Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Abstract

The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.

Energy Consumption of Methods for Pattern Recognition using Microcontrollers

“…In future, authors are going to explore machine learning methods and various types of neural networks [26,35] for a more detailed analysis in energy efficiency terms and power consumption optimization of autonomous MCU units in IoT.…”

Robust Symmetry Prediction with Multi-Modal Feature Fusion for Partial Shapes

“…where h is the global network and Agg is the aggregation function. The aggregation can be implemented as averaging [4], concatenation [5,10], and self-attention [11], and we use concatenation here simply. To the end, we voxelized the point clouds together with their color features x rgb and depth features x d from CTN.…”

“…e.g., a shape can be aligned to the principal axes, then planes formed by pairs of principal axes can be checked to see if they are symmetry planes. Recently, deep learning-based methods leverage a neural network to extract global features of the shape, which is used to capture possible symmetry [4,5].…”

Інтелектуальна Система Моніторингу Та Аналізу Трафіку Для Виявлення Атак В Програмно-Конфігурованих Мережах

“…У ситуації, коли вхідний потік не розпізнає нормальну поведінку, атака реєструється. Отже, поєднавши дві різні нейронні мережі, можна визначити та розпізнати інформаційні атаки із доволі високим ступенем точності [10]. Основні переваги використання підходів, основаних на нейронних мережах, -можливість адаптації до динамічних умов та показників продуктивності, що особливо важливо, коли система працює у режимі реального часу Ключовою особливістю запропонованої IDS є оптимальне вилучення сигнатур для зменшення обчислювальної складності та покращення виявлення раніше невідомих атак.…”