Card Number Research Articles

Perlindungan Hukum terhadap Kreditur dalam Pelaksanaan Pendaftaran Hak Tanggungan secara Elektronik

This research explains the procedure for implementing electronic mortgage registration and discusses legal protection for creditors in mortgage registration through SKMHT in the event of the principal's death. This normative legal research utilizes a prescriptive approach based on legislation, cases, and conceptual analysis Primary data sources include relevant laws and regulations related to mortgages, supplemented by interviews with Notaries and PPAT, as well as secondary legal materials such as books, legal research papers, and previous studies in the form of journals, theses, and dissertations related to the research. Data collection techniques are carried out through interviews, observations, and literature studies Meanwhile, the analysis technique used by the author is qualitative model. The research results indicate that electronic registration of Mortgage Rights is carried out by interested parties through the website https://mitra.atr.bpn.go.id. However, in practice, there are still shortcomings in electronic Mortgage Rights registration, particularly when it is registered using SKMHT (Power of Attorney to Burden Mortgage Rights), and then the certificate owner passes away, the registration of Mortgage Rights cannot be continued. The solution to this problem is to add an optional refinement formulation in the Electronic Mortgage Rights System that allows for the replacement of the Identity Card Number (NIK) of the grantor with the NIK of the recipient if the Mortgage Rights are registered first with a SKMHT.

SECURITY IN SOCIAL MEDIA: AWARNESS OF PHISHING ATTACKS TECHNIQUES AND COUNTER MEASURES

As time and technology progresses, the social media security is one of the basic and most important aspects to consider when browsing the internet.Nowadays all people using social media mostly, using social media is widely spread among different age groups to meet several purposes including education, entertainment, or even pursue a research. With the increase of using smartphones and social media, the cyber security threats also increased. The most widely- perpetrated form of social media attacks is phishing; it is used to gather sensitive personal and professional information such as social security numbers, credit card numbers, bank account numbers, user logins and passwords, as well as other information entered through a web site or online sources. Furthermore, many users of different age groups and mainly the old age people have been exposed to social media security violations most times due to a lack of awareness about phishing attacks and how to address with them. Our paper focuses primarily to make people of all age group aware of exposure to this type of attack while using social media networks. Key Words: Cyber security, Social media, Phishing attack

Acupuncture for reducing the south to reinforce the north on executive function and sleep structure in patients with chronic insomnia disorder of heart-kidney disharmony.

To observe the efficacy of acupuncture for reducing the south to reinforce the north on executive function, sleep structure and sleep quality in patients with chronic insomnia disorder of heart-kidney disharmony. A total of 100 patients with chronic insomnia disorder of heart-kidney disharmony were randomized into an acupuncture group (50 cases, 1 case dropped out) and a western medication group (50 cases, 2 cases dropped out). Acupuncture for reducing the south to reinforce the north was applied at Baihui (GV 20) and bilateral Shenmen (HT 7), Sanyinjiao (SP 6), Shenmai (BL 62), Zhaohai (KI 6), Xinshu (BL 15), Shenshu (BL 23) in the acupuncture group, once a day, 5 days a week. Lorazepam tablet was given orally in the western medication group, 0.5-1 mg a time, once a day. Both groups were treated for 4 weeks. The Stroop color-word test (SCWT) indexes (the time consuming and the correct number of card A, B, C and the Stroop interference effect [SIE]), sleep structure indexes (total sleep time [TST], sleep latency [SL], wake after sleep onset [WASO], sleep efficiency [SE], non-rapid eye movement period 1 [N1], non-rapid eye movement period 2 [N2], non-rapid eye movement period 3 [N3], rapid eye movement period [REM]) and Pittsburgh sleep quality index (PSQI) score were observed before and after treatment in the two groups. After treatment, the time consuming of card B and C, the time consuming and the correct number of SIE, SL, WASO, N1, N2, as well as the sub-item scores and total score of PSQI were decreased (P<0.05, P<0.01), the correct number of card A, B and C, TST, SE, N3 and REM were increased (P<0.01) compared with those before treatment in the acupuncture group; the time consuming of card C and SIE, the correct number of card A and SIE, TST, SE, REM were increased (P<0.05, P<0.01), SL, WASO, N1, as well as the sub-item scores of sleep quality, sleep latency, sleep duration, sleep efficiency, daytime function and total score of PSQI were decreased (P<0.01) compared with those before treatment in the western medication group. After treatment, in the acupuncture group, the time consuming of card C, the time consuming and the correct number of SIE, N1, N2, as well as the sub-item scores of sleep quality, sleep dysfunction, daytime function and total score of PSQI were lower than those in the western medication group (P<0.01), the correct number of card B and C, N3, REM were higher than those in the western medication group (P<0.01). Acupuncture for reducing the south to reinforce the north can improve the executive function of patients with chronic insomnia disorder of heart-kidney disharmony, adjust the sleep structure, and improve the night sleep quality and daytime body function.

Study of the Structural and Optical Properties of the TiO2:Au Nanocomposite Mixture Prepared by Laser Ablation Method

In this study, TiO2, Au, and a mixture of TiO2: Au nanoparticles (NPs) were produced using a Nd: YAG pulsed laser. X-Ray diffraction (XRD), Transmission electron microscopy, and Ultraviolet–visible spectroscopy (UV) was used to characterize the produced nanoparticles. The XRD patterns of TiO2 NP showed that the diffraction peaks values were corresponded to the ICDD card number (21-1272) for the anatase form of TiO2 NPs. The XRD patterns of Au NPs values were corresponded to the ICDD card number (00-004-0784) of Au NPs. The XRD patterns the mixture TiO2: Au nanoparticle with mixing ratios (4:1, 3:2 and 2.5:2.5), it was found that the diffraction peaks were (2θ~ 38.138° and 64.687°) of TiO2 nano-particles, and the diffraction peaks were (2θ~ 44.341° and 77.577° ) Au NPs. The average crystalline size of the prepared nanoparticles was determined by Scherer's technique, it was found that the average crystalline size (TiO2 NPs) is (23.36 nm), whereas mixing titanium oxide with gold nanoparticles (TiO2: Au) increased the average crystallite size to (34.18, 34.15, and 34.20 nm) for the mixing ratios (4:1, 3:2, and 2.5:2.5), respectively. The TEM images showed that the TiO2 NPs are light grey in the combination of (TiO2: Au) NPs, while for the ratio (4-1), the Au NPs are dark, spherical spheres. However, when the ratio of Au particles is increased to (2:3), the TiO2 particles remain light grey but are smaller in size. The absorption spectra of pure TiO2 NPs is in the UV spectrum, whereas the absorption spectrum of the (TiO2: Au) mixture shifts towards the wavelengths of visible light in the range (520-540 nm), according to the optical characteristics of TiO2 NPs and a mixture of (TiO2:Au) NPs. The optical energy gap of pure TiO2 NPs is 3.22eV, and it decreases as the proportion of gold nanoparticles in the mixture increases.

Enhancing cybersecurity: A review and comparative analysis of convolutional neural network approaches for detecting URL-based phishing attacks

Phishing attempts to mimic the official websites of businesses, including banks, e-commerce, government offices, and financial institutions. Phishing websites aim to collect and retrieve sensitive data from users, including passwords, credit card numbers, email addresses, personal information, and so on. The growing frequency of phishing attacks has prompted the development of numerous anti-phishing technologies. Because machine learning (ML) techniques perform better in categorization problems, they are used extensively. But the most crucial features are not extracted by the algorithms in use today, which could result in a false categorization. In addition, the complex algorithms contribute to the long reaction time. To solve these issues, this study suggests using a Bidirectional Long Short-Term Memory-based Gated Highway Attention Block Convolutional Neural Network (BiLSTM-GHA-CNN) to detect phishing URLs.

The Squirrel Prover and its Logic

Security protocols are widely used today to secure transactions that take place over public channels like the Internet. Common uses include the secure transfer of sensitive information such as credit card numbers, or user authentication on a system. Because of their presence in many widely used applications ( e.g. electronic commerce, government-issued ID), developing methods and tools to verify security protocols has become an important research challenge. Such tools help increase our trust in protocols, and hence on the applications that rely on them.

Phishing Detection Using Machine Learning Algorithm

Phishing is a criminal scheme to steal the user’s personal data and other credential information. It is a fraud that acquires victim’s confidential information such as password, bank account detail, credit card number, financial username and password etc. and later it can be misuse by attacker. The use of machine learning algorithms in phishing detection has gained significant attention in recent years. This research paper aims to evaluate the effectiveness of various machine learning algorithms in detecting phishing URL’s/website. The algorithms tested in this study are Decision Tree, Random Forest, Multilayer Perceptron, XGBoost, Autoencoder Neural Network, and Support Vector Machines. A dataset of phishing URLs is used to train and test the algorithms, and their performance is evaluated based on metrics such as accuracy, precision, recall, and F1 Score. The paper takes in data of phished URL from Phishtank and legitimate URL from University of New Brunswick. The results of this study demonstrate that the Random Forest and XGBoost algorithms outperforms other algorithms in terms of accuracy and other performance metrics and the system has an overall accuracy of 98 %.

SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES

SMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage. Through marketing campaigns and attempts to gain private information, such as credit card numbers, these spam messages seek to further business or financial objectives. The duty of removing spam mails has therefore grown in significance. In response, a number of deep learning and machine learning methods have been used to identify SMS spam. Using data from the University of California, Irvine (UCI), this research examines the application of such strategies. Key Words: MultinomailNaiveBayes, SMS Spam Detection, NLTK, Vectorization, Feature Extraction.

Trivalent dysprosium activated LaSr2AlO5 nanophosphors for NUV-excited wLEDs: Insights into structural, optical, Judd-Ofelt parameters and temperature dependent photoluminescence

Gel-combustion (GC) methodology was implemented in the present research to produce a series of single phase Dy3+ doped LaSr2AlO5 nanophosphors for the usage in wLEDs. X-ray powder diffraction patterns verified the phase purity and crystal structure of the produced materials. The XRD inspection supported the successful fabrication of tetragonal structured (I4/mcm space group) phosphors and revealed good accordance with card number 70–2197 on the JCPDS database. We collected diffuse reflectance spectra (DRS) to examine how doping affects the band gap. Furthermore, energy-dispersive X-ray (EDX) spectroscopy and transmission electron microscopy (TEM) have been employed for assessing elemental composition and surface morphology. On 349 nm excitation, LaSr2AlO5:Dy3+ phosphors produce the distinctive yellow PL emissions causedby 4F9/2→6H13/2 transition. Using excitation spectra, the Judd-Ofelt (J-O) characteristic (Ω = 2, 4, 6) have been determined. The Dy3+ transition's features show that the J-O parameters followed the trend which suggested an asymmetric environment around the ligand. The electric dipole transition predominated in the luminescence spectrum, causing the LaSr2AlO5:Dy3+ phosphor to emit a yellowish whitecolor. The fundamental mechanism for the energy transfer and concentration quenching instances among activator ions was discovered to be dipole-quadrupole interactions. Temperature dependent luminescence showed that the synthesized phosphors had thermal stability up to 523 K with activation energy of 0.1312 eV. The future prospects of the synthesized phosphor as an effective choice for near-ultraviolet-activated wLEDs are substantially supported by current comprehensive research.

REVIEW OF COMPLETENESS OF DEATH CERTIFICATE FILLING AT BANTEN

Background: Filling in the Death Certificate at RSU Banten is the part that gets attention in this study. The Death Certificate form was found to be incomplete in patient identification, completeness of important reports, author authentication and good records. The Medical Certificate of Cause of Death (SMPK) is the main source of mortality data. Death information is usually obtained from health practitioners or in cases of death due to accidents, violence and other diseases. Based on a preliminary study in March 2020 at Banten General Hospital, it was found that out of 20 death certificate forms there were 10 incomplete death certificate forms, including the incomplete identification component of 7.75 (39%) such as in the death serial number section, the main number population and population number. 9 (45%) important report components were incomplete, such as the type of examination, ICD code, method and location of burial. The incomplete authentication component of 10 (50%) is as in the reporting signature and reporting name. An incomplete record component of 6.6 (35%) is like a lot of blanks. The purpose of the study was to find out the Completeness of Filling in Death Certificates at RSU Banten and the results of this study were expected to be useful for hospitals as input and consideration in addressing the issue of completeness of medical certificates causing death. and the approach used in this research is to use a comparative approach, namely through a checklist and interviews at RSU Banten. The research method used is quantitative research. The population in January- February 2020 was 233 death certificate forms at RSU Banten, while the subject population was 12 officers who were responsible for completing death certificates. The sample object taken for this research is a Death Certificate form as many as 77 samples. While the sample of subjects in this study were 2 officers who filled out death certificates. Sampling Technique Subjects in this study used non-random (non-probability sampling) with purposive sampling. The results showed that the SOP (Standard Operating Procedure) already exists and is complete which regulates the Filling of Death Certificates at RSU Banten in the installation section of the funeral procession (IPJ), but it is still not fully implemented because there are still many incomplete forms, from the 5 SOP items it is known that those who are complete with SOP guidelines are (100%) while those that are incomplete with SOP guidelines are (0%). The results of completeness in patient identification with a complete percentage are found in the sub-components of medical record numbers and names, which are 77 (100%), while the incomplete percentages are found in the sub-components of death serial number, population identification number and population identification number, which is 77 (100% , Important reports on death certificates that are filled out completely are found in the time of death sub-component, which is 77 (100%), while the completeness of filling out important reports that are incomplete on the death serial number is 77 (100%).Authentication Author on death certificates which is filled in completely is contained in the sub-components of the doctor's TTD sign and the doctor's name, which is 77 (100%), while the incomplete completeness of author authentication on the reporting blood pressure, the name of the reporter, the identity card number of the reporting person, the address of the reporter is 77 (100%). Both the death certificate that is filled in completely, there are sub-components, there are no scribbles and there is no type - ex, which is 77 (100%). while the completeness of incomplete filling on a good record is found in the sub-components, there are no empty parts, namely 77 (100%). Data collection was carried out by survey, namely the researcher conducted direct interviews with respondents using a questionnaire. The research results obtained the following data; There were97 toddlers who had pneumonia or 26.5%, almost all of them 94.5% of mothers had good knowledge about pneumonia, 46.7% of mothers had sufficient behavior and 49.5% of mothers had sufficient attitudes and more than half of them 70.2% of mothers had not been exposed to information about pneumonia. The Bivariate results found that attitude and information exposure had a significant relationship with the incidence of pneumonia, while behavior and knowledge had no significant relationship with the incidence of pneumonia among toddlers in the city of Cilegon. From the results of this study, it is hoped that the Cilegon City Health Office can increase socialization and counseling about pneumonia through cooperation with related agencies so that it can improve the degree of public health.

The Cybersecurity Applied by Online Travel Agencies and Hotels to Protect Users’ Private Data in Smart Cities

The purpose of this paper is to analyse the cybersecurity in online travel agencies (OTAs) and hotel sectors to protect users’ private data in smart cities. Methodologically, this research uses a sample of information about cyberattacks that occurred during the period of 2000–2023 in companies operating as OTAs and in the travel, tourism, and food sectors, which was obtained from research articles. Then, we had to expand the research to include updated information about cyberattacks from digital newspapers, regulatory sources, and state data breach notification sites like CSIS, KonBriefing, EUROCONTROL, and GlobalData. The findings of the current research prove that hotels and OTAs were constantly exposed to cyberattacks in the period analysed, especially by data breaches and malware attacks; in fact, this is the main novelty of this research. In addition, these incidents were severe for both guests and tourism companies because their vulnerabilities and consequences affect the reputation of companies and smart cities where these firms operate, as well as consumer confidence. The results also showed that most of the cyberattacks examined in this manuscript were aimed at stealing information about the companies’ and users’ private data such as email addresses; credit card numbers, security codes, and expiration dates; and encoded magstripe data; among many other types of data. Cyberattacks and cyberthreats never disappear completely in the travel and tourism sectors because these illegal activities are closely related to the hacker’s thirst for power, fame, and wealth.

DEPLATFORMING THE SMART CITY: GIVING RESIDENTS CONTROL OVER THEIR PERSONAL DATA

Smart city platforms–encompassing mobile apps, cameras, sensors, algorithms, and predictive analytics—function as surveillance tools. Specifically, these Internet-connected devices and services generate troves of data on residents, including real-time geolocation, energy consumption habits, travel patterns, mobile device identifiers, Internet browsing history, phone contacts, credit card numbers, and much more. The proposed project is focused on the City of Long Beach’s vision to use data in ethical ways that avoid reinforcing existing racial biases and discriminatory decision-making. When fully implemented, this digital rights platform will operationalize both privacy and racial equity as priorities for all deployments of smart city technology. First, the platform will feature text and the open-source iconography that visually conveys how the City of Long Beach uses specific technologies, what data the devices collect and how the City utilizes that data. We plan to strategically deploy these information points across Long Beach, physically adjacent to or digitally embedded within civic technologies, e.g., sensors, cameras, small cells, mobile payment kiosks, and a 311 app. The platform will include a feedback application consisting of access (via QR code or hyperlink) to an online dashboard where users may learn additional details, update data collection preferences, and share comments/concerns with local government officials. The ultimate goal is to develop a backend solution that enables residents to opt-out of data collection. The platform will provide residents with a clear understanding of how local government applies predictive and diagnostic analytics to personal data, and will also empower community members by granting them agency.

TINJAUAN FIKIH MUAMALAH TERHADAP PENETAPAN DENDA PADA ARISAN ONLINE (STUDI KASUS NAGARI LIMA KAUM, KECAMATAN LIMA KAUM, KABUPATEN TANAH DATAR)

The main problem in this study is the implementation of the online arisan fine contract in Nagari Limo Kaum reviewed in muamalah fiqh. This study aims to explain and analyze the implementation of online arisan fines in Limo Kaum. As well as explaining and analyzing how muamalah fiqh views the implementation of the online arisan fine contract in Nagari Limo Kaum.This research method is a field research, using a qualitative descriptive approach. As the main data source, namely the people involved in implementing online arisan practices in Nagari Limo Kaum, the data sources obtained included the admin and 5 members of the arisan who played along. Meanwhile, secondary data sources were obtained through a number of books, journals and other reading sources available. relation to the title of the researcher can provide additional information or data to strengthen the primary data.Based on the results of the research, it was found that the implementation of fines on online arisan is that the admin promotes a chain message which is then distributed via WhatsApp messages. As well as prospective members who are interested the admin will provide the requirements for an ID card or cellphone number that can be contacted. After all is collected the admin will form an arisan group, in carrying out the arisan the admin does not pay at all and only runs the arisan, if there is a delay in paying the arisan the admin will give a fine to members of 10 thousand for those who have not received it and 15 thousand for those who have received the arisan . Meanwhile, according to the muamalah fiqh review, the practice of fines like this is contrary to the principle of muamalah where muamalah must be carried out clearly and openly and using the principle of consensual and prioritizing willingness in every transaction. Meanwhile, fines collected are not notified to members where they are used and in what amount the fines received by the admin were also not explained so that there it was seen that the admin was not open to its members resulting in the penalty contract at the turning into a fasid or damaged contract and the contract being classified as a vanity contract.

Europium-doped strontium gadolinium oxide phosphor: Investigating structural and photoluminescence characteristics via sol-gel combustion synthesis

SrGd2O4 phosphors doped with Eu3+ were successfully synthesized through a sol-gel combustion method, covering a range of dopant concentrations from 0.25 mol% to 3 mol%. The structural analysis of these phosphor materials was comprehensively conducted utilizing various techniques, including X-ray powder diffraction analysis (XRD), Energy Dispersive X-ray (EDX), and Fourier-transform infrared spectroscopy (FTIR). In addition to unveiling the structural characteristics, these analyses provide valuable insights into the compositional aspects, enhancing our understanding of the synthesized SrGd2O4:Eu3+ phosphors across different doping levels. XRD analysis findings validate the successful generation of the intended SrGd2O4 host, demonstrating orthorhombic system structures consistent with JPCD card number 98-019-3592. FTIR analyses conducted on the phosphor samples not only identify bending modes but also reveal intricate details about small vibration bonds within the material. When excited by the 349 nm laser, SrGd2O4:xEu3+ phosphors exhibit distinct photoluminescence (PL) properties like red emission at 614 nm from Eu3+. From the emission spectra, one can clearly observe that Eu3+ with an ionic radius close to the Gd3+ ion preferentially occupies the symmetry sites of the host lattice. The optimal doping concentration was determined to be 0.5 mol%, as revealed by the data in our study. Additionally, a deeper understanding of the luminescence quenching mechanism was attained, pinpointing the involvement of dipole-dipole (d-d) energy transfer in this intriguing phenomenon. This optimal concentration not only enhances the material's properties but also underscores the pivotal role of d-d interactions in governing the luminescence behavior within the doped system.

A Classifier Model to Detect Phishing Emails Using Ensemble Technique

Phishing attacks usually take advantage of weaknesses in the way users behave. An attacker sends an email to the recipient that mimics a genuine email with phishing links. When the recipient clicks on the embedded links, the attacker can harvest critical information like credit card numbers, usernames or passwords as a result of entering the compromised account. Online surveys have put phishing attacks as the leading attack for web content, mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attacks is about $1.5 billion annually. This is a global threat to information security, and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these losses and reputation injury. This research paper explores and reports the use of multiple machine learning models by using an algorithm called Random Forest and using more phishing email features to improve the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier to detect and prevent phishing emails and test the model with existing data. A dataset consisting of benign and phishing emails will be used to conduct supervised learning by the model. Expected accuracy is 99.9%, with a rate of less than 0.1% for False Negatives (FN) and False Positives (FP).

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.

Comparative Study of CatBoost, XGBoost, and LightGBM for Enhanced URL Phishing Detection: A Performance Assessment

Phishing via URLs involves cyber attackers crafting deceptive websites or emails, mimicking genuine entities like banks or social media outlets. The objective is to dupe users into divulging personal data, such as their passwords or card numbers. This study assesses the potential of machine learning in identifying phishing domains by constructing and contrasting three distinct models. These models, crafted using CatBoost, XGBoost, and LightGBM techniques, are then juxtaposed against prior solutions documented in academic literature. We employed the UCI phishing domains dataset, sourced from URLs, as a performance benchmark for our models. Findings indicate that the model built on CatBoost outperforms its counterparts and also surpasses earlier documented methods.

PERBANDINGAN KLASIFIKASI ALGORITMA K-NN, NEURAL NETWORK, NAÏVE BAYES, C 4.5 UNTUK MENDETEKSI WEB PHISING

The increasing number of internet users in the world and the rise of web phishing. Based on the Wearesocial report, there are several facts which include the number of world internet users that have reached 4.021 billion people. explained that of the hundreds of millions of internet users in Indonesia, 60 percent have accessed the internet using smartphones. Internet usage is dominated by socializing activities in cyberspace. Evidenced by the large number of world social media users, reaching 3,196 billion users. But with the rise of internet users and social media accompanied by the proliferation of phishing webs. The purpose of this attack is to make users believe that they interact with the official virtual or online site in question. Generally the information sought is in the form of a password, account or victim's credit card number. by way of being directed to a fake web or sending an email, banner or pop-up where the victim is asked to give his personal information. In this study C.4.5, Neural Network and K-Nerest Neighbor algorithm comparison will be conducted to determine the performance of the algorithm in detecting webphising by using classification techniques. Based on the model testing based on the Naïve Bayes method, Decision Tree C.4.5, K-NN, Neural Network uses the Weka framework v.3.8.2. The results of the Decision Tree C.4.5 algorithm have a higher level of accuracy with 89.66 percent accuracy.

Detecting Phishing Emails Using Random Forest and AdaBoost Classifier Model

Phishing attack occurs when a phishing email which is a legitimate-looking email, designed to lure the recipient into believing that it is a genuine email to open and click malicious links embedded into the email. This leads to user reveal sensitive information such as credit card number, usernames or passwords to the attacker thereby gaining entry into the compromised account. Online surveys have put phishing attack as the leading attack for web content mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attack is about $1.5 billion per year. This is a global threat to information security and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these loses and reputation injury. This research paper explores and reports the use of a combination of machine learning algorithms; Random Forest and AdaBoost and use of more phishing email features in improving the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier which can detect phishing and prevent phishing emails and test the model with existing data. A dataset consisting of both benign and phishing emails will be used to conduct a supervised learning by the model. Expected accuracy is 99.9%, False Negative (FN) and False Positive (FP) rates of 0.1% and below.

Trust Based Mechanism for Isolation of Malicious Nodes in Internet of Things

The Internet of Things systems are prone to the attacks as they have ad-hoc and limited resource structure. IoT-based systems are utilized for managing a large volume of information and assist in services related to industrial and medical applications. Due to this, the IoT attains becomes a target for a multitude of attackers and adversaries namely occasional hackers, cybercriminals, hacktivists, government, etc. The major goal of potential attackers is to steal the sensitive information such as credit card numbers, location data, credential of financial account and information related to health, by hacking the Internet of Things devices. The version number attack is one of malicious activity of IoT which affect network performance to great extend. The version number attack is triggered by the malicious nodes which can flood unlimited hello packets in the network. The hello flood attack raised situation of denial of service in the network. The trust based mechanism is proposed in this research work in which trust value is assigned to each node based on their activities. The node which is least trusted will be marked as malicious and get isolated from the network. The proposed scheme is implemented in NS2 and results are analyzed in terms of throughput, packetloss, energy consumption and delay.