Computing systems for autonomous driving: State of the art and challenges

L Liu, S Lu, R Zhong, B Wu, Y Yao… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …

Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities

A Bécue, I Praça, J Gama - Artificial Intelligence Review, 2021 - Springer
This survey paper discusses opportunities and threats of using artificial intelligence (AI)
technology in the manufacturing sector with consideration for offensive and defensive uses …

Deep learning for classification of malware system call sequences

B Kolosnjaji, A Zarras, G Webster, C Eckert - AI 2016: Advances in …, 2016 - Springer
The increase in number and variety of malware samples amplifies the need for improvement
in automatic detection and classification of the malware variants. Machine learning is a …

Cryptolock (and drop it): stopping ransomware attacks on user data

N Scaife, H Carter, P Traynor… - 2016 IEEE 36th …, 2016 - ieeexplore.ieee.org
Ransomware is a growing threat that encrypts auser's files and holds the decryption key until
a ransom ispaid by the victim. This type of malware is responsible fortens of millions of …

Outlier detection for temporal data: A survey

M Gupta, J Gao, CC Aggarwal… - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

There and back again: Outlier detection between statistical reasoning and data mining algorithms

A Zimek, P Filzmoser - Wiley Interdisciplinary Reviews: Data …, 2018 - Wiley Online Library
Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

[HTML][HTML] Event labeling combining ensemble detectors and background knowledge

H Fanaee-T, J Gama - Progress in Artificial Intelligence, 2014 - Springer
Event labeling is the process of marking events in unlabeled data. Traditionally, this is done
by involving one or more human experts through an expensive and time-consuming task. In …

Intrusion detection techniques in cloud environment: A survey

P Mishra, ES Pilli, V Varadharajan… - Journal of Network and …, 2017 - Elsevier
Security is of paramount importance in this new era of on-demand Cloud Computing.
Researchers have provided a survey on several intrusion detection techniques for detecting …

A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets

PMS Sánchez, JMJ Valero, AH Celdrán… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
In the current network-based computing world, where the number of interconnected devices
grows exponentially, their diversity, malfunctions, and cybersecurity threats are increasing at …

Tiresias: Predicting security events through deep learning

Y Shen, E Mariconti, PA Vervier… - Proceedings of the 2018 …, 2018 - dl.acm.org
With the increased complexity of modern computer attacks, there is a need for defenders not
only to detect malicious activity as it happens, but also to predict the specific steps that will …