Detection System of HTTP DDoS Attacks in a Cloud Environment Based on Information Theoretic Entropy and Random Forest

Cloud Computing services are often delivered through HTTP protocol. This facilitates access to services and reduces costs for both providers and end-users. However, this increases the vulnerabilities of the Cloud services face to HTTP DDoS attacks. HTTP request methods are often used to address web servers’ vulnerabilities and create multiple scenarios of HTTP DDoS attack such as Low and Slow or Flooding attacks. Existing HTTP DDoS detection systems are challenged by the big amounts of network traffic generated by these attacks, low detection accuracy, and high false positive rates. In this paper we present a detection system of HTTP DDoS attacks in a Cloud environment based on Information Theoretic Entropy and Random Forest ensemble learning algorithm. A time-based sliding window algorithm is used to estimate the entropy of the network header features of the incoming network traffic. When the estimated entropy exceeds its normal range the preprocessing and the classification tasks are triggered. To assess the proposed approach various experiments were performed on the CIDDS-001 public dataset. The proposed approach achieves satisfactory results with an accuracy of 99.54%, a FPR of 0.4%, and a running time of 18.5s

2L-ZED-IDS: A Two-Level Anomaly Detector for Multiple Attack Classes

Cloud computing is currently a thriving technology. Due to their critical nature, it is necessary to consider all kinds of intrusions and abuses that typically plague cloud environments. In order to maintain its resilient-state, a cloud system should have tools capable of detecting known and updated threats, but also unknown attacks (0-day). This paper presents a two-level deep learning architecture for detecting multiple attack classes. In particular, it is an extension of a previous study with a dual objective: reducing the false alarm rate and improving the detection rate, and testing the system with different types of attacks. The problem is treated as a semi-supervised task, and the anomaly detector exploits deep autoencoder building blocks. The model is described and tested on the recent CICIDS2017 and CSE-CIC-IDS2018 datasets. The performance comparison with our previous study shows a lower false alarm rate and the validity of the model for multiple attack classes

On a new law of bone remodeling based on damage elasticity: a thermodynamic approach

<p>Abstract</p> <p>Background</p> <p>Bone tissue is the main element of the human skeleton and is a dynamic tissue that is continuously renewed by bone-resorbing osteoclasts and bone-forming osteoblasts.</p> <p>The bone is also capable of repairing itself and adapting its structure to changes in its load environment through the process of bone remodeling.</p> <p>Therefore, this phenomenon has been gaining increasing interest in the last years and many laws have been developed in order to simulate this process.</p> <p>Results</p> <p>In this paper, we develop a new law of bone remodeling in the context of damaged elastic by applying the thermodynamic approach in the case of small perturbations.</p> <p>The model is solved numerically by a finite difference method in the one-dimensional bone structure of a n-unit elements model.</p> <p>Conclusion</p> <p>In addition, several numerical simulations are presented that confirm the accuracy and effectiveness of the model.</p