Tutorial and Critical Analysis of Phishing Websites Methods

The Internet has become an essential component of our everyday social and financial activities. Internet is not important for individual users only but also for organizations, because organizations that offer online trading can achieve a competitive edge by serving worldwide clients. Internet facilitates reaching customers all over the globe without any market place restrictions and with effective use of e-commerce. As a result, the number of customers who rely on the Internet to perform procurements is increasing dramatically. Hundreds of millions of dollars are transferred through the Internet every day. This amount of money was tempting the fraudsters to carry out their fraudulent operations. Hence, Internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customers’ confidence in e-commerce and online banking. Therefore, suitability of the Internet for commercial transactions becomes doubtful. Phishing is considered a form of web threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain user’s confidential credentials such as usernames, passwords and social security numbers. In this article, the phishing phenomena will be discussed in detail. In addition, we present a survey of the state of the art research on such attack. Moreover, we aim to recognize the up-to-date developments in phishing and its precautionary measures and provide a comprehensive study and evaluation of these researches to realize the gap that is still predominating in this area. This research will mostly focus on the web based phishing detection methods rather than email based detection methods

Email fraud classifier using machine learning

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Jordi José Bazán[en] Email is one of the most common methods of communication nowadays. Programs known as malware detection are essential to assist and protect users from the agents that are usually responsible for cyberattacks. This paper focuses on using machine learning algorithms to detect any possible email attacks by analyzing datasets of whitelists and blacklists. This document also includes other methods that try to solve this problem

User habitation in keystroke dynamics based authentication

Most computer systems use usernames and passwords for authentication and access control. For long, password security has been framed as a tradeoff between user experience and password security. Trading off one for the other appears to be an inevitable dilemma for single password based security applications. As a new biometric for authenticating access, keystroke dynamics offers great promises in hardening the password mechanism. Our research first investigate the keystroke dynamics based password security by conducting an incremental study on user\u27s habituation process for keystroke dynamics analysis using two distinct types of passwords. The study shows that (1) long and complex passwords are more efficient to be employed in keystroke dynamics systems; and (2) there is a habituation and acclimation process before the user obtains a stable keystroke pattern and the system collects enough training data. Then, based on our findings, we propose a two passwords mechanism that attempts to strike the right balance over user experience and password security by adopting a conventional easy-to-memorize password followed by a long-and-complex phrase for keystroke dynamics verification. Analysis and experimental studies successfully demonstrate the effectiveness of our proposed approach

Local and Global Trust Based on the Concept of Promises

We use the notion of a promise to define local trust between agents possessing autonomous decision-making. An agent is trustworthy if it is expected that it will keep a promise. This definition satisfies most commonplace meanings of trust. Reputation is then an estimation of this expectation value that is passed on from agent to agent. Our definition distinguishes types of trust, for different behaviours, and decouples the concept of agent reliability from the behaviour on which the judgement is based. We show, however, that trust is fundamentally heuristic, as it provides insufficient information for agents to make a rational judgement. A global trustworthiness, or community trust can be defined by a proportional, self-consistent voting process, as a weighted eigenvector-centrality function of the promise theoretical graph

An Emergent Architecture for Scaling Decentralized Communication Systems (DCS)

With recent technological advancements now accelerating the mobile and wireless Internet solution space, a ubiquitous computing Internet is well within the research and industrial community's design reach - a decentralized system design, which is not solely driven by static physical models and sound engineering principals, but more dynamically, perhaps sub-optimally at initial deployment and socially-influenced in its evolution. To complement today's Internet system, this thesis proposes a Decentralized Communication System (DCS) architecture with the following characteristics: flat physical topologies with numerous compute oriented and communication intensive nodes in the network with many of these nodes operating in multiple functional roles; self-organizing virtual structures formed through alternative mobility scenarios and capable of serving ad hoc networking formations; emergent operations and control with limited dependency on centralized control and management administration. Today, decentralized systems are not commercially scalable or viable for broad adoption in the same way we have to come to rely on the Internet or telephony systems. The premise in this thesis is that DCS can reach high levels of resilience, usefulness, scale that the industry has come to experience with traditional centralized systems by exploiting the following properties: (i.) network density and topological diversity; (ii.) self-organization and emergent attributes; (iii.) cooperative and dynamic infrastructure; and (iv.) node role diversity. This thesis delivers key contributions towards advancing the current state of the art in decentralized systems. First, we present the vision and a conceptual framework for DCS. Second, the thesis demonstrates that such a framework and concept architecture is feasible by prototyping a DCS platform that exhibits the above properties or minimally, demonstrates that these properties are feasible through prototyped network services. Third, this work expands on an alternative approach to network clustering using hierarchical virtual clusters (HVC) to facilitate self-organizing network structures. With increasing network complexity, decentralized systems can generally lead to unreliable and irregular service quality, especially given unpredictable node mobility and traffic dynamics. The HVC framework is an architectural strategy to address organizational disorder associated with traditional decentralized systems. The proposed HVC architecture along with the associated promotional methodology organizes distributed control and management services by leveraging alternative organizational models (e.g., peer-to-peer (P2P), centralized or tiered) in hierarchical and virtual fashion. Through simulation and analytical modeling, we demonstrate HVC efficiencies in DCS structural scalability and resilience by comparing static and dynamic HVC node configurations against traditional physical configurations based on P2P, centralized or tiered structures. Next, an emergent management architecture for DCS exploiting HVC for self-organization, introduces emergence as an operational approach to scaling DCS services for state management and policy control. In this thesis, emergence scales in hierarchical fashion using virtual clustering to create multiple tiers of local and global separation for aggregation, distribution and network control. Emergence is an architectural objective, which HVC introduces into the proposed self-management design for scaling and stability purposes. Since HVC expands the clustering model hierarchically and virtually, a clusterhead (CH) node, positioned as a proxy for a specific cluster or grouped DCS nodes, can also operate in a micro-capacity as a peer member of an organized cluster in a higher tier. As the HVC promotional process continues through the hierarchy, each tier of the hierarchy exhibits emergent behavior. With HVC as the self-organizing structural framework, a multi-tiered, emergent architecture enables the decentralized management strategy to improve scaling objectives that traditionally challenge decentralized systems. The HVC organizational concept and the emergence properties align with and the view of the human brain's neocortex layering structure of sensory storage, prediction and intelligence. It is the position in this thesis, that for DCS to scale and maintain broad stability, network control and management must strive towards an emergent or natural approach. While today's models for network control and management have proven to lack scalability and responsiveness based on pure centralized models, it is unlikely that singular organizational models can withstand the operational complexities associated with DCS. In this work, we integrate emergence and learning-based methods in a cooperative computing manner towards realizing DCS self-management. However, unlike many existing work in these areas which break down with increased network complexity and dynamics, the proposed HVC framework is utilized to offset these issues through effective separation, aggregation and asynchronous processing of both distributed state and policy. Using modeling techniques, we demonstrate that such architecture is feasible and can improve the operational robustness of DCS. The modeling emphasis focuses on demonstrating the operational advantages of an HVC-based organizational strategy for emergent management services (i.e., reachability, availability or performance). By integrating the two approaches, the DCS architecture forms a scalable system to address the challenges associated with traditional decentralized systems. The hypothesis is that the emergent management system architecture will improve the operational scaling properties of DCS-based applications and services. Additionally, we demonstrate structural flexibility of HVC as an underlying service infrastructure to build and deploy DCS applications and layered services. The modeling results demonstrate that an HVC-based emergent management and control system operationally outperforms traditional structural organizational models. In summary, this thesis brings together the above contributions towards delivering a scalable, decentralized system for Internet mobile computing and communications

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Security attacks and solutions on SDN control plane: A survey

Sommario Software Defined Networks (SDN) è un modello di rete programmabile aperto promosso da ONF , che è stato un fattore chiave per le recenti tendenze tecnologiche. SDN esplora la separazione dei dati e del piano di controllo . Diversamente dai concetti passati, SDN introduce l’idea di separazione del piano di controllo (decisioni di instradamento e traffico) e piano dati (decisioni di inoltro basate sul piano di controllo) che sfida l’integrazione verticale raggiunta dalle reti tradizionali, in cui dispositivi di rete come router e switch accumulano entrambe le funzioni. SDN presenta alcuni vantaggi come la gestione centralizzata e la possibilità di essere programmato su richiesta. Oltre a questi vantaggi, SDN presenta ancora vulnerabilità di sicurezza e, tra queste,le più letali prendono di mira il piano di controllo. Come i controllers che risiedono sul piano di con- trollo gestiscono l’infrastruttura e i dispositivi di rete sottostanti (es. router/switch), anche qualsiasi insicurezza, minacce, malware o problemi durante lo svolgimento delle attività da parte del controller, possono causare interruzioni dell’intera rete. In particolare, per la sua posizione centralizzata, il con- troller SDN è visto come un punto di fallimento. Di conseguenza, qualsiasi attacco o vulnerabilità che prende di mira il piano di controllo o il controller è considerato fatale al punto da sconvolgere l’intera rete. In questa tesi, le minacce alla sicurezza e gli attacchi mirati al piano di controllo (SDN) sono identificati e classificati in diversi gruppi in base a come causano l’impatto sul piano di controllo. Per ottenere risultati, è stata condotta un’ampia ricerca bibliografica attraverso uno studio appro- fondito degli articoli di ricerca esistenti che discutono di una serie di attacchi e delle relative soluzioni per il piano di controllo SDN. Principalmente, come soluzioni intese a rilevare, mitigare o proteggere il (SDN) sono stati presi in considerazione le potenziali minacce gli attachi al piano di controllo. Sulla base di questo compito, gli articoli selezionati sono stati classificati rispetto al loro impatto potenziale sul piano di controllo (SDN) come diretti e indiretti. Ove applicabile, è stato fornito un confronto tra le soluzioni che affrontano lo stesso attacco. Inoltre, sono stati presentati i vantaggi e gli svantaggi delle soluzioni che affrontano diversi attacchi . Infine, una discussione sui risultati e sui esitti ottenuti durante questo processo di indagine e sono stati affrontatti suggerimenti di lavoro futuri estratti du- rante il processo di revisione. Parole chiave : SDN, Sicurezza, Piano di controllo, Denial of Service, Attacchi alla topologiaAbstract Software Defined Networks (SDN) is an open programmable network model promoted by ONF that has been a key-enabler of recent technology trends. SDN explores the separation of data and control plane. Different from the past concepts, SDN introduces the idea of separation of the control plane (routing and traffic decisions) and data plane (forwarding decisions based on the control plane) that challenges the vertical integration achieved by the traditional networks, in which network devices such as router and switches accumulate both functions. SDN presents some advantages such as centralized management and the ability to be programmed on demand. Apart from these benefits, SDN still presents security vulnerabilities and among them, the most lethal ones are targeting the control plane. As the controllers residing on the control plane manages the underlying networking infrastructure and devices (i.e., routers/switches), any security threat, malware, or issues during the carrying out of activities by the controller can lead to disruption of the entire network. In particular, due to its centralized position, the (SDN) controller is seen as a single point of failure. As a result, any attack or vulnerability targeting the control plane or controller is considered fatal to the point of disrupting the whole network. In this thesis, the security threats and attacks targeting the (SDN) control plane are identified and categorized into different groups by considering how they cause an impact to the control plane. To obtain results, extensive literature research has been carried out by performing an in-depth study of the existing research articles that discusses an array of attacks and their corresponding solutions for the (SDN) control plane. Mainly, the solutions intended to detect, mitigate, or protect the (SDN) control plane against potential threats and attacks have been considered. On basis of this task, the potential articles selected were categorized with respect to their impact to the (SDN) control plane as direct and indirect. Where applicable a comparison of the solutions addressing the same attack has been provided. Moreover, the advantages and disadvantages of the solutions addressing the respective attacks are presented. Finally, a discussion regarding the findings and results obtained during this su- veying process and future work suggestions extracted during the review process have been discussed. Keywords: SDN, Security, Control Plane, Denial of Service, Topology Attacks, Openflo

A survey of blockchain and artificial intelligence for 6G wireless communications

The research on the sixth-generation (6G) wireless communications for the development of future mobile communication networks has been officially launched around the world. 6G networks face multifarious challenges, such as resource-constrained mobile devices, difficult wireless resource management, high complexity of heterogeneous network architectures, explosive computing and storage requirements, privacy and security threats. To address these challenges, deploying blockchain and artificial intelligence (AI) in 6G networks may realize new breakthroughs in advancing network performances in terms of security, privacy, efficiency, cost, and more. In this paper, we provide a detailed survey of existing works on the application of blockchain and AI to 6G wireless communications. More specifically, we start with a brief overview of blockchain and AI. Then, we mainly review the recent advances in the fusion of blockchain and AI, and highlight the inevitable trend of deploying both blockchain and AI in wireless communications. Furthermore, we extensively explore integrating blockchain and AI for wireless communication systems, involving secure services and Internet of Things (IoT) smart applications. Particularly, some of the most talked-about key services based on blockchain and AI are introduced, such as spectrum management, computation allocation, content caching, and security and privacy. Moreover, we also focus on some important IoT smart applications supported by blockchain and AI, covering smart healthcare, smart transportation, smart grid, and unmanned aerial vehicles (UAVs). Moreover, we thoroughly discuss operating frequencies, visions, and requirements from the 6G perspective. We also analyze the open issues and research challenges for the joint deployment of blockchain and AI in 6G wireless communications. Lastly, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of blockchain and AI in 6G networks. We hope this surve..

A Comprehensive Collection and Analysis Model for the Drone Forensics Field

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain