Adversarial machine learning for network intrusion detection systems: A comprehensive survey
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 …
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
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 …
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 …
adversarial misuse, as evidenced by the prevalence of “jailbreak” attacks on early releases …
A survey on vision transformer
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 …
network mainly based on the self-attention mechanism. Thanks to its strong representation …
Smoothllm: Defending large language models against jailbreaking attacks
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 …
LLMs such as GPT, Llama, Claude, and PaLM are susceptible to jailbreaking attacks …
A survey on visual transformer
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 …
network mainly based on the self-attention mechanism. Thanks to its strong representation …
Lift: Language-interfaced fine-tuning for non-language machine learning tasks
Fine-tuning pretrained language models (LMs) without making any architectural changes
has become a norm for learning various language downstream tasks. However, for non …
has become a norm for learning various language downstream tasks. However, for non …
Trustworthy ai: A computational perspective
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 …
developments, changing everyone's daily life and profoundly altering the course of human …
Universal adversarial triggers for attacking and analyzing NLP
Adversarial examples highlight model vulnerabilities and are useful for evaluation and
interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens …
interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens …
Adversarial attacks against network intrusion detection in IoT systems
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 …
capability of recognizing subtle differences between normal and malicious network activities …