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Security attacks become daily news due to an exposure of a security threat in a widely used software. Taking software security into consideration during the analysis, design, and implementation phases is a must. A software application should be protected against any security threat such as unauthorized distribution or code retrieval. Due to the lack of applying a software security standard architecture, developers may create software that may be vulnerable to many types of security threats. This paper begins by reviewing different types of known software security threats and their countermeasure mechanisms. Then, it proposes a new security optimization architecture for software applications. This architecture is a step towards establishing a standard to guarantee the software's security. Furthermore, it proposes an adapted software security optimization architecture for mobile applications. Besides, it presents an algorithmic implementation of the newly proposed architecture, then it proves its security. Moreover, it builds a secure mobile application based on the newly proposed architecture.

Numerous network cyberattacks have been launched due to inherent weaknesses. Network intrusion detection is a crucial foundation of the cybersecurity field. Intrusion detection systems (IDSs) are a type of machine learning (ML) software proposed for making decisions without explicit programming and with little human intervention. Although ML-based IDS advancements have surpassed earlier methods, they still struggle to identify attack types with high detection rates (DR) and low false alarm rates (FAR). This paper proposes a meta-heuristic optimization algorithm-based hierarchical IDS to identify several types of attack and to secure the computing environment. The proposed approach comprises three stages: The first stage includes data preprocessing, feature selection, and the splitting of the dataset into multiple binary balanced datasets. In the second stage, two novel meta-heuristic optimization algorithms are introduced to optimize the hyperparameters of the extreme learning machine during the construction of multiple binary models to detect different attack types. These are combined in the last stage using an aggregated anomaly detection engine in a hierarchical structure on account of the model’s accuracy. We propose a software machine learning IDS that enables multi-class classification. It achieved scores of 98.93, 99.63, 99.19, 99.78, and 0.01, with 0.51 for average accuracy, DR, and FAR in the UNSW-NB15 and CICIDS2017 datasets, respectively.

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