With the advancements in computer networks and systems, the number of security vulnerabilities and cyber attacks targeting/using these vulnerabilities continues to increase. Consequently, various intrusion detection systems (IDS) have been developed to detect cyber attacks and ensure information security. IDSs are categorized into two classes based on the data sources: Network-based intrusion detection system (NIDS) and host-based intrusion detection system (HIDS). In this systematic literature review (SLR), studies are examined that focus on HIDS or propose methods applicable to HIDS, as well as those related to IDSs that can be converted into HIDSs. The studies published between 2020 and 2023 are collected from widely used academic databases through various query statements. Filtering based on specific selection and elimination criteria is undergone by the collected studies, resulting in 21 studies for examination. Subsequently, these studies and their advantages and disadvantages are discussed. In addition, while examining the studies, five research questions are addressed. Finally, the defects, potential areas for improvement, and future research directions related to HIDSs are discussed.

The rapid increase in the number of cyber attacks and the emergence of various attack variations pose significant threats to the security of computer systems and networks. Various intrusion detection systems (IDS) are developed to defend computer systems and networks in response to these threats. One type of IDS, known as a host-based intrusion detection system (HIDS), focuses on securing a single host. Numerous HIDS have been proposed in the literature, incorporating various detection methods. This study develops multiple machine learning (ML) models and stacking ensemble based HIDS that can be used as detection methods in HIDS. Initially, n-grams, standard bag-of-words (BoW), binary BoW, probability BoW, and term frequency-inverse document frequency (TF-IDF) BoW methods are applied to the ADFA-LD and ADFA-WD datasets. Mutual information and k-means methods are used together for feature selection on the resulting BoW datasets. Individual models are created using either selected features or all features. Subsequently, the outputs of these individual models are used in extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) models to develop stacking ensemble based models. The experimental results show that the best accuracy (ACC) among models using ADFA-LD based BoW datasets is achieved by the stacking ensemble based XGBoost model, which has an ACC of 0.9747. This XGBoost model utilizes the standard BoW dataset and selected features. Among models using ADFA-WD based BoW datasets, the stacking ensemble based XGBoost is also the most successful in terms of ACC, with an ACC of 0.9163, using the standard BoW dataset and all features.

The security of lattice-based cryptosystems is based on solving hard lattice problems such as the shortest vector problem (SVP) and the closest vector problem (CVP). Various cryptanalysis algorithms such as (Pro)GaussSieve, HashSieve, ENUM, and BKZ have been proposed to solve these hard problems. Several implementations of these algorithms have been developed. On the other hand, the implementations of these algorithms are expected to be efficient in terms of run time and memory space. In this paper, a modular software package/library containing efficient implementations of GaussSieve, ProGaussSieve, HashSieve, and BKZ algorithms is developed. These implementations are considered efficient in terms of run time. While constructing this software library, some modifications to the algorithms are made to increase the performance. Then, the run times of these implementations are compared with the others. According to the experimental results, the proposed GaussSieve, ProGaussSieve, and HashSieve implementations are at least 70%, 75%, and 49% more efficient than previous ones, respectively.