Related papers: Reconstruction-based LSTM-Autoencoder for Anomaly-…

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard…

One of the most challenging problems in the field of intrusion detection is anomaly detection for discrete event logs. While most earlier work focused on applying unsupervised learning upon engineered features, most recent work has started…

Due to the growing amount of data from in-situ sensors in wastewater systems, it becomes necessary to automatically identify abnormal behaviours and ensure high data quality. This paper proposes an anomaly detection method based on a deep…

Distributed Denial of Service (DDoS) attacks have emerged as a popular means of causing mass targeted service disruptions, often for extended periods of time. The relative ease and low costs of launching such attacks, supplemented by the…

recent literature has proposed various detection and identification methods for FDIAs, but few studies have focused on a solution that would prevent such attacks from occurring. However, great strides have been made using deep learning to…

In recent years, computer networks have become more and more advanced in terms of size, applications, complexity and level of heterogeneity. Moreover, availability and performance are important issues for end users. New types of…

Anomaly detection using a network-based approach is one of the most efficient ways to identify abnormal events such as fraud, security breaches, and system faults in a variety of applied domains. While most of the earlier works address the…

Denial-of-Service (DoS) attacks remain a critical threat to network security, disrupting services and causing significant economic losses. Traditional detection methods, including statistical and rule-based models, struggle to adapt to…

Recent efforts towards video anomaly detection (VAD) try to learn a deep autoencoder to describe normal event patterns with small reconstruction errors. The video inputs with large reconstruction errors are regarded as anomalies at the test…

Distributed Denial of Service (DDoS) attacks remain a persistent threat to the availability of Internet services, edge networks, and cyber-physical infrastructure. Although recent AI-security work has increasingly focused on foundation…

We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time…

Distributed Denial-of-Service (DDoS) attacks remain a serious threat to online infrastructure, often bypassing detection by altering traffic in subtle ways. We present a method using hive-plot sequences of network data and a 3D…

This paper presents a simple yet efficient method for an anomaly-based Intrusion Detection System (IDS). In reality, IDSs can be defined as a one-class classification system, where the normal traffic is the target class. The high diversity…

This paper presents a new solution for reconstructing missing data in power system measurements. An Enhanced Denoising Autoencoder (EDAE) is proposed to reconstruct the missing data through the input vector space reconstruction based on the…

Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass…

As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy risks. Anomaly-based…

Anomaly subsequence detection is to detect inconsistent data, which always contains important information, among time series. Due to the high dimensionality of the time series, traditional anomaly detection often requires a large time…

Unsupervised anomaly detection (UAD) has been widely implemented in industrial and medical applications, which reduces the cost of manual annotation and improves efficiency in disease diagnosis. Recently, deep auto-encoder with its variants…

The Automatic Dependent Surveillance Broadcast protocol is one of the latest compulsory advances in air surveillance. While it supports the tracking of the ever-growing number of aircraft in the air, it also introduces cybersecurity issues…

The rapid growth in web-based services has significantly increased security risks related to user information, as web-based attacks become increasingly sophisticated and prevalent. Traditional security methods frequently struggle to detect…