MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach

Controller area network (CAN) bus which provides efficient, reliable and robust communication between electronic control units (ECUs) is the most frequently used protocol for in-vehicle networks. However, the lack of security mechanisms in the CAN protocol makes it vulnerable to inside and outside cyber-attacks. Therefore, an intrusion detection system (IDS), which is a widely used method to detect malicious activities, is preferred to improve the security of the CAN buses. In spite of the fact that various supervised and unsupervised machine learning algorithms are employed to increase the performance of IDSs, obtaining high classification performance is still a challenge for them. First, a lot of irrelevant and redundant features in the datasets result in long computational times with low detection performances. Second, different classification performances are acquired based on classifiers and a combination of the features. Third, many of the models suffer from unknown and different types of attacks. For these reasons, a new intrusion detection framework is proposed in this paper based on feature selection and classifier. Initially, we propose a meta-heuristic algorithm called modified genetic algorithm (MGA) m-feature selection for dimension reduction by selecting optimal feature subset based on k-fold cross validation. Then, we utilize five different linear and nonlinear classifiers: support vector classifier (SVC), logistic regression classifier (LRC), decision tree classifier (DTC), k-nearest neighbors classifier (KNC), and linear discriminant analysis classifier (LDAC) as candidate classifiers to develop an efficient IDS. Finally, we select the best classifier from the candidates and build an IDS. The experimental results reveal that the proposed MGA-DTC presents a better performance in terms of several metrics based on not only the HCRL-car hacking dataset but also UNSW-NB15, and CIC-IDS2017 datasets.(c) 2022 Elsevier Ltd. All rights reserved

Clear cell variant of syringoma as a rare case

Syringoma is a benign skin tumor derived from eccrine glands characterized by yellowish-pink color and firm papular lesions of the skin especially on the lower eyelid. Typical histopathological features of syringoma are dilated cystic eccrine sweat gland ducts. In this paper, we report a case of clear cell variant syringoma with neck and trunk lesions

New Approach for Risk Estimation Algorithms of BRCA1/2 Negativeness Detection with Modelling Supervised Machine Learning Techniques

BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study. An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years. This dataset consisted of 125 features of 2070 high-risk breast cancer patients. All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm. The performance of the algorithm was identified by studying the machine learning model with the test data. k nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective. The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT. BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented. That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice