Ensuring the security and integrity of computer network systems is utmost importance in today's digital landscape. Network intrusion detection (NIDS) play a critical role continuously monitoring traffic identifying unauthorized or potentially malicious activities that could compromise confidentiality, availability, these systems. However, traditional NIDS face daunting challenge effectively adapting to evolving tactics cyber attackers. To address this challenge, we propose multistage...

Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced security posture cybersecurity operations centers (defenders) through development ML-aided network intrusion detection systems (NIDS). Concurrently, abilities adversaries to evade also increased support AI/ML models. Therefore, defenders need proactively prepare for evasion attacks that exploit mechanisms NIDS. studies found...

Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced security posture cybersecurity operations centers (defenders) through development ML-aided network intrusion detection systems (NIDS). Concurrently, abilities adversaries to evade also increased support AI/ML models. Therefore, defenders need proactively prepare for evasion attacks that exploit mechanisms NIDS. studies found...

Cyber vulnerabilities are security deficiencies in computer and network systems of organizations, which can be exploited by an adversary to cause significant damage. The technology personnel resources currently available organizations mitigate the highly inadequate. As a result, routinely remain unpatched, thus making them vulnerable breaches from adversaries. potential consequences vulnerability depend upon context as well severity vulnerability, may differ among networks organizations....

Network intrusion detection systems (NIDS) play a critical role in discerning between benign and malicious network traffic. Deep neural networks (DNNs), anchored on large diverse datasets, exhibit promise enhancing the accuracy of NIDS by capturing intricate traffic patterns. However, safeguarding distributed computer against emerging cyber threats is increasingly challenging. Despite abundance data, decentralization persists due to data privacy security concerns. This confers an asymmetric...

Ensuring the security and integrity of computer network systems is utmost importance in today's digital landscape. Network intrusion detection (NIDS) play a critical role continuously monitoring traffic identifying unauthorized or potentially malicious activities that could compromise confidentiality, availability, these systems. However, traditional NIDS face daunting challenge effectively adapting to evolving tactics cyber attackers. To address this challenge, we propose multistage...

Anomaly detection is critical for network security. Unsupervised learning models trained on benign traffic data aim to detect anomalies without relying attack sets. Autoencoder-based have emerged as a promising approach detecting in intrusion data. While autoencoder predominantly been utilized flow-based approaches, which are suitable offline analysis, there notable gap research concerning unsupervised learning, particularly autoencoder-based techniques, packetbased detection. Packet-based...

Intelligence, surveillance, and reconnaissance (ISR) systems assist the defense military in their tactical operations by gathering movement intelligence data for tracking adversaries activities an area-of-interest. However, there are significant spatio-temporal gaps collected due to short track durations discontinuous coverage. As a result, ISR operators or analysts unable connect incomplete set of movements detect threats form salient adversaries. Our proposed approach aims fill this gap...

<p>Packet-based network intrusion detection systems (NIDS) allow for real-time detection, making this research area crucial. This study compares autoencoder models anomaly in packet-based NIDS. It presents a framework implementing an autoencoder-based NIDS using packet data. A novel metric reconstruction error autoencoders is introduced. evaluated at different thresholds to compare how accurately it detects traffic anomalies. The efficacy of showcased across various attacks and...

<p>Packet-based network intrusion detection systems (NIDS) allow for real-time detection, making this research area crucial. This study compares autoencoder models anomaly in packet-based NIDS. It presents a framework implementing an autoencoder-based NIDS using packet data. A novel metric reconstruction error autoencoders is introduced. evaluated at different thresholds to compare how accurately it detects traffic anomalies. The efficacy of showcased across various attacks and...

Cyber vulnerability management is a critical function of cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems. Adversaries hold an asymmetric advantage over the CSOC, as number deficiencies in these systems increasing at significantly higher rate compared to expansion security teams mitigate them resource-constrained environment. The current approaches are deterministic one-time decision-making methods, which do...