Anomaly detection on bipartite graphs for cyber situational awareness and threat detection

2017 ◽  
Author(s):  
Mohammed Eslami ◽  
George Zheng ◽  
Hamed Eramian ◽  
Georgiy Levchuk
Keyword(s):  
Anomaly Detection ◽  
Situational Awareness ◽  
Bipartite Graphs ◽  
Threat Detection

An email content-based insider threat detection model using anomaly detection algorithms

2021 ◽  
Author(s):  
NaanKang Garba ◽  
Sandip Rakshit ◽  
Chai Dakun Maa ◽  
Narasimha Rao Vajjhala
Keyword(s):  
Anomaly Detection ◽  
Insider Threat ◽  
Threat Detection ◽  
Detection Model ◽  
Detection Algorithms

Relevance search and anomaly detection in bipartite graphs

2005 ◽  
Vol 7 (2) ◽  
pp. 48-55 ◽  
Author(s):  
Jimeng Sun ◽  
Huiming Qu ◽  
Deepayan Chakrabarti ◽  
Christos Faloutsos
Keyword(s):  
Anomaly Detection ◽  
Bipartite Graphs

scholarly journals Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness

2021 ◽  
Author(s):  
Stefano Maria Iacus ◽  
Francesco Sermi ◽  
Spyridon Spyratos ◽  
Dario Tarchi ◽  
Michele Vespe
Keyword(s):  
Anomaly Detection ◽  
Situational Awareness ◽  
Mobile Positioning

scholarly journals Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches

2022 ◽  
Vol 10 (1) ◽  
pp. 112
Author(s):  
Konrad Wolsing ◽  
Linus Roepert ◽  
Jan Bauer ◽  
Klaus Wehrle
Keyword(s):  
Risk Assessment ◽  
Anomaly Detection ◽  
Situational Awareness ◽  
Automatic Detection ◽  
Use Cases ◽  
Research Articles ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Potential Use

The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations.

scholarly journals Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms

2019 ◽  
Vol 9 (19) ◽  
pp. 4018 ◽  
Author(s):  
Kim ◽  
Park ◽  
Kim ◽  
Cho ◽  
Kang
Keyword(s):  
Anomaly Detection ◽  
User Behavior ◽  
Behavior Modeling ◽  
Detection Methods ◽  
Insider Threat ◽  
Threat Detection ◽  
Insider Threats ◽  
Domain Experts ◽  
User Behavior Modeling ◽  
Detection Algorithms

Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very familiar with an organization’s system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based on user behavior modeling and anomaly detection algorithms. Based on user log data, we constructed three types of datasets: user’s daily activity summary, e-mail contents topic distribution, and user’s weekly e-mail communication history. Then, we applied four anomaly detection algorithms and their combinations to detect malicious activities. Experimental results indicate that the proposed framework can work well for imbalanced datasets in which there are only a few insider threats and where no domain experts’ knowledge is provided.

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