Adversarial machine learning for network intrusion detection systems: A comprehensive survey

K He, DD Kim, MR Asghar - IEEE Communications Surveys & …, 2023 - ieeexplore.ieee.org
Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges

K Ahmad, M Maabreh, M Ghaly, K Khan, J Qadir… - Computer Science …, 2022 - Elsevier
As the globally increasing population drives rapid urbanization in various parts of the world,
there is a great need to deliberate on the future of the cities worth living. In particular, as …

Jailbroken: How does llm safety training fail?

A Wei, N Haghtalab… - Advances in Neural …, 2024 - proceedings.neurips.cc
Large language models trained for safety and harmlessness remain susceptible to
adversarial misuse, as evidenced by the prevalence of “jailbreak” attacks on early releases …

A survey on vision transformer

K Han, Y Wang, H Chen, X Chen, J Guo… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Smoothllm: Defending large language models against jailbreaking attacks

A Robey, E Wong, H Hassani, GJ Pappas - arXiv preprint arXiv …, 2023 - arxiv.org
Despite efforts to align large language models (LLMs) with human values, widely-used
LLMs such as GPT, Llama, Claude, and PaLM are susceptible to jailbreaking attacks …

A survey on visual transformer

K Han, Y Wang, H Chen, X Chen, J Guo, Z Liu… - arXiv preprint arXiv …, 2020 - arxiv.org
Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Lift: Language-interfaced fine-tuning for non-language machine learning tasks

T Dinh, Y Zeng, R Zhang, Z Lin… - Advances in …, 2022 - proceedings.neurips.cc
Fine-tuning pretrained language models (LMs) without making any architectural changes
has become a norm for learning various language downstream tasks. However, for non …

Trustworthy ai: A computational perspective

H Liu, Y Wang, W Fan, X Liu, Y Li, S Jain, Y Liu… - ACM Transactions on …, 2022 - dl.acm.org
In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Universal adversarial triggers for attacking and analyzing NLP

E Wallace, S Feng, N Kandpal, M Gardner… - arXiv preprint arXiv …, 2019 - arxiv.org
Adversarial examples highlight model vulnerabilities and are useful for evaluation and
interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens …

Adversarial attacks against network intrusion detection in IoT systems

H Qiu, T Dong, T Zhang, J Lu… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Deep learning (DL) has gained popularity in network intrusion detection, due to its strong
capability of recognizing subtle differences between normal and malicious network activities …