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    Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control Systems
    (Mdpi, 2024) Tanyildiz, Hayriye; Sahin, Canan Batur; Dinler, Ozlem Batur
    Industrial control systems (ICSs) are critical components automating the processes and operations of electromechanical systems. These systems are vulnerable to cyberattacks and can be the targets of malicious activities. With increased internet connectivity and integration with the Internet of Things (IoT), ICSs become more vulnerable to cyberattacks, which can have serious consequences, such as service interruption, financial losses, and security hazards. Threat actors target these systems with sophisticated attacks that can cause devastating damage. Cybersecurity vulnerabilities in ICSs have recently led to increasing cyberattacks and malware exploits. Hence, this paper proposes to develop a security solution with dynamic and adaptive deceptive patching strategies based on studies on the use of deceptive patches against attackers in industrial control systems. Within the present study's scope, brief information on the adversarial training method and window size manipulation will be presented. It will emphasize how these methods can be integrated into industrial control systems and how they can increase cybersecurity by combining them with deceptive patch solutions. The discussed techniques represent an approach to improving the network and system security by making it more challenging for attackers to predict their targets and attack methods. The acquired results demonstrate that the suggested hybrid method improves the application of deception to software patching prediction, reflecting enhanced patch security.
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    Öğe
    Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features
    (Springer, 2021) Sahin, Canan Batur; Dinler, Ozlem Batur; Abualigah, Laith
    The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.
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    Öğe
    Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System
    (IEEE-Inst Electrical Electronics Engineers Inc, 2019) Sahin, Canan Batur; Diri, Banu
    Stability and robustness of feature selection techniques have great importance in the high dimensional and small sample data. The neglected subject in the feature selection is solving the instability problem. Therefore, an ensemble gene selection framework is used in order to provide stable and accurate results of feature selection algorithms. Sequence modeling from high-dimensional data is an important research area for the discovery of biomarkers. Identifying biomarkers requires robust gene selection methods, which makes it possible to find important tumor-related genes with high accuracy. The main issue of this paper is creating a model in order to learn long sequences with the artificial immune recognition system (AIRS) for robust feature selection. Long short-term memory (LSTM) recurrent neural networks are trained with the AIRS in order to obtain the long-lived unit cells for use in the feature selection process. LSTM was used to be better understanding the mechanisms involving the remember'' feature of the immunological behavior of the immune response. We tried to apply a theory suggested by immunologists in order to develop stable associative memory, which capable of solving robustness and optimization tasks. We examined the initial gene selection step based on the different types of group formation algorithm for analysis of the most informative selected features. Microarray datasets are showing remarkable increases in their robustness and classification accuracy. The suggested framework is evaluated on six commonly used microarray datasets.