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There are a large variety of different adversarial attacks that can be used against machine learning systems. Many of these work on both deep learning systems as well as traditional machine learning models such as SVMs [8] and linear regression. [76] A high level sample of these attack types include: Adversarial Examples [77]
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses machine ethics and AI alignment, which aim to ensure AI systems are moral and beneficial, as well as monitoring AI systems for risks and enhancing their reliability.
Tactics are the “why” of an attack technique. The framework consists of 14 tactics categories consisting of "technical objectives" of an adversary. [2] Examples include privilege escalation and command and control. [3] These categories are then broken down further into specific techniques and sub-techniques. [3]
Since 2008, there has been a rise in workplace violence that many experts believe is closely associated with the increasing pressure people are feeling at work and overall uncertainty about jobs ...
An Inference Attack is a data mining technique performed by analyzing data in order to illegitimately gain knowledge about a subject or database. [1] A subject's sensitive information can be considered as leaked if an adversary can infer its real value with a high confidence. [2] This is an example of breached information security.
Provides indicators of actions taken during each stage of the attack. [16] Communicates threat surfaces, attack vectors and malicious activities directed to both information technology and operational technology platforms. Serve as fact-based repository for evidence of both successful and unsuccessful cyber attacks.
In December 2016, the United States FBI and DHS issued a Joint Analysis Report which included attribution of Agent.BTZ to one or more "Russian civilian and military intelligence Services (RIS)." [ 6 ] In order to try to stop the spread of the worm, the Pentagon banned USB drives, and disabled the Windows autorun feature.
The resulting representation was called "attack trees." In 1998 Bruce Schneier published his analysis of cyber risks utilizing attack trees in his paper entitled "Toward a Secure System Engineering Methodology". [5] The paper proved to be a seminal contribution in the evolution of threat modeling for IT-systems.