Related papers: A Simple Framework to Enhance the Adversarial Robu…
With increasingly sophisticated cybersecurity threats and rising demand for network automation, autonomous cybersecurity mechanisms are becoming critical for securing modern networks. The rapid expansion of Internet of Things (IoT) systems…
In RPL security, intrusion detection (ID) plays a vital role, especially given its susceptibility to attacks, particularly those carried out by insider threats. While numerous studies in the literature have proposed intrusion detection…
Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly…
The growing interest in the Internet of Things (IoT) applications is associated with an augmented volume of security threats. In this vein, the Intrusion detection systems (IDS) have emerged as a viable solution for the detection and…
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our…
Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and…
Machine learning (ML)-based intrusion detection systems (IDSs) play a critical role in discovering unknown threats in a large-scale cyberspace. They have been adopted as a mainstream hunting method in many organizations, such as financial…
Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. Such attacks can also target multiple components of a Supply Chain, which…
The rapid advancement of machine learning (ML) and on-device computing has revolutionized various industries, including transportation, through the development of Connected and Autonomous Vehicles (CAVs) and Intelligent Transportation…
As the number of cyber-attacks is increasing, cybersecurity is evolving to a key concern for any business. Artificial Intelligence (AI) and Machine Learning (ML) (in particular Deep Learning - DL) can be leveraged as key enabling…
As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management. But it is also confronted with various security challenges and potential…
The escalating frequency of intrusions in networked systems has spurred the exploration of new research avenues in devising artificial intelligence (AI) techniques for intrusion detection systems (IDS). Various AI techniques have been used…
With massive data being generated daily and the ever-increasing interconnectivity of the world's Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and…
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…
Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning…
Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both gradient-based and distribution shift threat models. Existing defenses typically apply…
In todays rapidly evolving digital landscape, safeguarding network infrastructures against cyberattacks has become a critical priority. This research presents an innovative AI-driven real-time intrusion detection framework designed to…
Network intrusion detection systems (NIDS) to detect malicious attacks continue to meet challenges. NIDS are often developed offline while they face auto-generated port scan infiltration attempts, resulting in a significant time lag from…
Adversarial attacks pose a significant threat to the reliability of pre-trained language models (PLMs) such as GPT, BERT, RoBERTa, and T5. This paper presents Adversarial Robustness through Dynamic Ensemble Learning (ARDEL), a novel scheme…
Cyber threats are increasing not only in their volume but also in their sophistication and difficulty to detect. Attacks have become a national/global threat as they have targeted private and public, as well as government sectors over the…