The unprecedented growth in production and exchange of multimedia over unsecured channels is overwhelming mathematicians, scientists and engineers to realize secure and efficient cryptographic algorithms. In this paper, a color image encryption algorithm combining the KAA map with multiple chaotic maps is proposed. The proposed algorithm makes full use of Shannon's ideas of security, such that image encryption is carried out through bit confusion and diffusion. Confusion is carried out through employing 2 encryption keys. The first key is generated from the 2D Logistic Sine map and a Linear Congruential Generator, while the second key is generated from the Tent map and the Bernoulli map. Diffusion is attained through the use of the KAA map. An elaborate mathematical analysis is carried out to showcase the robustness and efficiency of the proposed algorithm, as well as its resistance to visual, statistical, differential and brute-force attacks. Moreover, the proposed image encryption algorithm is also shown to successfully pass all the tests of the NIST SP 800 suite.

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Network intrusion detection systems (NIDS) are the most common tool used to detect malicious attacks on a network. They help prevent the ever-increasing different attacks and provide better security for the network. NIDS are classified into signature-based and anomaly-based detection. The most common type of NIDS is the anomaly-based NIDS which is based on machine learning models and is able to detect attacks with high accuracy. However, in recent years, NIDS has achieved even better results in detecting already known and novel attacks with the adoption of deep learning models. Benchmark datasets in intrusion detection try to simulate real-network traffic by including more normal traffic samples than the attack samples. This causes the training data to be imbalanced and causes difficulties in detecting certain types of attacks for the NIDS. In this paper, a data resampling technique is proposed based on Adaptive Synthetic (ADASYN) and Tomek Links algorithms in combination with different deep learning models to mitigate the class imbalance problem. The proposed model is evaluated on the benchmark NSL-KDD dataset using accuracy, precision, recall and F-score metrics. The experimental results show that in binary classification, the proposed method improves the performance of the NIDS and outperforms state-of-the-art models with an achieved accuracy of 99.8%. In multi-class classification, the results were also improved, outperforming state-of-the-art models with an achieved accuracy of 99.98%.

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