TRUST-BASED DEFENSE AGAINST INSIDER PACKET DROP ATTACKS IN WIRELESS SENSOR NETWORKS

In most wireless sensor networks (WSNs), sensor nodes generate data packets and send them to the base station (BS) by multi-hop routing paths because of their limited energy and transmission range. The insider packet drop attacks refer to a set of attacks where compromised nodes intentionally drop packets. It is challenging to accurately detect such attacks because packets may also be dropped due to collision, congestion, or other network problems. Trust mechanism is a promising approach to identify inside packet drop attackers. In such an approach, each node will monitor its neighbor's packet forwarding behavior and use this observation to measure the trustworthiness of its neighbors. Once a neighbor's trust value falls below a threshold, it will be considered as an attacker by the monitoring node and excluded from the routing paths so further damage to the network will not be made. In this dissertation, we analyze the limitation of the state-of-the-art trust mechanisms and propose several enhancement techniques to better defend against insider packet drop attacks in WSNs. First, we observe that inside attackers can easily defeat the current trust mechanisms and even if they are caught, normally a lot of damage has already been made to the network. We believe this is caused by current trust models' inefficiency in distinguishing attacking behaviors and normal network transmission failures. We demonstrate that the phenomenon of consecutive packet drops is one fundamental difference between attackers and good sensor nodes and build a hybrid trust model based on it to improve the detection speed and accuracy of current trust models. Second, trust mechanisms give false alarms when they mis-categorize good nodes as attackers. Aggressive mechanisms like our hybrid approach designed to catch attackers as early as possible normally have high false alarm rate. Removing these nodes from routing paths may significantly reduce the performance of the network. We propose a novel false alarm detection and recovery mechanism that can recover the falsely detected good nodes. Next, we show that more intelligent packet drop attackers can launch advanced attacks without being detected by introducing a selective forwarding-based denial-of-service attack that drops only packets from specific victim nodes. We develop effective detection and prevention methods against such attack. We have implemented all the methods we have proposed and conducted extensive simulations with the OPNET network simulator to validate their effectiveness

A dilution-based defense method against poisoning attacks on deep learning systems

Poisoning attack in deep learning (DL) refers to a type of adversarial attack that injects maliciously manipulated data samples into a training dataset for the purpose of forcing a DL model trained based on the poisoned training dataset to misclassify inputs and thus significantly degrading its performance and reliability. Meanwhile, a traditional defense approach against poisoning attacks tries to detect poisoned data samples from the training dataset and then remove them. However, since new sophisticated attacks avoiding existing detection methods continue to emerge, a detection method alone cannot effectively counter poisoning attacks. For this reason, in this paper, we propose a novel dilution-based defense method that mitigates the effect of poisoned data by adding clean data to the training dataset. According to our experiments, our dilution-based defense technique can significantly decrease the success rate of poisoning attacks and improve classification accuracy by effectively reducing the contamination ratio of the manipulated data. Especially, our proposed method outperformed an existing defense method (Cutmix data augmentation) by 20.9%p at most in terms of classification accuracy

Cultural Foundations of Contentious Democracy in South Korea

This study takes a cultural approach to examine the unstable and contentious nature of Korean democracy. Analyzing an original nationwide survey conducted in 2015, we find that the democratic and participatory culture of the Korean people underlies Korean democracy. This finding suggests substantial tension between the participatory orientation of the public and Korean representative democracy

Development and characterization of nine polymorphic microsatellite markers in the seven-spotted lady beetle, Coccinella septempunctata (Coleoptera: Coccinellidae)

In this study, nine microsatellite loci were isolated and characterized from the seven-spotted lady beetle, Coccinella septempunctata (Coleoptera: Coccinellidae). The loci were validated and characterized using 20 samples collected from five Korean localities. These results indicate that some loci were highly variable in terms of number of alleles (2 to 13), heterozygosity (0.10 to 0.40), and polymorphic information content (0.31 to 0.85). These microsatellite markers will be very valuable for population genetic studies of C. septempunctata.Key words: Seven-spotted lady beetle, Coccinella septempunctata, microsatellite Deoxyribonucleic acid (DNA)

Towards an Inclusive and Accessible Metaverse

The push towards a Metaverse is growing, with companies such as Meta developing their own interpretation of what it should look like. The Metaverse at its conceptual core promises to remove boundaries and borders, becoming a decentralised entity for everyone to use - forming a digital virtual layer over our own "real"world. However, creation of a Metaverse or "new world"presents the opportunity to create one which is inclusive and accessible to all. This challenge is explored and discussed in this workshop, with an aim of understanding how to create a Metaverse which is open and inclusive to people with physical and intellectual disabilities, and how interactions can be designed in a way to minimise disadvantage. The key outcomes of this workshop outline new opportunities for improving accessibility in the Metaverse, methodologies for designing and evaluating accessibility, and key considerations for designing accessible Metaverse environments and interactions

GW Calculations on post-transition-metal oxides

In order to establish the reliable GW scheme that can be consistently applied to post-transition-metal oxides (post-TMOs), we carry out comprehensive GW calculations on electronic structures of ZnO, Ga2O3, In2O3, and SnO2, the four representative post-TMOs. Various levels of self-consistency (G0W0, GW0, and QPGW0) and different starting functionals (GGA, GGA + U, and hybrid functional) are tested and their influence on the resulting electronic structure is closely analyzed. It is found that the GW0 scheme with GGA + U as the initial functional turns out to give the best agreement with experiment, implying that describing the position of metal-d level precisely in the ground state plays a critical role for the accurate dielectric property and quasiparticle band gap. Nevertheless, the computation on ZnO still suffers from the shallow Zn-d level and we propose a modified approach (GW0+Ud) that additionally considers an effective Hubbard U term during GW0 iterations and thereby significantly improves the band gap. It is also shown that a GGA + U-based GW0(+Ud) scheme produces an accurate energy gap of crystalline InGaZnO4, implying that this can serve as a standard scheme that can be applied to general structures of post-TMOs. © 2014 American Physical Society.1991sciescopu

The CUAVA-1 CubeSat—A Pathfinder Satellite for Remote Sensing and Earth Observation

In this paper we report a 3U CubeSat named CUAVA-1 designed by the ARC Training Centre for CubeSats, UAVs, and Their Applications (CUAVA). CUAVA, funded by the Australian Research Council, aims to train students, develop new instruments and technology to solve crucial problems, and help develop a world-class Australian industry in CubeSats, UAVs, and related products. The CUAVA-1 project is the Centre’s first CubeSat mission, following on from the 2 Australian satellites INSPIRE-2 and UNSW-EC0 CubeSats that launched in 2017. The mission is designed to serve as a precursor for a series of Earth observations missions and to demonstrate new technologies developed by our partners. We also intend to use the satellite to provide students hands-on experiences and to gain experience for our engineering, science and industry teams for future, more complex, missions

Intelligent On-Off Web Defacement Attacks and Random Monitoring-Based Detection Algorithms

Recent cyberattacks armed with various ICT (information and communication technology) techniques are becoming advanced, sophisticated and intelligent. In security research field and practice, it is a common and reasonable assumption that attackers are intelligent enough to discover security vulnerabilities of security defense mechanisms and thus avoid the defense systems’ detection and prevention activities. Web defacement attacks refer to a series of attacks that illegally modify web pages for malicious purposes, and are one of the serious ongoing cyber threats that occur globally. Detection methods against such attacks can be classified into either server-based approaches or client-based approaches, and there are pros and cons for each approach. From our extensive survey on existing client-based defense methods, we found a critical security vulnerability which can be exploited by intelligent attackers. In this paper, we report the security vulnerability in existing client-based detection methods with a fixed monitoring cycle and present novel intelligent on-off web defacement attacks exploiting such vulnerability. Next, we propose to use a random monitoring strategy as a promising countermeasure against such attacks, and design two random monitoring defense algorithms: (1) Uniform Random Monitoring Algorithm (URMA), and (2) Attack Damage-Based Random Monitoring Algorithm (ADRMA). In addition, we present extensive experiment results to validate our idea and show the detection performance of our random monitoring algorithms. According to our experiment results, our random monitoring detection algorithms can quickly detect various intelligent web defacement on-off attacks (AM1, AM2, and AM3), and thus do not allow huge attack damage in terms of the number of defaced slots when compared with an existing fixed periodic monitoring algorithm (FPMA)

HyperLedger Fabric-Based Proactive Defense against Inside Attackers in the WSN With Trust Mechanism

In Wireless Sensor Networks (WSNs), the Trust Mechanism (TM) is used to defend against insider attacks by measuring the trustworthiness of all inside sensor nodes in the network. Thus, each sensor node with TM observes its neighbor nodes’ behaviors, evaluates their trustworthiness as numeric trust values, and captures untrustworthy nodes as inside attackers. Although the defense performance of trust mechanisms can be further improved by sharing the information about inside attackers detected by TM with all sensor nodes, the detected inside attacker list must be securely shared with and stored in all sensor nodes in the WSN. However, according to our survey, we observed that most existing studies simply assume that the communication channel for sharing the attacker detection list is reliable and trusted even in the presence of inside attackers in the WSN. In this paper, we propose and implement a proactive defense model that integrates the HyperLedger Fabric and trust mechanism to defend against inside attackers by securely sharing the detected inside attacker list with all sensor nodes in the WSN. In addition, we conduct comparative experiments to show that our proposed model can better defend against inside attackers than an existing trust mechanism. According to our experimental results, our proposed model could lower the attack damage (the number of packet drops) caused by an inside packet drop attacker by 59 to 67% compared to an existing trust mechanism