ترغب بنشر مسار تعليمي؟ اضغط هنا

A Jumping Mining Attack and Solution

113   0   0.0 ( 0 )
 نشر من قبل Wensheng Gan
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Mining is the important part of the blockchain used the proof of work (PoW) on its consensus, looking for the matching block through testing a number of hash calculations. In order to attract more hash computing power, the miner who finds the proper block can obtain some rewards. Actually, these hash calculations ensure that the data of the blockchain is not easily tampered. Thus, the incentive mechanism for mining affects the security of the blockchain directly. This paper presents an approach to attack against the difficulty adjustment algorithm (abbreviated as DAA) used in blockchain mining, which has a direct impact on miners earnings. In this method, the attack miner jumps between different blockchains to get more benefits than the honest miner who keep mining on only one blockchain. We build a probabilistic model to simulate the time to obtain the next block at different hash computing power called hashrate. Based on this model, we analyze the DAAs of the major cryptocurrencies, including Bitcoin, Bitcoin Cash, Zcash, and Bitcoin Gold. We further verify the effectiveness of this attack called jumping mining through simulation experiments, and also get the characters for the attack in the public block data of Bitcoin Gold. Finally, we give an improved DAA scheme against this attack. Extensive experiments are provided to support the efficiency of our designed scheme.



قيم البحث

اقرأ أيضاً

208 - Shenghui Su , Tao Xie , Shuwang Lu 2014
To examine the integrity and authenticity of an IP address efficiently and economically, this paper proposes a new non-Merkle-Damgard structural (non-MDS) hash function called JUNA that is based on a multivariate permutation problem and an anomalous subset product problem to which no subexponential time solutions are found so far. JUNA includes an initialization algorithm and a compression algorithm, and converts a short message of n bits which is regarded as only one block into a digest of m bits, where 80 <= m <= 232 and 80 <= m <= n <= 4096. The analysis and proof show that the new hash is one-way, weakly collision-free, and strongly collision-free, and its security against existent attacks such as birthday attack and meet-in-the- middle attack is to O(2 ^ m). Moreover, a detailed proof that the new hash function is resistant to the birthday attack is given. Compared with the Chaum-Heijst-Pfitzmann hash based on a discrete logarithm problem, the new hash is lightweight, and thus it opens a door to convenience for utilization of lightweight digital signing schemes.
Miners play a key role in cryptocurrencies such as Bitcoin: they invest substantial computational resources in processing transactions and minting new currency units. It is well known that an attacker controlling more than half of the networks mining power could manipulate the state of the system at will. While the influence of large mining pools appears evenly split, the actual distribution of mining power within these pools and their economic relationships with other actors remain undisclosed. To this end, we conduct the first in-depth analysis of mining reward distribution within three of the four largest Bitcoin mining pools and examine their cross-pool economic relationships. Our results suggest that individual miners are simultaneously operating across all three pools and that in each analyzed pool a small number of actors (<= 20) receives over 50% of all BTC payouts. While the extent of an operators control over the resources of a mining pool remains an open debate, our findings are in line with previous research, pointing out centralization tendencies in large mining pools and cryptocurrencies in general.
Trillions of network packets are sent over the Internet to destinations which do not exist. This darknet traffic captures the activity of botnets and other malicious campaigns aiming to discover and compromise devices around the world. In order to mi ne threat intelligence from this data, one must be able to handle large streams of logs and represent the traffic patterns in a meaningful way. However, by observing how network ports (services) are used, it is possible to capture the intent of each transmission. In this paper, we present DANTE: a framework and algorithm for mining darknet traffic. DANTE learns the meaning of targeted network ports by applying Word2Vec to observed port sequences. Then, when a host sends a new sequence, DANTE represents the transmission as the average embedding of the ports found that sequence. Finally, DANTE uses a novel and incremental time-series cluster tracking algorithm on observed sequences to detect recurring behaviors and new emerging threats. To evaluate the system, we ran DANTE on a full year of darknet traffic (over three Tera-Bytes) collected by the largest telecommunications provider in Europe, Deutsche Telekom and analyzed the results. DANTE discovered 1,177 new emerging threats and was able to track malicious campaigns over time. We also compared DANTE to the current best approach and found DANTE to be more practical and effective at detecting darknet traffic patterns.
Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasio n attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.
We explore a new type of malicious script attacks: the persistent parasite attack. Persistent parasites are stealthy scripts, which persist for a long time in the browsers cache. We show to infect the caches of victims with parasite scripts via TCP i njection. Once the cache is infected, we implement methodologies for propagation of the parasites to other popular domains on the victim client as well as to other caches on the network. We show how to design the parasites so that they stay long time in the victims cache not restricted to the duration of the users visit to the web site. We develop covert channels for communication between the attacker and the parasites, which allows the attacker to control which scripts are executed and when, and to exfiltrate private information to the attacker, such as cookies and passwords. We then demonstrate how to leverage the parasites to perform sophisticated attacks, and evaluate the attacks against a range of applications and security mechanisms on popular browsers. Finally we provide recommendations for countermeasures.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا