Currently, miners typically join mining pools to solve cryptographic puzzles together, and mining pools are in high competition. This has led to the development of several attack strategies such as block withholding (BWH) and fork after withholding (FAW) attacks that can weaken the health of PoW systems and but maximize mining pools' profits. In this paper, we present strategies called Adaptive Retaliation Strategies (ARS) to mitigate not only BWH attacks but also FAW attacks. In ARS, each pool cooperates with other pools in the normal situation, and adaptively executes either FAW or BWH attacks for the purpose of retaliation only when attacked. In addition, in order for rational pools to adopt ARS, ARS should strike to an adaptive balance between retaliation and selfishness because the pools consider their payoff even when they retaliate. We theoretically and numerically show that ARS would not only lead to the induction of a no-attack state among mining pools, but also achieve the adaptive balance between retaliation and selfishness.

Bitcoin uses the proof-of-work (PoW) mechanism where nodes earn rewards in return for the use of their computing resources. Although this incentive system has attracted many participants, power has, at the same time, been significantly biased towards a few nodes, called mining pools. In addition, poor decentralization appears not only in PoW-based coins but also in coins that adopt proof-of-stake (PoS) and delegated proof-of-stake (DPoS) mechanisms.

Bitcoin has become the most popular cryptocurrency based on a peer-to-peer network. In Aug. 2017, Bitcoin was split into the original Bitcoin (BTC) and Bitcoin Cash (BCH). Since then, miners have had a choice between BTC and BCH mining because they have compatible proof-of-work algorithms. Therefore, they can freely choose which coin to mine for higher profit, where the profitability depends on both the coin price and mining difficulty. Some miners can immediately switch the coin to mine only when mining difficulty changes because the difficulty changes are more predictable than that for the coin price, and we call this behavior fickle mining. In this paper, we study the effects of fickle mining by modeling a game between two coins. To do this, we consider both fickle miners and some factions (e.g., BITMAIN for BCH mining) that stick to mining one coin to maintain that chain. In this model, we show that fickle mining leads to a Nash equilibrium in which only a faction sticking to its coin mining remains as a loyal miner to the less valued coin (e.g., BCH), where loyal miners refer to those who conduct mining even after coin mining difficulty increases. This situation would cause severe centralization, weakening the security of the coin system. To determine which equilibrium the competing coin systems (e.g., BTC vs. BCH) are moving toward, we traced the historical changes of mining power for BTC and BCH and found that BCH often lacked loyal miners until Nov. 13, 2017, when the difficulty adjustment algorithm of BCH mining was changed. However, the change in difficulty adjustment algorithm of BCH mining led to a state close to the stable coexistence of BTC and BCH. We also demonstrate that the lack of BCH loyal miners may still be reached when a fraction of miners automatically and repeatedly switches to the most profitable coin to mine (i.e., automatic mining). According to our analysis, as of Dec. 2018, loyal miners to BCH would leave if more than about 5% of the total mining capacity for BTC and BCH has engaged in the automatic mining. In addition, we analyze the recent “hash war” between Bitcoin ABC and SV, which confirms our theoretical analysis. Finally, we note that our results can be applied to any competing cryptocurrency systems in which the same hardware (e.g., ASICs or GPUs) can be used for mining. Therefore, our study brings new and important angles in competitive coin markets: a coin can intentionally weaken the security and decentralization level of the other rival coin when mining hardware is shared between them, allowing for automatic mining.

The consumer drone market is booming. Consumer drones are predominantly used for aerial photography; however, their use has been expanding because of their autopilot technology. Unfortunately, terrorists have also begun to use consumer drones for kamikaze bombing and reconnaissance. To protect against such threats, several companies have started “anti-drone” services that primarily focus on disrupting or incapacitating drone operations. However, the approaches employed are inadequate, because they make any drone that has intruded stop and remain over the protected area. We specify this issue by introducing the concept of safe-hijacking , which enables a hijacker to expel the intruding drone from the protected area remotely. As a safe-hijacking strategy, we investigated whether consumer drones in the autopilot mode can be hijacked via adaptive GPS spoofing. Specifically, as consumer drones activate GPS fail-safe and change their flight mode whenever a GPS error occurs, we performed black- and white-box analyses of GPS fail-safe flight mode and the following behavior after GPS signal recovery of existing consumer drones. Based on our analyses results, we developed a taxonomy of consumer drones according to these fail-safe mechanisms and designed safe-hijacking strategies for each drone type. Subsequently, we applied these strategies to four popular drones: DJI Phantom 3 Standard, DJI Phantom 4, Parrot Bebop 2, and 3DR Solo. The results of field experiments and software simulations verified the efficacy of our safe-hijacking strategies against these drones and demonstrated that the strategies can force them to move in any direction with high accuracy.

In the Bitcoin system, participants are rewarded for solving cryptographic puzzles. In order to receive more consistent rewards over time, some participants organize mining pools and split the rewards from the pool in proportion to each participant's contribution. However, several attacks threaten the ability to participate in pools. The block withholding (BWH) attack makes the pool reward system unfair by letting malicious participants receive unearned wages while only pretending to contribute work. When two pools launch BWH attacks against each other, they encounter the miner's dilemma: in a Nash equilibrium, the revenue of both pools is diminished. In another attack called selfish mining, an attacker can unfairly earn extra rewards by deliberately generating forks.