Biblio
Today's phishing websites are constantly evolving to deceive users and evade the detection. In this paper, we perform a measurement study on squatting phishing domains where the websites impersonate trusted entities not only at the page content level but also at the web domain level. To search for squatting phishing pages, we scanned five types of squatting domains over 224 million DNS records and identified 657K domains that are likely impersonating 702 popular brands. Then we build a novel machine learning classifier to detect phishing pages from both the web and mobile pages under the squatting domains. A key novelty is that our classifier is built on a careful measurement of evasive behaviors of phishing pages in practice. We introduce new features from visual analysis and optical character recognition (OCR) to overcome the heavy content obfuscation from attackers. In total, we discovered and verified 1,175 squatting phishing pages. We show that these phishing pages are used for various targeted scams, and are highly effective to evade detection. More than 90% of them successfully evaded popular blacklists for at least a month.
Phishing is a technique aimed to imitate an official websites of any company such as banks, institutes, etc. The purpose of phishing is to theft private and sensitive credentials of users such as password, username or PIN. Phishing detection is a technique to deal with this kind of malicious activity. In this paper we propose a method able to discriminate between web pages aimed to perform phishing attacks and legitimate ones. We exploit state of the art machine learning algorithms in order to build models using indicators that are able to detect phishing activities.
In last decades, the web and online services have revolutionized the modern world. However, by increasing our dependence on online services, as a result, online security threats are also increasing rapidly. One of the most common online security threats is a so-called Phishing attack, the purpose of which is to mimic a legitimate website such as online banking, e-commerce or social networking website in order to obtain sensitive data such as user-names, passwords, financial and health-related information from potential victims. The problem of detecting phishing websites has been addressed many times using various methodologies from conventional classifiers to more complex hybrid methods. Recent advancements in deep learning approaches suggested that the classification of phishing websites using deep learning neural networks should outperform the traditional machine learning algorithms. However, the results of utilizing deep neural networks heavily depend on the setting of different learning parameters. In this paper, we propose a swarm intelligence based approach to parameter setting of deep learning neural network. By applying the proposed approach to the classification of phishing websites, we were able to improve their detection when compared to existing algorithms.
Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.
Phishing websites remain a persistent security threat. Thus far, machine learning approaches appear to have the best potential as defenses. But, there are two main concerns with existing machine learning approaches for phishing detection. The first is the large number of training features used and the lack of validating arguments for these feature choices. The second concern is the type of datasets used in the literature that are inadvertently biased with respect to the features based on the website URL or content. To address these concerns, we put forward the intuition that the domain name of phishing websites is the tell-tale sign of phishing and holds the key to successful phishing detection. Accordingly, we design features that model the relationships, visual as well as statistical, of the domain name to the key elements of a phishing website, which are used to snare the end-users. The main value of our feature design is that, to bypass detection, an attacker will find it very difficult to tamper with the visual content of the phishing website without arousing the suspicion of the end user. Our feature set ensures that there is minimal or no bias with respect to a dataset. Our learning model trains with only seven features and achieves a true positive rate of 98% and a classification accuracy of 97%, on sample dataset. Compared to the state-of-the-art work, our per data instance classification is 4 times faster for legitimate websites and 10 times faster for phishing websites. Importantly, we demonstrate the shortcomings of using features based on URLs as they are likely to be biased towards specific datasets. We show the robustness of our learning algorithm by testing on unknown live phishing URLs and achieve a high detection accuracy of \$99.7%\$.
In this paper, we analyze the evolution of Certificate Transparency (CT) over time and explore the implications of exposing certificate DNS names from the perspective of security and privacy. We find that certificates in CT logs have seen exponential growth. Website support for CT has also constantly increased, with now 33% of established connections supporting CT. With the increasing deployment of CT, there are also concerns of information leakage due to all certificates being visible in CT logs. To understand this threat, we introduce a CT honeypot and show that data from CT logs is being used to identify targets for scanning campaigns only minutes after certificate issuance. We present and evaluate a methodology to learn and validate new subdomains from the vast number of domains extracted from CT logged certificates.
We are exploring new ways to analyze phishing attacks. To do this, we investigate the change in the dynamics of the power of phishing attacks. We also analyze the effectiveness of detection of phishing attacks. We are considering the possibility of using new tools for analyzing phishing attacks. As such tools, the methods of chaos theory and the ideology of wavelet coherence are used. The use of such analysis tools makes it possible to investigate the peculiarities of the phishing attacks occurrence, as well as methods for their identification effectiveness. This allows you to expand the scope of the analysis of phishing attacks. For analysis, we use real data about phishing attacks.
Phishing is a form of cybercrime where an attacker imitates a real person / institution by promoting them as an official person or entity through e-mail or other communication mediums. In this type of cyber attack, the attacker sends malicious links or attachments through phishing e-mails that can perform various functions, including capturing the login credentials or account information of the victim. These e-mails harm victims because of money loss and identity theft. In this study, a software called "Anti Phishing Simulator'' was developed, giving information about the detection problem of phishing and how to detect phishing emails. With this software, phishing and spam mails are detected by examining mail contents. Classification of spam words added to the database by Bayesian algorithm is provided.
Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.
Phishing is a security attack to acquire personal information like passwords, credit card details or other account details of a user by means of websites or emails. Phishing websites look similar to the legitimate ones which make it difficult for a layman to differentiate between them. As per the reports of Anti Phishing Working Group (APWG) published in December 2018, phishing against banking services and payment processor was high. Almost all the phishy URLs use HTTPS and use redirects to avoid getting detected. This paper presents a focused literature survey of methods available to detect phishing websites. A comparative study of the in-use anti-phishing tools was accomplished and their limitations were acknowledged. We analyzed the URL-based features used in the past to improve their definitions as per the current scenario which is our major contribution. Also, a step wise procedure of designing an anti-phishing model is discussed to construct an efficient framework which adds to our contribution. Observations made out of this study are stated along with recommendations on existing systems.
The world is witnessing the emerging role of Internet of Things (IoT) as a technology that is transforming different industries, global community and its economy. Currently a plethora of interconnected smart devices have been deployed for diverse pervasive applications and services, and billions more are expected to be connected to the Internet in the near future. The potential benefits of IoT include improved quality of life, convenience, enhanced energy efficiency, and more productivity. Alongside these potential benefits, however, come increased security risks and potential for abuse. Arguably, this is partly because many IoT start-ups and electronics hobbyists lack security expertise, and some established companies do not make security a priority in their designs, and hence they produce IoT devices that are often ill-equipped in terms of security. In this paper, we discuss different IoT application areas, and identify security threats in IoT architecture. We consider security requirements and present typical security threats for each of the application domains. Finally, we present several possible security countermeasures, and introduce the IoT Hardware Platform Security Advisor (IoT-HarPSecA) framework, which is still under development. IoT-HarPSecA is aimed at facilitating the design and prototyping of secure IoT devices.
Nowadays, the proliferation of smart, communication-enable devices is opening up many new opportunities of pervasive applications. A major requirement of pervasive applications is to be secured. The complexity to secure pervasive systems is to address a end-to-end security level: from the device to the services according to the entire life cycle of devices, applications and platform. In this article, we propose a solution combining both hardware and software elements to secure communications between devices and pervasive platform based on certificates issued from a Public Key Infrastructure. Our solution is implemented and validated with a real device extended by a secure element and our own Public Key Infrastructure.
Peer to Peer (P2P) is a dynamic and self-organized technology, popularly used in File sharing applications to achieve better performance and avoids single point of failure. The popularity of this network has attracted many attackers framing different attacks including Sybil attack, Routing Table Insertion attack (RTI) and Free Riding. Many mitigation methods are also proposed to defend or reduce the impact of such attacks. However, most of those approaches are protocol specific. In this work, we propose a Blockchain based security framework for P2P network to address such security issues. which can be tailored to any P2P file-sharing system.
Underpinning the operation of Bitcoin is a peer-to-peer (P2P) network [1] that facilitates the execution of transactions by end users, as well as the transaction confirmation process known as bitcoin mining. The security of this P2P network is vital for the currency to function and subversion of the underlying network can lead to attacks on bitcoin users including theft of bitcoins, manipulation of the mining process and denial of service (DoS). As part of this paper the network protocol and bitcoin core software are analysed, with three bitcoin message exchanges (the connection handshake, GETHEADERS/HEADERS and MEMPOOL/INV) found to be potentially vulnerable to spoofing and use in distributed denial of service (DDoS) attacks. Possible solutions to the identified weaknesses and vulnerabilities are evaluated, such as the introduction of random nonces into network messages exchanges.
Peer-to-Peer botnets have become one of the significant threat against network security due to their distributed properties. The decentralized nature makes their detection challenging. It is important to take measures to detect bots as soon as possible to minimize their harm. In this paper, we propose PeerGrep, a novel system capable of identifying P2P bots. PeerGrep starts from identifying hosts that are likely engaged in P2P communications, and then distinguishes P2P bots from P2P hosts by analyzing their active ratio, packet size and the periodicity of connection to destination IP addresses. The evaluation shows that PeerGrep can identify all P2P bots with quite low FPR even if the malicious P2P application and benign P2P application coexist within the same host or there is only one bot in the monitored network.
The underlying or core technology of Bitcoin cryptocurrency has become a blessing for human being in this era. Everything is gradually changing to digitization in this today's epoch. Bitcoin creates virtual money using Blockchain that's become popular over the world. Blockchain is a shared public ledger, and it includes all transactions which are confirmed. It is almost impossible to crack the hidden information in the blocks of the Blockchain. However, there are certain security and technical challenges like scalability, privacy leakage, selfish mining, etc. which hampers the wide application of Blockchain. In this paper, we briefly discuss this emerging technology namely Blockchain. In addition, we extrapolate in-depth insight on Blockchain technology.
Peer-to-peer computing (P2P) refers to the famous technology that provides peers an equal spontaneous collaboration in the network by using appropriate information and communication systems without the need for a central server coordination. Today, the interconnection of several P2P networks has become a genuine solution for increasing system reliability, fault tolerance and resource availability. However, the existence of security threats in such networks, allows us to investigate the safety of users from P2P threats by studying the effects of competition between these interconnected networks. In this paper, we present an e-epidemic model to characterize the worm propagation in an interconnected peer-to-peer network. Here, we address this issue by introducing a model of network competition where an unprotected network is willing to partially weaken its own safety in order to more severely damage a more protected network. The unprotected network can infect all peers in the competitive networks after their non react against the passive worm propagation. Our model also evaluated the effect of an immunization strategies adopted by the protected network to resist against attacking networks. The launch time of immunization strategies in the protected network, the number of peers synapse connected to the both networks, and other effective parameters have also been investigated in this paper.
In recent years, the increasing concerns around the centralized cloud web services (e.g. privacy, governance, surveillance, security) have triggered the emergence of new distributed technologies, such as IPFS or the Blockchain. These innovations have tackled technical challenges that were unresolved until their appearance. Existing models of peer-to-peer systems need a revision to cover the spectrum of potential systems that can be now implemented as peer-to-peer systems. This work presents a framework to build these systems. It uses an agent-oriented approach in an open environment where agents have only partial information of the system data. The proposal covers data access, data discovery and data trust in peer-to-peer systems where different actors may interact. Moreover, the framework proposes a distributed architecture for these open systems, and provides guidelines to decide in which cases Blockchain technology may be required, or when other technologies may be sufficient.
Ethereum, the second-largest cryptocurrency valued at a peak of \$138 billion in 2018, is a decentralized, Turing-complete computing platform. Although the stability and security of Ethereum—and blockchain systems in general—have been widely-studied, most analysis has focused on application level features of these systems such as cryptographic mining challenges, smart contract semantics, or block mining operators. Little attention has been paid to the underlying peer-to-peer (P2P) networks that are responsible for information propagation and that enable blockchain consensus. In this work, we develop NodeFinder to measure this previously opaque network at scale and illuminate the properties of its nodes. We analyze the Ethereum network from two vantage points: a three-month long view of nodes on the P2P network, and a single day snapshot of the Ethereum Mainnet peers. We uncover a noisy DEVp2p ecosystem in which fewer than half of all nodes contribute to the Ethereum Mainnet. Through a comparison with other previously studied P2P networks including BitTorrent, Gnutella, and Bitcoin, we find that Ethereum differs in both network size and geographical distribution.
Fourier domain mode locked (FDML) lasers, in which the sweep period of the swept bandpass filter is synchronized with the roundtrip time of the optical field, are broadband and rapidly tunable fiber ring laser systems, which offer rich dynamics. A detailed understanding is important from a fundamental point of view, and also required in order to improve current FDML lasers which have not reached their coherence limit yet. Here, we study the formation of localized patterns in the intensity trace of FDML laser systems based on a master equation approach [1] derived from the nonlinear Schrödinger equation for polarization maintaining setups, which shows excellent agreement with experimental data. A variety of localized patterns and chaotic or bistable operation modes were previously discovered in [2–4] by investigating primarily quasi-static regimes within a narrow sweep bandwidth where a delay differential equation model was used. In particular, the formation of so-called holes which are characterized by a dip in the intensity trace and a rapid phase jump are described. Such holes have tentatively been associated with Nozaki-Bekki holes which are solutions to the complex Ginzburg-Landau equation. In Fig. 1 (b) to (d) small sections of a numerical solution of our master equation are presented for a partially dispersion compensated polarization maintaining FDML laser setup. Within our approach, we are able to study the full sweep dynamics over a broad sweep range of more than 100 nm. This allows us to identify different co-existing intensity patterns within a single sweep. In general, high frequency distortions in the intensity trace of FDML lasers [5] are mainly caused by synchronization mismatches caused by the fiber dispersion or a detuning of the roundtrip time of the optical field to the sweep period of the swept bandpass filter. This timing errors lead to rich and complex dynamics over many roundtrips and are a major source of noise, greatly affecting imaging and sensing applications. For example, the imaging quality in optical coherence tomography where FDML lasers are superior sources is significantly reduced [5].
This paper proposes the implementation of progressive authentication service in smart android mobile phone. In this digital era, massive amount of work can be done in the digital form using the smart devices like smart phone , laptop, Tablets, etc. The number of smartphone users approx. reach to 299.24 million, as per the recent survey report [1] in 2019 this count will reach 2.7 billion and after 3 years, this count will increase up to 442.5 million. This article includes a cluster based progressive smart lock with a dependent combination that is short and more secure in nature. Android provides smart lock facilities with the combination of 9 dot, 6dot, 5dot, 4dot and 1-9 number. By using this mobile phone user will be able to generate pattern lock or number password for authentication. This is a single authentication system, this research paper includes a more secured multiple cluster based pattern match system.