Related papers: A flow-based IDS using Machine Learning in eBPF

As Artificial Intelligence (AI) technologies continue to gain traction in the modern-day world, they ultimately pose an immediate threat to current cybersecurity systems via exploitative methods. Prompt engineering is a relatively new field…

A significant increase in the number of interconnected devices and data communication through wireless networks has given rise to various threats, risks and security concerns. Internet of Things (IoT) applications is deployed in almost…

Rising complexity of in-vehicle electronics is enabling new capabilities like autonomous driving and active safety. However, rising automation also increases risk of security threats which is compounded by lack of in-built security measures…

IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and…

By placing computation resources within a one-hop wireless topology, the recent edge computing paradigm is a key enabler of real-time Internet of Things (IoT) applications. In the context of IoT scenarios where the same information from a…

As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems (IDS) has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated…

Despite decades of development, existing IDSs still face challenges in improving detection accuracy, evasion, and detection of unknown attacks. To solve these problems, many researchers have focused on designing and developing IDSs that use…

Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real…

The presence of missing values within high-dimensional data is an ubiquitous problem for many applied sciences. A serious limitation of many available data mining and machine learning methods is their inability to handle partially missing…

Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in…

Existing model-based processes for embedded real-time systems support the analysis of various non-functional properties, most notably schedulability, through model checking, simulation or other means. The analysis results are then used for…

In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical…

Working set size estimation (WSS) is of great significance to improve the efficiency of program executing and memory arrangement in modern operating systems. Previous work proposed several methods to estimate WSS, including self-balloning,…

Globally, the external internet is increasingly being connected to industrial control systems. As a result, there is an immediate need to protect these networks from a variety of threats. The key infrastructure of industrial activity can be…

In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is…

Software-Defined Networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches. The most important element…

Network intrusions have become a significant threat in recent years as a result of the increased demand of computer networks for critical systems. Intrusion detection system (IDS) has been widely deployed as a defense measure for computer…

eBPF is a technology that allows developers to safely extend kernel functionality without modifying kernel source code or developing loadable kernel modules. Since the kernel governs critical system operations and enforces isolation…

Searchable Symmetric Encryption (SSE) allows users to search over encrypted data stored on untrusted servers, like cloud providers. While SSE hides the content of queries and documents, it still leaks patterns, such as how often a query is…

Modern networks carry increasingly diverse and encrypted traffic types that demand classification techniques beyond traditional port-based and payload-based methods. This tutorial provides a practical, end-to-end guide to building…