A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
Project compares KDDCup99 and NSL-KDD datasets using ML classifiers.
Study detects and mitigates stealthy DDoS attacks in IoT networks.
To reap the benefits of the Internet of Things (IoT), it is imperative to secure the system against cyber attacks in order to enable mission critical and real-time applications. To this end, intrusion detection systems (IDSs) have been widely used to detect anomalies caused by a cyber attacker in IoT systems. However, …
This paper improves AI defenses against network attacks using ML and adversarial learning.
Adaptive anomaly detection for IoT data reduces delay by 84%.
This paper shows diffusion models improve intrusion detection by purifying adversarial examples.
Enhances network intrusion detection in noisy data.
Paper uses ensemble learning for IoT cybersecurity anomaly detection.
Purveyors of malicious network attacks continue to increase the complexity and the sophistication of their techniques, and their ability to evade detection continues to improve as well. Hence, intrusion detection systems must also evolve to meet these increasingly challenging threats. Machine learning is often used to …
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
Paper proposes SDS for 5G security using machine learning.
ReRe detects anomalies in real-time for time series data.
Proposes SOMDAGMM for more accurate network intrusion detection.
With the growth of adversarial attacks against machine learning models, several concerns have emerged about potential vulnerabilities in designing deep neural network-based intrusion detection systems (IDS). In this paper, we study the resilience of deep learning-based intrusion detection systems against adversarial at…
Deep learning detects cyber-attacks in smart grid systems.
EagerNet detects network attacks quickly with less resources.
Deep learning identifies unknown IoT devices in network traffic.
The techniques of deep learning have become the state of the art methodology for executing complicated tasks from various domains of computer vision, natural language processing, and several other areas. Due to its rapid development and promising benchmarks in those fields, researchers started experimenting with this t…
The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to diffe…
Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to …
RAID algorithm detects anomalies in real-time IoT systems.
Paper proposes personalized climate control for driver comfort.
User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a principled sta…
Growing number of network devices and services have led to increasing demand for protective measures as hackers launch attacks to paralyze or steal information from victim systems. Intrusion Detection System (IDS) is one of the essential elements of network perimeter security which detects the attacks by inspecting net…
Random Forest outperforms other IDS algorithms in smart grids.
This paper proposes a communication-efficient deep anomaly detection framework for industrial IoT.
New NIDS uses hypergraphs for real-time detection of evolving port scans.
Survey examines ML for IoT security, addressing new challenges.
Neural networks are increasingly used for intrusion detection on industrial control systems (ICS). With neural networks being vulnerable to adversarial examples, attackers who wish to cause damage to an ICS can attempt to hide their attacks from detection by using adversarial example techniques. In this work we address…
Personalized deep learning reduces inappropriate shocks in VA detection.
In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find th…
Bayesian Optimization improves machine learning for detecting network attacks.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
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 of network attacks in addition to the need for generalization, motivate us to pr…
Study efficient resource allocation for detecting extreme values.
Study categorizes time series anomaly detection metrics based on evaluation challenges.
This paper presents a method called One-class Classification using Length statistics of Emerging Patterns Plus (OCLEP+).
This paper studies adversarial examples in NIDS, revealing their vulnerability.
Proposes an autonomous IDS using multiple learning techniques.
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations o…
Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection…
DiFF-RF detects point-wise and collective anomalies using random partitioning trees.
Intrusion Detection System (IDS) is one of the most effective solutions for providing primary security services. IDSs are generally working based on attack signatures or by detecting anomalies. In this paper, we have presented AutoIDS, a novel yet efficient solution for IDS, based on a semi-supervised machine learning …
Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph. In these applications presence of intrusion, community or disease outbreak is characterized by novel observations on some unknown connected subgraph. These prob…
Paper speeds up IoT device detection and data decoding.
This paper uses deep learning to improve network threat detection in finance.
Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive …