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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 revealed practitioners' common feeling for better protection of machine learning systems in industrial applications.
In the oil and gas sector, anomaly detection is not just crucial for maintenance and safety, but also for environmental protection. [21] Aljameel et al. propose an advanced machine learning-based model for detecting minor leaks in oil and gas pipelines, a task traditional methods may miss. [21]
Data sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity. [ 2 ] This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to ...
An example of a physical security measure: a metal lock on the back of a personal computer to prevent hardware tampering. Computer security (also cybersecurity, digital security, or information technology (IT) security) is the protection of computer software, systems and networks from threats that can lead to unauthorized information disclosure, theft or damage to hardware, software, or data ...
With federated learning coupled with local differential privacy, researchers have found this model to be quite effective to facilitate crowdsourcing applications and provide protection for users' privacy. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage. Likewise ...
Advanced security measures employ machine learning and temporal reasoning algorithms to detect abnormal access to data (e.g., databases or information retrieval systems) or abnormal email exchange, honeypots for detecting authorized personnel with malicious intentions and activity-based verification (e.g., recognition of keystroke dynamics) and ...
Newer systems combining unsupervised machine learning with full network traffic analysis can detect active network attackers from malicious insiders or targeted external attackers that have compromised a user machine or account. [5] Communication between two hosts using a network may be encrypted to maintain security and privacy.
Sharing of "cybersecurity best practices with attention to the challenges faced by small businesses. In 2016, the U.S. government agency National Institute of Standards and Technology (NIST) issued a publication (NIST SP 800-150) which further outlined the necessity for Cyber Threat Information Sharing as well as a framework for implementation.