Intrusion detection is a predominant task that monitors and protects the network infrastructure. Therefore, many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection. In particular, the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) is an extensively used benchmark dataset for evaluating intrusion detection systems (IDSs) as it incorporates various network traffic attacks. It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models, but the performance of these models often decreases when evaluated on new attacks. This has led to the utilization of deep learning techniques, which have showcased significant potential for processing large datasets and therefore improving detection accuracy. For that reason, this paper focuses on the role of stacking deep learning models, including convolution neural network (CNN) and deep neural network (DNN) for improving the intrusion detection rate of the NSL-KDD dataset. Each base model is trained on the NSL-KDD dataset to extract significant features. Once the base models have been trained, the stacking process proceeds to the second stage, where a simple meta-model has been trained on the predictions generated from the proposed base models. The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate. Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection. The performance of the ensemble of base models, combined with the meta-model, exceeds the performance of individual models. Our stacking model has attained an accuracy of 99% and an average F1-score of 93% for the multi-classification scenario. Besides, the training time of the proposed ensemble model is lower than the training time of benchmark techniques, demonstrating its efficiency and robustness.

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.

Identifying human actions and interactions finds its use in many areas, such as security, surveillance, assisted living, patient monitoring, rehabilitation, sports, and e-learning. This wide range of applications has attracted many researchers to this field. Inspired by the existing recognition systems, this paper proposes a new and efficient human-object interaction recognition (HOIR) model which is based on modeling human pose and scene feature information. There are different aspects involved in an interaction, including the humans, the objects, the various body parts of the human, and the background scene. The main objectives of this research include critically examining the importance of all these elements in determining the interaction, estimating human pose through image foresting transform (IFT), and detecting the performed interactions based on an optimized multi-feature vector. The proposed methodology has six main phases. The first phase involves preprocessing the images. During preprocessing stages, the videos are converted into image frames. Then their contrast is adjusted, and noise is removed. In the second phase, the human-object pair is detected and extracted from each image frame. The third phase involves the identification of key body parts of the detected humans using IFT. The fourth phase relates to three different kinds of feature extraction techniques. Then these features are combined and optimized during the fifth phase. The optimized vector is used to classify the interactions in the last phase. The MSR Daily Activity 3D dataset has been used to test this model and to prove its efficiency. The proposed system obtains an average accuracy of 91.7% on this dataset.