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1. Adversarial Machine Learning Attacks in Internet of Things Systems

   https://doi.org/10.1109/AIPR57179.2022.10092216  

   Kone, Rachida; Toutsop, Otily; Thierry, Ketchiozo Wandji; Kornegay, Kevin; Falaye, Joy (October 2022, IEEE)

   Researchers are looking into solutions to support the enormous demand for wireless communication, which has been exponentially increasing along with the growth of technology. The sixth generation (6G) Network emerged as the leading solution for satisfying the requirements placed on the telecommunications system. 6G technology mainly depends on various machine learning and artificial intelligence techniques. The performance of these machine learning algorithms is high. Still, their security has been neglected for some reason, which leaves the door open to various vulnerabilities that attackers can exploit to compromise systems. Therefore, it is essential to evaluate the security of machine learning algorithms to prevent them from being spoofed by malicious hackers. Prior research has shown that the decision tree is one of the most popular algorithms used by 80% of researchers for classification problems. In this work, we collect the dataset from a laboratory testbed of over 100 Internet of things (IoT) devices. The devices include smart cameras, smart light bulbs, Alexa, and others. We evaluate classifiers using the original dataset during the experiment and record a 98% accuracy. We then use the label-flipping attack approach to poison our dataset and record the output. As a result, flipping 10%, 20%, 30%, 40%, and 50% of the poison data generated accuracies of 86%, 74%, 64%, 54%, and 50%, respectively. 

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2. Network Intrusion Detection in Smart Grids for Imbalanced Attack Types Using Machine Learning Models

   https://doi.org/10.1109/ICTC46691.2019.8939744  

   Roy, Dipanjan Das; Shin, Dongwan (October 2019, 2019 International Conference on Information and Communication Technology Convergence (ICTC))

   Smart grid has evolved as the next generation power grid paradigm which enables the transfer of real time information between the utility company and the consumer via smart meter and advanced metering infrastructure (AMI). These information facilitate many services for both, such as automatic meter reading, demand side management, and time-of-use (TOU) pricing. However, there have been growing security and privacy concerns over smart grid systems, which are built with both smart and legacy information and operational technologies. Intrusion detection is a critical security service for smart grid systems, alerting the system operator for the presence of ongoing attacks. Hence, there has been lots of research conducted on intrusion detection in the past, especially anomaly-based intrusion detection. Problems emerge when common approaches of pattern recognition are used for imbalanced data which represent much more data instances belonging to normal behaviors than to attack ones, and these approaches cause low detection rates for minority classes. In this paper, we study various machine learning models to overcome this drawback by using CIC-IDS2018 dataset [1]. 

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3. Droid-NNet: Deep Learning Neural Network for Android Malware Detection

   https://doi.org/10.1109/BigData47090.2019.9006053  

   Masum, Mohammad; Shahriar, Hossain (December 2019, IEEE International Conference on Big Data (Big Data))

   Android, the most dominant Operating System (OS), experiences immense popularity for smart devices for the last few years. Due to its' popularity and open characteristics, Android OS is becoming the tempting target of malicious apps which can cause serious security threat to financial institutions, businesses, and individuals. Traditional anti-malware systems do not suffice to combat newly created sophisticated malware. Hence, there is an increasing need for automatic malware detection solutions to reduce the risks of malicious activities. In recent years, machine learning algorithms have been showing promising results in classifying malware where most of the methods are shallow learners like Logistic Regression (LR). In this paper, we propose a deep learning framework, called Droid-NNet, for malware classification. However, our proposed method Droid-NNet is a deep learner that outperforms existing cutting-edge machine learning methods. We performed all the experiments on two datasets (Malgenome-215 & Drebin-215) of Android apps to evaluate Droid-NNet. The experimental result shows the robustness and effectiveness of Droid-NNet. 

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4. Arcing Fault Detection with Interpretable Learning Model Under the Integration of Renewable Energy

   https://doi.org/10.1109/NAPS46351.2019.8999972  

   Hashmy, Yousaf; Cui, Qiushi; Ma, Zhihao; Weng, Yang (October 2019, 2019 North American Power Symposium (NAPS))

   Under the trend of deeper renewable energy integration, active distribution networks are facing increasing uncertainty and security issues, among which the arcing fault detection (AFD) has baffled researchers for years. Existing machine learning based AFD methods are deficient in feature extraction and model interpretability. To overcome these limitations in learning algorithms, we have designed a way to translate the non-transparent machine learning prediction model into an implementable logic for AFD. Moreover, the AFD logic is tested under different fault scenarios and realistic renewable generation data, with the help of our self-developed AFD software. The performance from various tests shows that the interpretable prediction model has high accuracy, dependability, security and speed under the integration of renewable energy. 

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5. Network Traffic Characteristics of IoT Devices in Smart Homes

   https://doi.org/10.1109/ICCCN52240.2021.9522168  

   Mainuddin, Md; Duan, Zhenhai; Dong, Yingfei (July 2021, 2021 International Conference on Computer Communications and Networks (ICCCN))

   Understanding network traffic characteristics of IoT devices plays a critical role in improving both the performance and security of IoT devices, including IoT device identification, classification, and anomaly detection. Although a number of existing research efforts have developed machine-learning based algorithms to help address the challenges in improving the security of IoT devices, none of them have provided detailed studies on the network traffic characteristics of IoT devices. In this paper we collect and analyze the network traffic generated in a typical smart homes environment consisting of a set of common IoT (and non-IoT) devices. We analyze the network traffic characteristics of IoT devices from three complementary aspects: remote network servers and port numbers that IoT devices connect to, flow-level traffic characteristics such as flow duration, and packet-level traffic characteristics such as packet inter-arrival time. Our study provides critical insights into the operational and behavioral characteristics of IoT devices, which can help develop more effective security and performance algorithms for IoT devices. 

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