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We perform an in-depth, systematic benchmarking study and evaluation of phishing features on diverse and extensive datasets. We propose a new taxonomy of features based on the interpretation and purpose of each feature. Next, we propose a benchmarking framework called ‘PhishBench,’ which enables us to evaluate and compare the existing features for phishing detection systematically and thoroughly under identical experimental conditions, i.e., unified system specification, datasets, classifiers, and evaluation metrics. PhishBench is a first in the field of benchmarking phishing related research and incorporates thorough and systematic evaluation and feature comparison. We use PhishBench to test methods published in the phishing literature on new and diverse datasets to check their robustness and scalability. We study how dataset characteristics, e.g., varying legitimate to phishing ratios and increasing the size of imbalanced datasets, affect classification performance. Our results show that the imbalanced nature of phishing attacks affects the detection systems’ performance and researchers should take this into account when proposing a new method. We also found that retraining alone is not enough to defeat new attacks. New features and techniques are required to stop attackers from fooling detection systems.

Due to the scarcity of skilled labourers, areca growers are finding it difficult to harvest their crop. Arecanut tree climbing is a risky job which requires skilled labourers. If a person slips while climbing, it will be irreparable damage to him and as well as to his family. In recent years, getting labourers who climb the arecanut tree is a major challenge. Arecanut trees grow to a height of about 60 to 70 feet. It is required to climb the arecanut tree around four to five times in a year for harvesting. Only skilled labourers can carry out these operations. In this paper, a Smart Arecanut Plucking Robot is designed through which some of the above difficulties are addressed. This robot is designed to climb the arecanut trees of varying diameter. Here, the robot can be operated either in manual mode or in automatic mode. Six pneumatic cylinders are used with an air compressor for robot movement. Arduino UNO is used for controlling the pneumatic cylinders. The robot climbs as human climbs the tree and a camera is mounted on the robot to get the live video streaming. Raspberry Pi is used for interfacing camera to get live video streaming of the plucking operation. By watching the live video, the position of the cutter can be adjusted. The android application RaspiCAM Remote installed in users android phone is used to watch the live video stream sent by the Raspberry Pi present in the robotic unit. A robot is successfully tested on an arecanut tree of varying trunk diameter (up to 7 inches) and heights (up to a maximum of 45 feet). The user is successfully able to guide the robot to cut the ripened arecanut bunch.

Malware has become a huge problem and obfuscation techniques used by attackers means that signature management for malware becomes too time and space consuming. If we can cluster different versions of the same malware together, we can attain many benefits. In this paper, we study the malware clustering problem systematically using several algorithms, distance functions and sets of features. A real-world, ground-truth dataset and multiple metrics are used to evaluate the performance of these suite of approaches. Agglomerative Hierarchical clustering, Spectral clustering, and DBSCAN emerged as the winning algorithms.

Hurricane Harvey was a major disaster that struck Texas in August 2017. We wondered whether such disasters are being exploited by phishers, as phishing is one of the most popular attacks. In October 2017, we surveyed the University of Houston population to study their experiences and behavior during/after the storm. Over 300 responses were received. This paper discusses our study design and the results from that survey. Results show that the storm did cause about 6.3% of the participants to change their behavior, i.e., they clicked on links or downloaded attachments they normally would NOT have. An analysis using the symmetric Jensen-Shannon divergence shows that the increased email volume and the timing of arrival or non-arrival of hurricane-related spam had the biggest impacts.

The different DC - DC Converter topologies such as Buck - Boost Converter, Boost Converter, Buck Converter, Sepic Converter and the Phase Shifted Full Bridge DC - DC Converter have been examined and dealt with in this paper. Comparison, study as well as simulation of these topologies have been done for the implementation of a topology best suited for the Solar based inverterless system. The performance parameters are Output Voltage, Output Current and Efficiency and the necessary waveforms corresponding to these parameters have been simulated. The system can be exclusively built for DC loads as DC systems consume half the power required by the AC Systems and the unnecessary conversions are avoided due to which the efficiency and reliability of the system is highly increased. The analysis and simulations have been done on the MATLAB’17 software.

Active power filtering is one of the most promising and mature technologies used for eradication of harmonics in power systems. This paper presents the implementation and simulation of shunt active power filter with diode rectifier connected to RL Load. Generation of reference fundamental load current signal is done without calculating load active power. The simulation results are analyzed and THD-I of source current is compared in both cases (with and without filter) to bring out the effects of active power filter on source current. The results show successful reduction of harmonics from source current.

Video of classroom lectures is a valuable and increasingly popular learning resource. A major weakness of the video format is the inability to quickly access the content of interest. The goal of this work is to automatically partition a lecture video into topical segments which are then presented to the user in a customized video player. The approach taken in this work is to identify topics based on text similarities across the video. The paper investigates the use of screen text extracted by Optical Character Recognition tools, as well as the speech text extracted by Automatic Speech Recognition tools. An automatic text-based segmentation algorithm is developed to identify topic changes and evaluated on a set of twenty-five lecture videos. The key conclusions are as follows. Screen text is a better guide to discovering topic changes than speech text, the effectiveness of speech text can be improved significantly with the correction of speech text, and combining screen text and accurate speech text can improve accuracy. Results are presented from surveys showing a high level of satisfaction among student users of automatically segmented videos. The paper also discusses the limits of automatic segmentation and the reasons why it is far from perfect.

Big data is a hot topic and has found various applications in different areas such as scientific research, financial analysis, and market studies. The development of cloud computing technology provides an adequate platform for big data applications. No matter public or private, the outsourcing and sharing characteristics of the computation model make security a big concern for big data processing in the cloud. Most existing works focus on protection of data privacy but integrity protection of the processing procedure receives little attention, which may lead the big data application user to wrong conclusions and cause serious consequences. To address this challenge, we design an integrity protection solution for big data processing in cloud environments using reputation based redundancy computation. The implementation and experiment results show that the solution only adds limited cost to achieve integrity protection and is practical for real world applications.

A formal analysis of a key management protocol, called LEAP (Localized Encryption and Authentication Protocol), intended for wireless sensor networks is presented in this paper. LEAP is modeled using the high level formal language HLSPL and checked using the AVISPA tool for attacks on the security and authenticity of the exchanges. We focus on the protocol's establishment of pairwise keys for nearest neighbors and for multi-hop neighbors. We then use this foundation to test the protocol's method of cluster key redistribution. Finally, we check LEAP's use of μTESLA, an authentication protocol utilized a one-way key chain and delayed key disclosure, which LEAP uses for authentication of node revocation messages.

Phishing causes billions of dollars in damage every year and poses a serious threat to the Internet economy. Among the many possible communication channels, electronic mail still remains the most commonly used medium to launch phishing attacks. In this paper, we present a two dimensional approach to detecting phishing emails. We devise two independent, unsupervised classifiers, namely the link and header classifiers, and two combinations of these classifiers. We show that our schemes significantly outperform the previous unsupervised and supervised phishing detection schemes for emails in the literature. We also utilize contextual information, when available, to detect phishing. Finally, our protocol is designed to detect phishing at the email level rather than detecting fraudulent, masqueraded websites. Our implementation framework called PhishSnag, operates between a user's mail transfer agent (MTA) and mail user agent (MUA) and processes each arriving email for phishing attacks even before reaching the inbox.

Existing undecidability proofs of checking secrecy of cryptographic protocols have the limitations of not considering protocols common in literature, which are in the form of communication sequences, since only protocols as non- matching roles are considered, and not considering an attacker who is an insider since only an outsider attacker is considered. Therefore the complexity of checking the realistic attacks, such as the attack to the public key Needham-Schroeder protocol, is unknown. The limitations have been observed independently and described similarly by Froschle in a recently published paper, where two open problems are posted. This paper investigates these limitations, and we present a generally applicable approach by reductions with novel features from the reachability problem of 2-counter machines, and we solve the two open problems. We also prove the undecidability of checking authentication which is the first detailed proof to our best knowledge. A unique feature of the proof is to directly address the secrecy and authentication goals as defined for the public key Needham-Schroeder protocol, whose attack has motivated many researches of formal verification of security protocols.

As huge amounts of knowledge are created rapidly, effective information access becomes an important issue. Especially for critical domains, such as medical and financial areas, efficient retrieval of concise and relevant information is highly desired. In this paper we propose a new user query based text summarization technique that makes use of unified medical language system, an ontology knowledge source from National Library of Medicine. We compare our method with keyword-only approach, and our ontology-based method performs clearly better. Our method also shows potential to be used in other information retrieval areas

Genetic Algorithms are search techniques for global optimization in a complex search space. One of the interesting features of a Genetic Algorithm is that they lend themselves very well for parallel and distributed processing. This feature of Genetic Algorithm is useful in improving its computation efficiency for complex optimization problems. In this paper, we have implemented Genetic Algorithm in a distributed environment such that its implementation problem independent. This key attribute of distributed implementation allows it to be used for different types of optimization problems. Fault tolerance and user transparency are two other important features of our distributed Genetic Algorithm implementation. The effectiveness and generality of Genetic Algorithms have been demonstrated by solving two problems of network topology design and file allocation.

Let G = h x w be a rectangular grid and H = s x s be the optimal sqaure grid for G, i.e., the least square grid which is no less than G in size. In this paper, an embedding scheme is presented for embedding G into H such that the dilation cost is at most 6. The significance of this result is that optimal expansion is always achieved, regardless of the aspect ratio of the rectangle, while keeping the dilation constant.