Abstract:
In recent years, phishing has become one of the biggest security threats on the Internet. To combat phishing, it requires multiple steps and multi-agency participation and thus desperately need uniform data sharing format and unobstructed sharing channels, which unfortunately is just what is lacking currently. This paper proposes a novel phishing data sharing mechanism based on the consortium blockchain. It designs four types of nodes, including reporting node, accounting node, servicing node and supervising node and illustrates the roles of each type. Then it demonstrates the process of reporting, accounting and servicing and designs the process of post-supervision, which ensures the operation of the mechanism stable and fastest; and then discusses its implementation on Hyperledger Fabric. The proposed mechanism includes multi-source reporting, anti-tamper accounting, multi-channel disposal of phishing data and post-supervision. It provides a platform for multi-party participation, transparent and efficient coordination and unified standard and overcomes the current prominent problems of phishing data sharing; and the participants on the consortium blockchain all have a strong desire to combat phishing, which ensures the proposed mechanism is also very practical and highly feasible.Keywords:
Phishing
Data Sharing
In this paper, we study the different channel estimation strategies applied in an AF network, including ordinary channel estimation which estimate the whole source-relay-destination (SRD) channel at the destination, and a new proposed equalized AF (EAF) which make channel estimation separately at relay node and destination node. The EAF carries out the equalization at relay node by the channel estimation, which leads to performance improvement. The analysis of achievable rates is given and the performance benefit of EAF is verified by both numeric and realistic simulation.
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In this paper, we study the different channel estimation strategies applied in an AF network, including ordinary channel estimation which estimate the whole source-relay-destination (SRD) channel at the destination, and a new proposed equalized AF (EAF) which make channel estimation separately at relay node and destination node. The EAF carries out the equalization at relay node by the channel estimation, which leads to performance improvement. The analysis of achievable rates is given and the performance benefit of EAF is verified by both numeric and realistic simulation.
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Phishing attacks are becoming increasingly prevalent: 2016 saw more phishing attacks than any previous year on record according to the Anti-Phishing Working Group. At the same time, the growing level of sophistication of cybercriminals must be understood for the development of effective anti-phishing systems, as phishers have extensive control over the content they serve to their victims. By examining two large, real-world datasets of phishing kits and URLs from 2016 through mid-2017, we paint a clear picture of today's anti-phishing ecosystem while inferring the higher-level motives and thought processes of phishers. We analyze the nature of server-side .htaccess filtering techniques used by phishers to evade detection by the security community. We also propose a new generic classification scheme for phishing URLs which corresponds to modern social engineering techniques and reveals a correlation between URL type and compromised infrastructure use. Our analysis identifies measures that can be taken by the security community to defeat phishers' countermeasures and increase the likelihood of a timely response to phishing. We discover that phishers have a keen awareness of the infrastructure used against them, which illustrates the ever-evolving struggle between cybercriminals and security researchers and motivates future work to positively impact online security.
Phishing
Sophistication
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This study offers a detailed investigation of phishing attacks, a common cybersecurity danger aimed at individuals and organizations globally. It explores the reasons behind phishing attacks, which vary from making money to taking advantage of people's psychology. The research underscores the necessity for thorough detection systems and protective measures in order to effectively combat phishing. A study among knowledgeable individuals in the cybersecurity sector provides information on their awareness and vulnerability to phishing attempts. The results highlight how common phishing attacks are and the significance of grasping various attack vectors. KEYWORDS : Cyber Threat, Cyber Security, Phishing, Social Engineering, Identity Theft.
Phishing
Vulnerability
Identity Theft
Social engineering (security)
Cybercrime
Online identity
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In this paper we investigate the problem of phishing. Phishing is a hybrid problem in that it involves both technical and social issues. In this paper, we also discuss the polymorphic nature of phishing attacks and how this can make defense more difficult. Despite the persistence of the phishing problem, there are ways to combat it and we discuss them in this paper. We highlight countermeasures to tackle phishing and propose suggestions to businesses in order to minimize the loss of revenue and reputation to phishing attacks. The paper discusses technical methods of defending against phishing attacks hardening the infrastructure, and training employees against phishing attacks.
Phishing
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Phishing is an act of creating a website similar to a legitimate website with a motive of stealing user's confidential information. Phishing fraud might be the most popular cybercrime. Phishing is one of the risks that originated a couple of years back but still prevailing. This paper discusses various phishing attacks, some of the latest phishing evasion techniques used by attackers and anti-phishing approaches. This review raises awareness of those phishing strategies and helps the user to practice phishing prevention. Here, a hybrid approach of phishing detection also described having fast response time and high accuracy.
Phishing
Cybercrime
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Aim of this research was to examine familiarity of students in Croatia with threats in form of social engineering and phishing attacks. To obtain our research data, a practical assessment of student's capabilities to identify phishing attacks against them was conducted with the help of a graphical questionnaire that presented real phishing messages that tried to identify what kind of scams or attacks were successful against students and what security topics are needed to identify and protect against phishing attempts. This paper shows the result of our work, some thoughts on phishing research and the identified features that are problematic for students' detection of phishing and social engineering attacks.
Phishing
Social engineering (security)
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Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on white-list, black-list, content-based, URL-based, visual-similarity and machine-learning. In this paper, we discuss various kinds of phishing attacks, attack vectors and detection techniques for detecting the phishing sites. Performance comparison of 18 different models along with nine different sources of datasets are given. Challenges in phishing detection techniques are also given.
Phishing
Similarity (geometry)
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Advancement of technology have both positive and negative impacts. Some of the negative impacts are cyber crimes. Cyber crimes have become more dangerous. Phishing is one of the cyber crime which results in exploitation of data. There are many phishing attacks which are identified every day. There are different techniques in phishing attacks. We have to reduce those attacks by employing suitable anti-phishing techniques. Some of the anti-phishing techniques and algorithms are discussed in this paper. History of phishing and the lifecycle are also discussed in this paper. People should be aware of all such phishing and the anti-phishing techniques. They have to be careful while checking their mails and should not click on any links or downloadable malware files.
Phishing
Cybercrime
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Phishing is the process of illicitly obtaining data through social engineering via electronic communication channels. As reported by the Anti-Phishing Working Group (APWG), Phishing attacks are growing in volume and sophistication. As a result, the need to improve Phishing detection methods increases. We introduce a simple and novel Phishing classification feature that aims toward supplementing existing classifiers by detecting a subset of Phishing attacks.
Phishing
Sophistication
Feature (linguistics)
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