Related papers: Insider Threat Detection Based on Stress Recogniti…

Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can…

In big data systems, the infrastructure is such that large amounts of data are hosted away from the users. In such a system information security is considered as a major challenge. From a customer perspective, one of the big risks in…

Insider threats are the cyber attacks from within the trusted entities of an organization. Lack of real-world data and issue of data imbalance leave insider threat analysis an understudied research area. To mitigate the effect of skewed…

Insider threat is one of the most pernicious threat vectors to information and communication technologies (ICT)across the world due to the elevated level of trust and access that an insider is afforded. This type of threat can stem from…

Reliably identifying and verifying subjects remains integral to computer system security. Various novel authentication techniques, such as biometric authentication systems, have been developed in recent years. This paper provides a detailed…

We describe the motivation and design for esINSIDER, an automated tool that detects potential persistent and insider threats in a network. esINSIDER aggregates clues from log data, over extended time periods, and proposes a small number of…

Conventional security solutions are insufficient to address the urgent cybersecurity challenge posed by insider attacks. While a great deal of research has been done in this area, our systematic literature analysis attempts to give readers…

Biometrics technologies are gaining popularity today since they provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the…

Insider threats are costly, hard to detect, and unfortunately rising in occurrence. Seeking to improve detection of such threats, we develop novel techniques to enable us to extract powerful features and augment attack vectors for greater…

Intrusion Detection is one of major threats for organization. The approach of intrusion detection using text processing has been one of research interests which is gaining significant importance from researchers. In text mining based…

Insider Attack Detection in commercial networks is a critical problem that does not have any good solutions at this current time. The problem is challenging due to the lack of visibility into live networks and a lack of a standard feature…

The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Keystroke dynamics can be used to analyze the way that a user types based on various…

The massive explosion and ubiquity of computing devices and the outreach of the web have been the most defining events of the century so far. As more and more people gain access to the internet, traditional know-something and have-something…

Insider threat detection has been a challenging task over decades, existing approaches generally employ the traditional generative unsupervised learning methods to produce normal user behavior model and detect significant deviations as…

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that…

The employees of any organization, institute, or industry, spend a significant amount of time on a computer network, where they develop their own routine of activities in the form of network transactions over a time period. Insider threat…

Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model. In this paper, we show that prior…

Neural network inference typically operates on raw input data, increasing the risk of exposure during preprocessing and inference. Moreover, neural architectures lack efficient built-in mechanisms for directly authenticating input data.…

A vision-based keystroke inference attack is a side-channel attack in which an attacker uses an optical device to record users on their mobile devices and infer their keystrokes. The threat space for these attacks has been studied in the…

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to…