Honeypot is a network environment used to protect the legitimate network resources from attacks. Honeypot creates an environment that impresses attackers to inject their activities to steal resources. This is a way to detect the attacks by doing attack detection procedures. In this work, Denial of Service (DoS) attacks are effectively detected by proposed honeypot system. Machine Learning (ML) and Deep Learning (DL) methods evolve in many areas to build intelligent decision making systems. This work uses DL approaches and secures event validation procedures for finding predicting DoS attacks. The proposed system called Deep Adaptive Reinforcement Learning for Honeypots (DARLH) is implemented to monitor internal and external DoS attacks. In the honeypot environment, the proposed DARLH system implements DARL based IDS (Intrusion Detection System) agents and Deep Recurrent Neural Network (DRNN) based IDS agents for monitoring multiple runtime DoS attacks. These techniques support for dynamic IDS against DoS attack. In addition, the DARLH creates protected poison distribution and server side supervision system for keeping the monitoring events legitimate.This work is implemented and performance is evaluated. The results are compared with existing 2 systems like GNBH, BCH and RNSG. In this comparison, the proposed system provides 5% to 10% better results than other systems.

The presence of shadows degrades the performance of many computer vision and video surveillance applications, as objects can be incorrectly classified. The article proposes a method for detecting moving shadows using stationary wavelet transform (SWT) and Zernike moments (ZM) based on an automatic threshold determined by the wavelet coefficients. The multi-resolution and shift invariance properties of the SWT make it suitable for change detection and feature extraction. To reduce the redundant wavelet coefficients, ZM are applied. The novelty of the proposed method is the determination of the variant statistical threshold-'skewness', without the requirement of any supervised learning or manual calibration. The experimental results prove that the proposed threshold performs well to show a better variation between the objects and shadows in various environments.

Honeypot is a network environment used to protect the legitimate network resources from attacks. Honeypot creates an environment that impresses attackers to inject their activities to steal resources. This is a way to detect the attacks by doing attack detection procedures. In this work, Denial of Service (DoS) attacks are effectively detected by proposed honeypot system. Machine Learning (ML) and Deep Learning (DL) methods evolve in many areas to build intelligent decision making systems. This work uses DL approaches and secures event validation procedures for finding predicting DoS attacks. The proposed system called Deep Adaptive Reinforcement Learning for Honeypots (DARLH) is implemented to monitor internal and external DoS attacks. In the honeypot environment, the proposed DARLH system implements DARL based IDS (Intrusion Detection System) agents and Deep Recurrent Neural Network (DRNN) based IDS agents for monitoring multiple runtime DoS attacks. These techniques support for dynamic IDS against DoS attack. In addition, the DARLH creates protected poison distribution and server side supervision system for keeping the monitoring events legitimate. This work is implemented and performance is evaluated. The results are compared with existing systems like GNBH, BCH and RNSG. In this comparison, the proposed system provides 5–10% better results than other systems.

Many surveillance and forensic applications face problems in identifying shadows and their removal. The moving shadow points overlap with the moving objects in a video sequence leading to misclassification of the exact object. This article presents a novel method for identifying and removing moving shadows using stationary wavelet transform (SWT) based on a threshold determined by wavelet coefficients. The multi-resolution property of the stationary wavelet transform leads to the decomposition of the frames into four different bands without the loss of spatial information. The conventional discrete wavelet transform (DWT), which has the same property, suffers from the problem of shift invariance due to the decimation operation leading to a shift in the original signal during reconstruction. Since SWT does not have the decimation operation, the problem of shift invariance is solved which makes it feasible for change detection, pattern recognition and feature extraction and retrieves the original signal without the loss of phase information also. For detection and removal of shadow, a new threshold in the form of a variant statistical parameter-"skewness"-is proposed. The value of threshold is determined through the wavelet coefficients without the requirement of any supervised learning or manual calibration. Normally, the statistical parameters like mean, variance and standard deviation does not show much variation in complex environments. Skewness shows a unique variation between the shadow and non-shadow pixels in various environments than the previously used thresholds-standard deviation and relative standard deviation. The experimental results prove that the proposed method works better than other state-of-art-methods.

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