient algorithm for distributed density-based outlier detection on big data, Neurocomputing, 181, 19–28, 2016.
- Lozano E., Acufia E., Parallel algorithms for distance-based and density-based outliers, Fifth IEEE International Conference on Data Mining (ICDM’05), 1–4, 2005.
- Cheng Z., Zou C., Dong J., Outlier detection using isolation forest and local outlier factor, Proceedings of the Conference on Research in Adaptive and Convergent Systems, 161–168, 2019.
- Mika S., Schölkopf B., Smola A., Müller K.R., Scholz M., Rätsch G., Kernel PCA and De-noising in feature spaces, Advances in Neural Information Processing Systems, 11, 1998.
- Topal M.O., Bas A., Heerden I.V., Exploring transformers in natural language generation: GPT, BERT, and XLNet, arXiv:2102.08036, 2021.
- Ma X., Jin R., Paik J.Y., Chung T.S., Lecture Notes in Electrical Engineering (Chapter: Large scale text classification with efficient word embedding), International Conference on Mobile and Wireless Technology, 465–469, Springer, 2017.
- Graves A., Supervised sequence labelling with recurrent neural networks, Springer, USA, 2012.
- Wang T., Wang X., Qin Y., Packer B., Li K., Chen J., Beutel A., Chi E., CAT-Gen: Improving robustness in NLP models via controlled adversarial text generation, arXiv:2010.02338, 2020.
- Morris J.X., Liand E., Lanchantin J., Ji Y., Qi Y., Reevaluating adversarial examples in natural language, arXiv:2004.14174, 2020.
- Morris J.X., Liand E., Yoo J.Y., Grigsby J., Jin D., Qi Y., TextAttack: A framework for adversarial attacks, data augmentation and adversarial training in NLP, arXiv:2005.05909, 2020.
- Asghar N., Yelp dataset challenge: Review rating prediction, arXiv:1605.05362, 2016.
- Poth C., Pfeiffer J., Rücklé, Gurevych I., What to pre-train on? Efficient intermediate task selection, arXiv:2104.08247, 2021.
- Zhang X., Zhao J., LeCun Y., Character-level convolutional networks for text classification, Advances in Neural Information Processing Systems, 28, 2015.
- Pennington J., Socher R., Manning C., Glove: Global vectors for word representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543, 2014.
- Kingma D.P., Ba J., Adam: A method for stochastic optimization, arXiv:1412.6980, 2014.
- Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P, Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau d., Brucher M., Perrot M., Duchesnay É., Scikit-learn: Machine learning in python, Journal of Machine Learning Research, 12, 2825–2830, 2011.
- Gao J., Lanchantin J., Soffa M.L., Qi Y., Black-box generation of adversarial text sequences to evade deep learning classifiers, In 2018 IEEE Security and Privacy Workshops (SPW), 50–56, 2018.