Biometric verification is essential for secure identity verification and authentication during banking transactions using fingerprints, facial features, irises, and voices. Among these methods, voice biometrics is a promising alternative owing to its potential for robust and convenient user authentication. However, their effectiveness is significantly challenged by variations in the voice caused by different device configurations and environmental conditions. These variations can reduce the effectiveness of speaker identification and undermine the reliability of voice-based systems for securing online transactions. For an effective comparative solution, this study addresses these challenges by focusing on the difficulties posed by voice variations due to differences in device hardware, microphone quality, and environmental noise. Our approach employs machine-learning techniques using advanced speech enhancement methods to improve the consistency and accuracy of voice biometric verification across diverse devices. Specifically, we employ an adaptive filter model that enhances signal extraction, noise suppression, and predictive precision. Furthermore, our empirical demonstration showed that the adaptive filter significantly improved the accuracy of voice biometric systems by mitigating the impact of device-induced voice variations. In addition, we evaluate the performance of this model using a range of metrics.

Mobile phones were originally designed for commu-nication, wherein they have evolved into multifunctional devices used for financial transactions, social media, and more, making them an integral mode of communication for the dweller's world. However, the mostly used OS in mobile devices, Android consist of various vulnerabilities and lack sufficient security measures, leaving them susceptible to malware injection. Moreover, the attackers develop sophisticated malware, which is challenging to detect by traditional detection approaches. Henceforth, an effective malware detection method is imperative to ensure the safety and security of Android systems. In this study, we address the dynamic analysis of Android malware using supervised and semi-supervised deep neural network techniques to tackle existing challenges. Our investigation is conducted on the CCCS-CIC-AndMal-2020 dataset and the results reveal that our proposed supervised models (1D CNN, MLP, RNN and LSTM) outperform state-of-the-art supervised models significantly with an accuracy of 99.76%. Additionally, we explore semi-supervised approach using limited label data, where our label spreading approach showcases a highly effective detection accuracy of 97.26%, approaching that of a fully supervised approach.

Electricity thefts are conventionally manually detected by inspections, accusations, and the failure of meters. However, the recent evolution of machine learning may allow the automatic detection of electricity theft only from the patterns of meter readings. Electric consumption heavily relies on many factors, e.g., the lifestyle of the day and the weather, and thus the accuracy of detection is questioned. We propose an electricity theft detection framework for smart homes with knowledge-based synthetic attack data. This allows training of the attack classifier only from the legitimate power consumption data, i.e, without attack actions and associated labels. We identified five attack patterns as the knowledge which consisted of smart attacks and legacy attacks. We have conducted comprehensive evaluations with nine machine learning models using the Almanac of Minutely Power dataset version 2 (AMPds2) dataset fine-grained time-series data of a smart home. We found that Gradient Boosting-based algorithms achieved the best, and Random Forest performed alternatively with almost 100% accuracy for detecting and classifying legacy attacks. Some smart attacks were not detected, but those algorithms achieved good performance in detection and classification.

Cyber-attacks on industrial control systems (ICSs) may cause huge damage to our society and our lives. RS-485 is a backbone network for many ICSs deployed worldwide as a standard. Attack detection in the RS-485 network has been studied in the past. However, the operator still needs to identify and eliminate the attacker in the network after detected, which may require a huge downtime of the system. We propose an attacker localization framework for RS-485 networks. This framework uses (1) a current transformer for monitoring the analog signals of the communication line and (2) machine learning for detecting and localizing the attacker. We have carried out a performance evaluation on a 200-meter scale testbed and found that regression-based localization model performed the best with an averaging aggregator. It could estimate the location of the attacker with about 100% accuracy if we could obtain 6 or 10 attacker points in the training dataset. It could also estimate the location with 93%-96% accuracy with only 4 attacker training points, which would be still practically useful for finding the attacker in RS-485 network.

Remotely connected devices have been adopted in several industrial control systems (ICS) recently due to the advancement in the Industrial Internet of Things (IIoT). This led to new security vulnerabilities because of the expansion of the attack surface. Moreover, cybersecurity incidents in critical infrastructures are increasing. In the ICS, RS-485 cables are widely used in its network for serial communication between each component. However, almost 30 years ago, most of the industrial network protocols implemented over RS-485 such as Modbus were designed without security features. Therefore, anomaly detection is required in industrial control networks to secure communication in the systems. The goal of this paper is to study unsupervised anomaly detection in RS-485 traffic using autoencoders. Five threat scenarios in the physical layer of the industrial control network are proposed. The novelty of our method is that RS-485 traffic is collected indirectly by an analog-to-digital converter. In the experiments, multilayer perceptron (MLP), 1D convolutional, Long Short-Term Memory (LSTM) autoencoders are trained to detect anomalies. The results show that three autoencoders effectively detect anomalous traffic with F1-scores of 0.963, 0.949, and 0.928 respectively. Due to the indirect traffic collection, our method can be practically applied in the industrial control network.

The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%.

The modern car is increasingly connected. That connection is magnified by the presence of a large number of electronic control units (ECUs). The communication between the ECUs of a modern car is assured by the Controller Area Network (CAN) bus system. Despite its importance, the CAN bus system is bereft of security mechanisms making it vulnerable to numerous security attacks. When an attacker succeeds in compromising the ECUs, they can take control and stop the engine, disable the brakes, turn the lights on/off, etc. An intrusion detection system (IDS) can be deployed as an appropriate security measure to detect the malicious network traffic in the CAN bus system. In this paper, we propose a Convolutional Neural Network (CNN)-based network attacks IDS for protecting the CAN bus system. For efficiency reasons, we generated our own datasets from three car models. Our experiment results demonstrate that our classifier is efficient for detecting the CAN bus system attacks, and it performs with a high accuracy of 99.99% and a detection rate of 0.99.