4 research outputs found
Detecting Phishing Sites Using ChatGPT
The rise of large language models (LLMs) has had a significant impact on
various domains, including natural language processing and artificial
intelligence. While LLMs such as ChatGPT have been extensively researched for
tasks such as code generation and text synthesis, their application in
detecting malicious web content, particularly phishing sites, has been largely
unexplored. To combat the rising tide of automated cyber attacks facilitated by
LLMs, it is imperative to automate the detection of malicious web content,
which requires approaches that leverage the power of LLMs to analyze and
classify phishing sites. In this paper, we propose a novel method that utilizes
ChatGPT to detect phishing sites. Our approach involves leveraging a web
crawler to gather information from websites and generate prompts based on this
collected data. This approach enables us to detect various phishing sites
without the need for fine-tuning machine learning models and identify social
engineering techniques from the context of entire websites and URLs. To
evaluate the performance of our proposed method, we conducted experiments using
a dataset. The experimental results using GPT-4 demonstrated promising
performance, with a precision of 98.3% and a recall of 98.4%. Comparative
analysis between GPT-3.5 and GPT-4 revealed an enhancement in the latter's
capability to reduce false negatives. These findings not only highlight the
potential of LLMs in efficiently identifying phishing sites but also have
significant implications for enhancing cybersecurity measures and protecting
users from the dangers of online fraudulent activities
SoC-Cluster as an Edge Server: an Application-driven Measurement Study
Huge electricity consumption is a severe issue for edge data centers. To this
end, we propose a new form of edge server, namely SoC-Cluster, that
orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip
network. For the first time, we have developed a concrete SoC-Cluster server
that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server
has been commercialized successfully and deployed in large scale on edge
clouds. The current dominant workload on those deployed SoC-Clusters is cloud
gaming, as mobile SoCs can seamlessly run native mobile games.
The primary goal of this work is to demystify whether SoC-Cluster can
efficiently serve more general-purpose, edge-typical workloads. Therefore, we
built a benchmark suite that leverages state-of-the-art libraries for two
killer edge workloads, i.e., video transcoding and deep learning inference. The
benchmark comprehensively reports the performance, power consumption, and other
application-specific metrics. We then performed a thorough measurement study
and directly compared SoC-Cluster with traditional edge servers (with Intel CPU
and NVIDIA GPU) with respect to physical size, electricity, and billing. The
results reveal the advantages of SoC-Cluster, especially its high energy
efficiency and the ability to proportionally scale energy consumption with
various incoming loads, as well as its limitations. The results also provide
insightful implications and valuable guidance to further improve SoC-Cluster
and land it in broader edge scenarios