The use of AI for security has proven to be a promising avenue for many critical tasks such as intrusion detection and malware classification.
Reducing manual effort, increasing response times, and generalising to new examples allows defenders to react quickly to incoming threats and allocate valuable human expertise more efficiently.
However, the dynamic nature of data typically seen in security tasks, resulting from the ongoing arms race between attackers and defenders, causes issues that need to be addressed in order to use machine learning to its full potential. Concept drift occurs as malicious techniques evolve and previously trained models aren’t equipped to handle them. The crafting of adversarial examples enables attackers to create specific inputs that bypass ML-based detectors. And many open questions regarding the security of AI, such as information leakage, covert backdoors, and data poisoning, continue to be researched.
AI is a powerful if not revolutionary technology for society, but in reaping the rewards we must also guarantee its safety.
Related Publications
Are Machine Learning Models for Malware Detection Ready for Prime Time?
IEEE S&P Magazine · IEEE Security & Privacy Magazine, 2023
IEEE S&P Magazine · IEEE Security & Privacy Magazine, 2023
@article{CavKinPen23,
author = {Cavallaro, Lorenzo and Kinder, Johannes and Pendlebury, Feargus and Pierazzi, Fabio},
journal = {IEEE Security \& Privacy Magazine},
title = {Are Machine Learning Models for Malware Detection Ready for Prime Time?},
year = {2023},
volume = {21},
number = {2},
pages = {53-56},
doi = {10.1109/MSEC.2023.3236543},
}
Drift Forensics of Malware Classifiers
AISEC · In Prof. of the ACM Workshop on Artificial Intelligence and Security, 2023
AISEC · In Prof. of the ACM Workshop on Artificial Intelligence and Security, 2023
@inproceedings{chow2023driftforensics,
title = {Drift Forensics of Malware Classifiers},
author = {Chow, Theo and Kan, Zeliang and Linhardt, Lorenz and Cavallaro, Lorenzo and Arp, Daniel and Pierazzi, Fabio},
booktitle = {Prof. of the {ACM} Workshop on Artificial Intelligence and Security ({AISec})},
year = {2023},
}
Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors
DLSP · 6th IEEE Workshop on Deep Learning Security and Privacy, 2023
DLSP · 6th IEEE Workshop on Deep Learning Security and Privacy, 2023
@inproceedings{chen23dlsp,
author = {Zhi Chen and Zhenning Zhang and Zeliang Kan and Limin Yang and and Jacopo Cortellazzi and Feargus Pendlebury and Fabio Pierazzi and Lorenzo Cavallaro and Gang Wang},
title = {Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors},
booktitle = {{IEEE} Workshop on Deep Learning Security and Privacy ({DLSP})},
year = {2023},
}
Dos and Don'ts of Machine Learning in Computer Security
USENIX Sec | Distinguished Paper Award · 31st USENIX Security Symposium, 2022
USENIX Sec | Distinguished Paper Award · 31st USENIX Security Symposium, 2022
@inproceedings{arp2022dodo,
author = {Daniel Arp and Erwin Quiring and Feargus Pendlebury and Alexander Warnecke and Fabio Pierazzi and Christian Wressnegger and Lorenzo Cavallaro and Konrad Rieck},
title = {Dos and Don'ts of Machine Learning in Computer Security},
booktitle = {31st USENIX Security Symposium},
year = {2022},
}
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
IEEE S&P · 43rd IEEE Symposium on Security and Privacy, 2022
IEEE S&P · 43rd IEEE Symposium on Security and Privacy, 2022
@inproceedings{barbero2022transcendent,
author = {Federico Barbero and Feargus Pendlebury and Fabio Pierazzi and Lorenzo Cavallaro},
title = {Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift},
booktitle = {{IEEE} Symposium on Security and Privacy},
year = {2022},
}
Investigating Labelless Drift Adaptation for Malware Detection
AISec · 14th ACM Workshop on Artificial Intelligence and Security, 2021
AISec · 14th ACM Workshop on Artificial Intelligence and Security, 2021
@inproceedings{kan2021adaptation,
author = {Zeliang Kan and Feargus Pendlebury and Fabio Pierazzi and Lorenzo Cavallaro},
title = {Investigating Labelless Drift Adaptation for Malware Detection},
booktitle = {{ACM} Workshop on Artificial Intelligence and Security ({AISec})},
year = {2021},
}
INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection
AISec · 14th ACM Workshop on Artificial Intelligence and Security, 2021
AISec · 14th ACM Workshop on Artificial Intelligence and Security, 2021
@inproceedings{andresini2021insomnia,
author = {Giuseppina Andresini and Feargus Pendlebury and Fabio Pierazzi and Corrado Loglisci and Annalisa Appice and Lorenzo Cavallaro},
title = {{INSOMNIA}: Towards Concept-Drift Robustness in Network Intrusion Detection},
journal = {{ACM} Workshop on Artificial Intelligence and Security ({AISec})},
year = {2021},
}
Intriguing Properties of Adversarial ML Attacks in the Problem Space
IEEE S&P · 41st IEEE Symposium on Security and Privacy, 2020
IEEE S&P · 41st IEEE Symposium on Security and Privacy, 2020
@inproceedings{pierazzi2020problemspace,
author = {Fabio Pierazzi and Feargus Pendlebury and Jacopo Cortellazzi and Lorenzo Cavallaro},
booktitle = {2020 IEEE Symposium on Security and Privacy (SP)},
title = {Intriguing Properties of Adversarial ML Attacks in the Problem Space},
year = {2020},
volume = {},
issn = {2375-1207},
pages = {1308-1325},
doi = {10.1109/SP40000.2020.00073},
url = {https://doi.ieeecomputersociety.org/10.1109/SP40000.2020.00073},
publisher = {IEEE Computer Society},
}
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
USENIX Sec · 28th USENIX Security Symposium, 2019
USENIX Sec · 28th USENIX Security Symposium, 2019
@inproceedings{pendlebury2019tesseract,
author = {Feargus Pendlebury and Fabio Pierazzi and Roberto Jordaney and Johannes Kinder and Lorenzo Cavallaro},
title = {{TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time}},
booktitle = {28th USENIX Security Symposium},
year = {2019},
address = {Santa Clara, CA},
publisher = {USENIX Association},
note = {USENIX Sec}
}
Transcend: Detecting Concept Drift in Malware Classification Models
USENIX Sec · 26th USENIX Security Symposium, 2017
USENIX Sec · 26th USENIX Security Symposium, 2017
@inproceedings {jordaney2017,
author = {Roberto Jordaney and Kumar Sharad and Santanu K. Dash and Zhi Wang and Davide Papini and Ilia Nouretdinov and Lorenzo Cavallaro},
title = {{Transcend: Detecting Concept Drift in Malware Classification Models}},
booktitle = {26th USENIX Security Symposium},
year = {2017},
address = {Vancouver, BC},
url = {https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/jordaney},
publisher = {USENIX Association},
note = {USENIX Sec}
}
DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware
ACM CODASPY · 7th ACM Conference on Data and Application Security and Privacy, 2017
ACM CODASPY · 7th ACM Conference on Data and Application Security and Privacy, 2017
@inproceedings{codaspy17,
author = {Guillermo Suarez-Tangil and Santanu Kumar Dash and Mansour Ahmadi and Johannes Kinder and Giorgio Giacinto and Lorenzo Cavallaro},
title = {{DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware}},
booktitle = {{Proceedings of the Seventh ACM Conference on Data and Application Security and Privacy}},
year = {2017},
month = {March},
url = {http://dx.doi.org/10.1145/3029806.3029825},
doi = {10.1145/3029806.3029825},
note = {ACM CODASPY}
}
Misleading Metrics: On Evaluating Machine Learning for Malware with Confidence
TR@RHUL · Technical Report, 2016
TR@RHUL · Technical Report, 2016
@TechReport{RHUL2016,
author = {Roberto Jordaney and Zhi Wang and Davide Papini and Ilia Nouretdinov and Lorenzo Cavallaro},
title = {{Misleading Metrics: On Evaluating Machine Learning for Malware with Confidence}},
institution = {Royal Holloway, University of London},
year = {2016},
number = {2016-1},
note = {TR@RHUL}
}
DroidScribe: Classifying Android Malware Based on Runtime Behavior
IEEE S&P-MoST · IEEE Security and Privacy Workshops: Mobile Security Technologies, 2016
IEEE S&P-MoST · IEEE Security and Privacy Workshops: Mobile Security Technologies, 2016
@inproceedings{most16-droidscribe,
author = {Santanu Kumar Dash and Guillermo Suarez-Tangil and Salahuddin Khan and Kimberly Tam and Mansour Ahmadi and Johannes Kinder and Lorenzo Cavallaro},
title = {DroidScribe: Classifying Android Malware Based on Runtime Behavior},
booktitle = {IEEE Security and Privacy Workshops: Mobile Security Technologies},
year = 2016,
month = {May},
note = {IEEE S&P-MoST}
}
Prescience: Probabilistic Guidance on the Retraining Conundrum for Malware Detection
ACM CCS-AISec · 9th ACM CCS Workshop on Artificial Intelligence and Security, 2016
ACM CCS-AISec · 9th ACM CCS Workshop on Artificial Intelligence and Security, 2016
@inproceedings{aisec16,
author = {Amit Deo and Santanu Kumar Dash and Guillermo Suarez-Tangil and Volodya Vovk and Lorenzo Cavallaro},
title = {{Prescience: Probabilistic Guidance on the Retraining Conundrum for Malware Detection}},
booktitle = {9th ACM CCS Workshop on Artificial Intelligence and Security},
year = {2016},
note = {ACM CCS-AISec}
}
Conformal Clustering and Its Application to Botnet Traffic
SLDS · 3rd International Symposium of Statistical Learning and Data Science, 2015
SLDS · 3rd International Symposium of Statistical Learning and Data Science, 2015
@inproceedings{cherubin,
author = {Giovanni Cherubin and Ilia Nouretdinov and Alexander Gammerman and Roberto Jordaney and Zhi Wang and Davide Papini and Lorenzo Cavallaro},
title = {{Conformal Clustering and Its Application to Botnet Traffic}},
booktitle = {Statistical Learning and Data Sciences, 3rd International Symposium},
year = {2015},
note = {SLDS}
}