Fooling intrusion detection systems using adversarially autoencoder

Adversarially Regularized Graph Autoencoder

Graph embedding is an e‚ective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which o‰en results in inferior embedding in real-worl...

Paraphrase Detection Using Recursive Autoencoder

In this paper, we tackle the paraphrase detection task. We present a novel recursive autoencoder architecture that learns representations of phrases in an unsupervised way. Using these representations, we are able to extract features for classification algorithms that allow us to outperform many results from previous works.

Intrusion Detection Using Evolutionary Hidden Markov Model

Intrusion detection systems are responsible for diagnosing and detecting any unauthorized use of the system, exploitation or destruction, which is able to prevent cyber-attacks using the network package analysis. one of the major challenges in the use of these tools is lack of educational patterns of attacks on the part of the engine analysis; engine failure that caused the complete training,  ...

Intrusion Detection Systems and Intrusion Prevention Systems

Improving Accuracy in Intrusion Detection Systems Using Classifier Ensemble and Clustering

Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be cons...