0000000001157612
AUTHOR
Timo Hamalainen
On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples
The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In …
A Survey on Technologies Which Make Bitcoin Greener or More Justified
According to recent estimates, one bitcoin transaction consumes as much energy as 1.5 million Visa transactions. Why is bitcoin using so much energy? Most of the energy is used during the bitcoin mining process, which serves at least two significant purposes: a) distributing new cryptocurrency coins to the cryptoeconomy and b) securing the Bitcoin blockchain ledger. In reality, the comparison of bitcoin transactions to Visa transactions is not that simple. The amount of transactions in the Bitcoin network is not directly connected to the amount of bitcoin mining power nor the energy consumption of those mining devices; for example, it is possible to multiply the number of bitcoin transactio…
On Apache Log4j2 Exploitation in Aeronautical, Maritime, and Aerospace Communication
Apache Log4j2 is a prevalent logging library for Java-based applications. In December 2021, several critical and high-impact software vulnerabilities, including CVE-2021-44228, were publicly disclosed, enabling remote code execution (RCE) and denial of service (DoS) attacks. To date, these vulnerabilities are considered critical and the consequences of their disclosure far-reaching. The vulnerabilities potentially affect a wide range of internet of things (IoT) devices, embedded devices, critical infrastructure (CI), and cyber-physical systems (CPSs). In this paper, we study the effects and feasibility of exploiting these vulnerabilities in mission-critical aviation and maritime environment…
Exploring Oscillatory Dysconnectivity Networks in Major Depression During Resting State Using Coupled Tensor Decomposition
Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time frequency connectivity subject are constr…
Cybersecurity Attacks on Software Logic and Error Handling Within ADS-B Implementations: Systematic Testing of Resilience and Countermeasures
Automatic Dependent Surveillance-Broadcast (ADS-B) is a cornerstone of the next-generation digital sky and is now mandated in several countries. However, there have been many reports of serious security vulnerabilities in the ADS-B architecture. In this paper, we demonstrate and evaluate the impact of multiple cyberattacks on ADS-B via remote radio frequency links that affected various network, processing, and display subsystems used within the ADS-B ecosystem. Overall we implemented and tested 12 cyberattacks on ADS-B in a controlled environment, out of which 5 attacks were presented or implemented for the first time. For all these attacks, we developed a unique testbed that consisted of 1…
Inverse Scattering Solutions with Applications to Electromagnetic Signal Processing
When a signal is recorded that has been physically generated by some scattering process (the interaction of electromagnetic, acoustic or elastic waves with inhomogeneous materials, for example), the ‘standard model’ for the signal (i.e. information content convolved with a characteristic Impulse Response Function) is usually based on a single scattering approximation. An additive noise term is introduced into the model to take into account a range of non-deterministic factors including multiple scattering that, along with electronic noise and other background noise sources, is assumed to be relatively weak. Thus, the standard model is based on a ‘weak field condition’ and the inverse scatte…
Cybersecurity Attacks on Software Logic and Error Handling Within AIS Implementations: A Systematic Testing of Resilience
To increase situational awareness of maritime vessels and other entities and to enable their exchange of various information, the International Maritime Organization mandated the use of the Automatic Identification System (AIS) in 2004. The AIS is a self-reporting system that uses the VHF radio link. However, any radio-based self-reporting system is prone to forgery, especially in situations where authentication of the message is not designed into the architecture. As AIS was designed in the 1990s when cyberattacks were in their infancy, it does not implement authentication or encryption; thus, it can be seen as fundamentally vulnerable against modern-day cyberattacks. This paper demonstrat…
GDL90fuzz: Fuzzing - GDL-90 Data Interface Specification Within Aviation Software and Avionics Devices–A Cybersecurity Pentesting Perspective
As the core part of next-generation air transportation systems, the Automatic Dependent Surveillance-Broadcast (ADS-B) is becoming very popular. However, many (if not most) ADS-B devices and implementations support and rely on Garmin’s GDL-90 protocol for data exchange and encapsulation. In this paper, we research GDL-90 protocol fuzzing options and demonstrate practical Denial-of-Service (DoS) attacks on popular Electronic Flight Bag (EFB) software operating on mobile devices. For this purpose, we specifically configured our own avionics pentesting platform. and targeted the popular Garmin’s GDL-90 protocol as the industry-leading devices operate on it. We captured legitimate traffic from …
Communication-Efficient Federated Learning in Channel Constrained Internet of Things
Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As the most widely-used FL framework,Jederated averaging (FedAvg) suffers an expensive communication cost especially when there are large amounts of devices involving the FL process. Moreover, when considering asynchronous FL, the slowest device becomes the bottleneck for the cask effect and determines the overall latency. In this work, we propose a communication-efficient federated learning framework with partial model aggregation (CE-FedPA) algorithm to utilize comp…
Energy Efficient Resource Allocation for Wireless Powered UAV Wireless Communication System with Short Packet
The unmanned aerial vehicle (UAV), which is prominent in its flexibility and low cost, is considered to be fully utilized in the future wireless communication system to provide flexible services and improve connectivities. In this paper, we investigate the resource allocation problem in a wireless powered UAV communication system. In this considered system, The UAV acts as hybrid access point (HAP), which can first perform wireless power transfer in the downlink and charge the Internet of Thing (IoT) user devices (UDs). The UDs can use the harvested energy to deliver the data to the UAV. In the uplink, we explicitly consider short packet communication (SPC) as the transmission feature, whic…
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study
IntroductionPreeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning algorithms demonstrate promising potential, while there is a controversial discussion about whether machine learning methods should be recommended preferably, compared to traditional statistical models.MethodsWe employed both logistic regression and six machine learning methods as binary predictive models for a dataset containing 733 women diagnosed with preeclampsia. Participants were grouped by four different pregnancy outcomes. After the imputation of missing values, statistic…