An ensemble model to detect packet length covert channels

Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors

Breast cancer detection using machine learning approaches: a comparative study

As the cause of the breast cancer disease has not yet clearly identified and a method to prevent its occurrence has not yet been developed, its early detection has a significant role in enhancing survival rate. In fact, artificial intelligent approaches have been playing an important role to enhance the diagnosis process of breast cancer. This work has selected eight classification models that are mostly used to predict breast cancer to be under investigation. These classifiers include single and ensemble classifiers. A trusted dataset has been enhanced by applying five different feature selection methods to pick up only weighted features and to neglect others. Accordingly, a dataset of only 17 features has been developed. Based on our experimental work, three classifiers, multi-layer perceptron (MLP), support vector machine (SVM) and stack are competing with each other by attaining high classification accuracy compared to others. However, SVM is ranked on the top by obtaining an accuracy of 97.7% with classification errors of 0.029 false negative (FN) and 0.019 false positive (FP). Therefore, it is noteworthy to mention that SVM is the best classifier and it outperforms even the stack classier

A polymorphic advanced encryption standard - A novel approach

To secure various forms of data, a polymorphic variant of the Advanced Encryption Standard (P-AES) has been introduced. In the P-AES, the AES parameters' values will change with every new key. The exact values will be available only to legitimate communicating parties during execution time. To achieve these objectives, the basic AES transformations, SubBytes, ShiftRows, and MixColumns, have been made key-dependent in the proposed P-AES. Hence, with every new key, these transformations will operate differently. The receiver can retrieve the operations' details from the encryption key. Consequently, polymorphism has been achieved and interoperability remains intact. P-AES has been implemented seamlessly using the existing AES modules, and the performance was more or less equal to the AES performance (71 and 70 milliseconds to encrypt 500 bytes using the P-AES and the AES respectively). From a security standpoint, the proposed P-AES fully complies with Kerckhoff's principle. This means the cipher has an open design, and the security provided by the P-AES depends only on the secrecy of the encryption key. The cipher resistance to differential and linear attacks has been proved. Moreover, the resulting proposed cipher can operate in 128 different ways, which will significantly reduce the capabilities of any sophisticated attacker. Furthermore, the proposed P-AES's scores of the key avalanche and the plaintext avalanche were 0.496 and 0.504 respectively. Finally, the Statistical Test Suite (STS) recommended by the NIST has been used to ensure the randomness of the cipher output, and the cipher has passed all the STS tests