Abstract. In this paper, we apply the ideas from [2] to investigate the effect of some semantic based guidance to the crossover operator of GP. We conduct a series of experiments on a family of real-valued symbolic regression problems, examining four different semantic aware crossover operators. One operator considers the semantics of the exchanged subtrees, while the other compares the semantics of the child trees to their parents. Two control operators are adopted which reverse the logic of the semantic equivalence test. The results show that on the family of test problems examined, the (approximate) semantic aware crossover operators can provide performance advantages over the standard subtree crossover adopted in Genetic Programming.

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The digital revolution has substantially changed our lives in which Internet-of-Things (IoT) plays a prominent role. The rapid development of IoT to most corners of life, however, leads to various emerging cybersecurity threats. Therefore, detecting and preventing potential attacks in IoT networks have recently attracted paramount interest from both academia and industry. Among various attack detection approaches, machine learning-based methods, especially deep learning, have demonstrated great potential thanks to their early detecting capability. However, these machine learning techniques only work well when a huge volume of data from IoT devices with label information can be collected. Nevertheless, the labeling process is usually time consuming and expensive, thus, it may not be able to adapt with quick evolving IoT attacks in reality. In this paper, we propose a novel deep transfer learning (DTL) method that allows to learn from data collected from multiple IoT devices in which not all of them are labeled. Specifically, we develop a DTL model based on two AutoEncoders (AEs). The first AE (AE 1) is trained on the source datasets (source domains) in the supervised mode using the label information and the second AE (AE 2) is trained on the target datasets (target domains) in an unsupervised manner without label information. The transfer learning process attempts to force the latent representation (the bottleneck layer) of AE 2 similarly to the latent representation of AE 1. After that, the latent representation of AE 2 is used to detect attacks in the incoming samples in the target domain. We carry out intensive experiments on nine recent IoT datasets to evaluate the performance of the proposed model. The experimental results demonstrate that the proposed DTL model significantly improves the accuracy in detecting IoT attacks compared to the baseline deep learning technique and two recent DTL approaches. INDEX TERMS Deep transfer learning, IoT, cyberattack detection, AutoEncoder. I. INTRODUCTION T HE Internet-of-Things (IoT) refers to connected devices, sensors, an actuators used in vehicles, electronic appliances, buildings, and structures. As the sensors, data storage, and the Internet become cheaper, faster, and more integrated together, IoT devices will find more and more applications [1] (e.g., in smart buildings, smart city, intelligent transportation systems, and healthcare). The rapid development of IoT to most corners of life, however, leads to various emerging cybersecurity threats. This is because IoT devices are often limited in computing capability and energy, making them particularly vulnerable to adversaries. IoT devices are more exposed to and unfortunately more difficult to be protected from cyber attacks than computers [2], [3]. Consequently, detecting attacks to protect IoT devices from malicious behaviors is critical to broadening the applications of IoT [4]-[7].

A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

Building classification models from clinical data using machine learning methods often relies on labeling of patient examples by human experts. Standard machine learning framework assumes the labels are assigned by a homogeneous process. However, in reality the labels may come from multiple experts and it may be difficult to obtain a set of class labels everybody agrees on; it is not uncommon that different experts have different subjective opinions on how a specific patient example should be classified. In this work we propose and study a new multi-expert learning framework that assumes the class labels are provided by multiple experts and that these experts may differ in their class label assessments. The framework explicitly models different sources of disagreements and lets us naturally combine labels from different human experts to obtain: (1) a consensus classification model representing the model the group of experts converge to, as well as, and (2) individual expert models. We test the proposed framework by building a model for the problem of detection of the Heparin Induced Thrombocytopenia (HIT) where examples are labeled by three experts. We show that our framework is superior to multiple baselines (including standard machine learning framework in which expert differences are ignored) and that our framework leads to both improved consensus and individual expert models.