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3yr ROI

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Talent Scout

C

Christian Rondanini

University of Insubria

B

Barbara Carminati

University of Insubria

E

Elena Ferrari

University of Insubria

N

Niccolò Lardo

University of Insubria

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References (48)

[1]
Malware Detection at the Edge with Lightweight LLMs: A Performance Evaluation
2025Christian Rondanini, B. Carminati et al.
[2]
A Survey on ML Techniques for Multi-Platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud Environments
2025Jannatul Ferdous, Rafiqul Islam et al.
[3]
SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
2025Yichen Wu, Hongming Piao et al.
[4]
Gated Integration of Low-Rank Adaptation for Continual Learning of Language Models
2025Yan-Shuo Liang, Wu-Jun Li
[5]
Guessing As A Service: Large Language Models Are Not Yet Ready For Vulnerability Detection
2025Francesco Panebianco, A. Isgrò et al.
[6]
Harnessing LLMs for IoT Malware Detection: A Comparative Analysis of BERT and GPT-2
2024Marwan Omar, H. Zangana et al.
[7]
Large Language Models to Enhance Malware Detection in Edge Computing
2024Christian Rondanini, B. Carminati et al.
[8]
Combining replay and LoRA for continual learning in natural language understanding
2024Zeinb Borhanifard, Heshaam Faili et al.
[9]
Learning Attentional Mixture of LoRAs for Language Model Continual Learning
2024Jialin Liu, Jianhua Wu et al.
[10]
Towards Lifelong Learning of Large Language Models: A Survey
2024Junhao Zheng, Shengjie Qiu et al.
[11]
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls
2024P. Sánchez, Alberto Huertas Celdrán et al.
[12]
AppPoet: Large Language Model based Android malware detection via multi-view prompt engineering
2024Wenxiang Zhao, Juntao Wu et al.
[13]
Towards Incremental Learning in Large Language Models: A Critical Review
2024M. Jovanovic, Peter Voss
[14]
Continual Learning of Large Language Models: A Comprehensive Survey
2024Haizhou Shi, Zihao Xu et al.
[15]
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
2024Zeyu Han, Chao Gao et al.
[16]
Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters
2024Jiazuo Yu, Yunzhi Zhuge et al.
[17]
A Survey on Knowledge Distillation of Large Language Models
2024Xiaohan Xu, Ming Li et al.
[18]
Continual Learning for Large Language Models: A Survey
2024Tongtong Wu, Linhao Luo et al.
[19]
Continual Learning with Low Rank Adaptation
2023Martin Wistuba, Prabhu Teja Sivaprasad et al.
[20]
Orthogonal Subspace Learning for Language Model Continual Learning
2023Xiao Wang, Tianze Chen et al.

Showing 20 of 48 references

Founder's Pitch

"Enable edge-based real-time malware detection with parameter-efficient continuous learning using LoRA."

Security and Edge ComputingScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

1/4 signals

2.5

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Why It Matters

The paper provides a method to enhance edge computing for malware detection by continuously learning and adapting through parameter-efficient techniques, addressing resource constraints of edge devices.

Product Angle

Create an edge-focused cybersecurity platform using this approach, targeting IoT networks, providing continuous malware detection and updates with minimal resource usage.

Disruption

Replaces static or central-only malware detection models with a dynamic, decentralized approach that updates without centralized data pools, increasing real-time adaptability.

Product Opportunity

The increasing number of IoT devices leads to rising security needs. This approach targets a market involving billions of such devices, where traditional methods fail to adapt quickly to evolving threats.

Use Case Idea

Develop a cybersecurity tool for IoT devices that utilizes LoRA-based continual learning for real-time malware detection, providing adaptive security across distributed edge devices.

Science

The research integrates Lightweight transformer models like DistilBERT with Low-Rank Adaptation (LoRA) for continuous, parameter-efficient learning on edge devices. LoRA enables adaptation by adding minimal model size increase, sharing knowledge with a central hub without transferring raw data, enhancing cross-device learning.

Method & Eval

The method uses minimalist LLMs (DistilBERT, etc.) on edge devices, applying LoRA for incremental learning, tested on Edge-IIoTset and TON-IoT datasets, showing 20-25% increased accuracy for unseen attacks while maintaining efficiency.

Caveats

LoRA reliance may complicate adapter management on devices, and new attack profiles must be accurately generated to ensure learning effectiveness, with potential latency concerns during aggregation phases.

Author Intelligence

Christian Rondanini

University of Insubria
christian.rondanini@uninsubria.it

Barbara Carminati

University of Insubria
barbara.carminati@uninsubria.it

Elena Ferrari

University of Insubria
elena.ferrari@uninsubria.it

Niccolò Lardo

University of Insubria
nlardo1@uninsubria.it

Ashish Kundu

Cisco Research
ashkundu@cisco.com