Abstract With widespread adoption across industries, Industrial Internet of Things (IIoT) environments have become prime targets for cyberattacks. Moreover, the complexity and scale of these attacks can involve highly sophisticated, artificial intelligence (AI)–enabled, and even autonomous capabilities, occurring at machine speeds and making conventional defensive mechanisms insufficient. Therefore, defensive systems must possess considerable autonomy to detect and mitigate such attacks effectively and promptly. This work presents an IIoT cyber defense system (NS-IoT) that integrates the sensitivity of Deep Reinforcement Learning (DRL) with the agility of multi-agent systems, providing an autonomous defense solution for distributed denial of service (DDoS) attacks. The NS-IoT system consists of two modules: detection and defense. For the detection module, a Deep Q-Network (DQN)-based agent (DQN-IoT) was developed to detect DDoS attacks. This agent employs DRL techniques to treat attack classification like a guessing game, leverages feedback to improve decision-making within the Markov Decision Process (MDP), and combines rewards for enhanced performance. In this study, DDoS attacks were detected using the proposed DQN-IoT model, achieving 98.43% and 98.05% accuracy on the CIC-IoT-2022 and CIC-IoT-2023 datasets, respectively. While these results highlight the model’s effectiveness, real-time response speed is crucial in real-time events. Therefore, the proposed NS-IoT system addresses this need with its autonomous multi-agent structure, which minimizes human intervention.

In recent years, with the development of IP-based systems, circuit-switched systems have rapidly started to be replaced by packet-switched systems in communication infrastructures and operators have started to prefer VoIP systems more due to their advantages such as cost and resource efficiency. However, since VoIP (Voice over Internet Protocol) systems are equally open to all threats to which IP-based systems are open, researchers have proposed different methods for obtaining strong security solutions. Recently, rule-based systems have been replaced by machine learning-based systems in many areas and different machine learning-based solutions have been suggested for VoIP security.In this study, a machine learning-based solution was proposed for detecting SIP flooding attacks within the scope of DoS (Denial of Service) attacks which is one of the current threats to VoIP infrastructure. For this purpose, a test environment was created on a previously developed simulation infrastructure, primarily normal traffic and attack traffic were generated, and then the effectiveness of certain machine learning methods in the classification of traffic was tested with the labeled data obtained.When the results are evaluated with the parameters based on the working conditions, it is observed that the related methods can produce meaningful results.

With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.

Abstract Breast cancer can be effectively detected and diagnosed using the technology of digital mammography. However, although this technology has been rapidly developing recently, suspicious regions cannot be detected in some cases by radiologists, because of the noise or inappropriate mammogram contrast. This study presents a classification of segmented region of interests (ROIs) as either benign or malignant to serve as a second eye of the radiologists. Our study consists of three steps. In the first step, spherical wavelet transform (SWT) is applied to the original ROIs. In the second step, shape, boundary and grey level based features of wavelet (detail) and scaling (approximation) coefficients are extracted. Finally, in the third step, malignant/benign classification of the masses is implemented by giving the feature matrices to a support vector machine system. The proposed system achieves 91.4% and 90.1% classification accuracy using the dataset acquired from the hospital of Istanbul University in Turkey and the free Mammographic Image Analysis Society, respectively. Furthermore, discrete wavelet transform, which produces 83.3% classification accuracy, is applied to the coefficients to make a comparison with the SWT method.

There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.

MOTIVATION: Gene therapy aims at using viral vectors for attaching helpful genetic code to target genes. Therefore, it is of great importance to develop methods that can discover significant patterns around viral integration sites. Canonical correlation analysis is an unsupervised statistical tool that is used to describe the relations between two related views of the same semantic object, which fits well for identifying such salient patterns. RESULTS: Proposed method is demonstrated on a sequence dataset obtained from a study on HIV-1 preferred integration regions. The subsequences on the left and right sides of the integration points are given to the method as the two views, and statistically significant relations are found between sequence-driven features derived from these two views, which suggest that the viral preference must be the factor responsible for this correlation. We found that there are significant correlations at x=5 indicating a palindromic behavior surrounding the viral integration site, which complies with the previously reported results. AVAILABILITY: Developed software tool is available at http://ce.istanbul.edu.tr/bioinformatics/hiv1/.