Search results for: Spam detection.

Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

Abstract:

With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers, but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a highquality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: Fake reviews, Feature collection, Opinion spam, Spam detection.

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Authors: T. Kumaresan, S. Sanjushree, C. Palanisamy

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Image spam is a kind of email spam where the spam text is embedded with an image. It is a new spamming technique being used by spammers to send their messages to bulk of internet users. Spam email has become a big problem in the lives of internet users, causing time consumption and economic losses. The main objective of this paper is to detect the image spam by using histogram properties of an image. Though there are many techniques to automatically detect and avoid this problem, spammers employing new tricks to bypass those techniques, as a result those techniques are inefficient to detect the spam mails. In this paper we have proposed a new method to detect the image spam. Here the image features are extracted by using RGB histogram, HSV histogram and combination of both RGB and HSV histogram. Based on the optimized image feature set classification is done by using k- Nearest Neighbor(k-NN) algorithm. Experimental result shows that our method has achieved better accuracy. From the result it is known that combination of RGB and HSV histogram with k-NN algorithm gives the best accuracy in spam detection.

Keywords: File Type, HSV Histogram, k-NN, RGB Histogram, Spam Detection.

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Authors: Claudiu N.Musat

Abstract:

Due to the constant increase in the volume of information available to applications in fields varying from medical diagnosis to web search engines, accurate support of similarity becomes an important task. This is also the case of spam filtering techniques where the similarities between the known and incoming messages are the fundaments of making the spam/not spam decision. We present a novel approach to filtering based solely on layout, whose goal is not only to correctly identify spam, but also warn about major emerging threats. We propose a mathematical formulation of the email message layout and based on it we elaborate an algorithm to separate different types of emails and find the new, numerically relevant spam types.

Keywords: Clustering, layout, k-means, spam.

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Authors: Raheleh Kholghi, Soheil Behnam Roudsari, Alireza Nemaney Pour

Abstract:

Spam mails are unwanted mails sent to large number of users. Spam mails not only consume the network resources, but cause security threats as well. This paper proposes an efficient technique to detect, and to prevent spam mail in the sender side rather than the receiver side. This technique is based on a counter set on the sender server. When a mail is transmitted to the server, the mail server checks the number of the recipients based on its counter policy. The counter policy performed by the mail server is based on some pre-defined criteria. When the number of recipients exceeds the counter policy, the mail server discontinues the rest of the process, and sends a failure mail to sender of the mail; otherwise the mail is transmitted through the network. By using this technique, the usage of network resources such as bandwidth, and memory is preserved. The simulation results in real network show that when the counter is set on the sender side, the time required for spam mail detection is 100 times faster than the time the counter is set on the receiver side, and the network resources are preserved largely compared with other anti-spam mail techniques in the receiver side.

Keywords: Anti-spam, Mail server, Sender side, Spam mail

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Authors: Yanti Rosmunie Bujang, Husnayati Hussin

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This paper attempts to discuss the spam issue from the Malaysian e-mail users- perspective. The purpose is to discover how Malaysian users handle the spam e-mail problem. From the experiences we hope to discover the necessary effort needed to be undertaken to face this problem in the context of Malaysia. A survey was conducted to understand how Malaysian individual perceived spam and what they actually do with the spam e-mail they received in their daily life. The findings indicate that the level of awareness on spam issue in action is still low and need some extra effort by government and relevant agencies to increase their level of awareness.

Keywords: E-mail, Malaysia, spam, users' perspective.

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Authors: Fahim A., Mutahira N. Naseem

Abstract:

Spam is any unwanted electronic message or material in any form posted too many people. As the world is growing as global world, social networking sites play an important role in making world global providing people from different parts of the world a platform to meet and express their views. Among different social networking sites Facebook become the leading one. With increase in usage different users start abusive use of Facebook by posting or creating ways to post spam. This paper highlights the potential spam types nowadays Facebook users’ faces. This paper also provide the reason how user become victim to spam attack. A methodology is proposed in the end discusses how to handle different types of spam.

Keywords: Artificial neural networks, Facebook spam, social networking sites, spam filter.

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Authors: N. Arulanand, K. Premalatha

Abstract:

Bloom filter is a probabilistic and memory efficient data structure designed to answer rapidly whether an element is present in a set. It tells that the element is definitely not in the set but its presence is with certain probability. The trade-off to use Bloom filter is a certain configurable risk of false positives. The odds of a false positive can be made very low if the number of hash function is sufficiently large. For spam detection, weight is attached to each set of elements. The spam weight for a word is a measure used to rate the e-mail. Each word is assigned to a Bloom filter based on its weight. The proposed work introduces an enhanced concept in Bloom filter called Bin Bloom Filter (BBF). The performance of BBF over conventional Bloom filter is evaluated under various optimization techniques. Real time data set and synthetic data sets are used for experimental analysis and the results are demonstrated for bin sizes 4, 5, 6 and 7. Finally analyzing the results, it is found that the BBF which uses heuristic techniques performs better than the traditional Bloom filter in spam detection.

Keywords: Cuckoo search algorithm, levy’s flight, metaheuristic, optimal weight.

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Authors: Wojciech P. Gajewski

Abstract:

The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag Of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without affecting the classifier precision as it happens when only the NBC based on single words is retrained.

Keywords: Text classification, naïve Bayesian classification, spam, email.

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Authors: Jatinderkumar R. Saini, Apurva A. Desai

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In recent times, the problem of Unsolicited Bulk Email (UBE) or commonly known as Spam Email, has increased at a tremendous growth rate. We present an analysis of survey based on classifications of UBE in various research works. There are many research instances for classification between spam and non-spam emails but very few research instances are available for classification of spam emails, per se. This paper does not intend to assert some UBE classification to be better than the others nor does it propose any new classification but it bemoans the lack of harmony on number and definition of categories proposed by different researchers. The paper also elaborates on factors like intent of spammer, content of UBE and ambiguity in different categories as proposed in related research works of classifications of UBE.

Keywords: E-mail, Scams, Spam Email, Unsolicited Bulk Email(UBE)

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

Abstract:

Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: Classification, data mining, spam filtering, naive Bayes, decision tree.

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Authors: Leili Nosrati, Alireza Nemaney Pour

Abstract:

Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms.

Keywords: Concept drift, Content-based filtering, E-mail, Spammail.

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Authors: Shao Bo Cheng, Yong-Jin Han, Se Young Park, Seong-Bae Park

Abstract:

Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like ebay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C E-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C E-commerce.

Keywords: Spam Keyword, E-commerce, keyword features, spam filtering.

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Authors: Jatinderkumar R. Saini, Apurva A. Desai

Abstract:

Email has become a fast and cheap means of online communication. The main threat to email is Unsolicited Bulk Email (UBE), commonly called spam email. The current work aims at identification of unigrams in more than 2700 UBE that advertise body-enhancement drugs. The identification is based on the requirement that the unigram is neither present in dictionary, nor is a slang term. The motives of the paper are many fold. This is an attempt to analyze spamming behaviour and employment of wordmutation technique. On the side-lines of the paper, we have attempted to better understand the spam, the slang and their interplay. The problem has been addressed by employing Tokenization technique and Unigram BOW model. We found that the non-lexicon words constitute nearly 66% of total number of lexis of corpus whereas non-slang words constitute nearly 2.4% of non-lexicon words. Further, non-lexicon non-slang unigrams composed of 2 lexicon words, form more than 71% of the total number of such unigrams. To the best of our knowledge, this is the first attempt to analyze usage of non-lexicon non-slang unigrams in any kind of UBE.

Keywords: Body Enhancement, Lexicon, Medicinal, Slang, Unigram, Unsolicited Bulk e-mail (UBE)

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Authors: Wei Zhang, Hong Yu, Xian-ping Zhao, Da-da Wang, Fei Xue

Abstract:

For lack of the visualization of the ultrasonic detection method of partial discharge (PD), the ultrasonic detection technology combined with the X-ray visual detection method (UXV) is proposed. The method can conduct qualitative analysis accurately and conduct reliable positioning diagnosis to the internal insulation defects of GIS, and while it could make up the blindness of the X-ray visual detection method and improve the detection rate. In this paper, an experimental model of GIS is used as the trial platform, a variety of insulation defects are set inside the GIS cavity. With the proposed method, the ultrasonic method is used to conduct the preliminary detection, and then the X-ray visual detection is used to locate and diagnose precisely. Therefore, the proposed UXV technology is feasible and practical.

Keywords: GIS, ultrasonic, visual detection, X-ray

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Authors: Dilip Singh Sisodia, Shrish Verma

Abstract:

In this paper we are presenting some spamming techniques their behaviour and possible solutions. We have analyzed how Spammers enters into online social networking sites (OSNSs) to target them and diverse techniques used by them for this purpose. Spamming is very common issue in present era of Internet especially through Online Social Networking Sites (like Facebook, Twitter, and Google+ etc.). Spam messages keep wasting Internet bandwidth and the storage space of servers. On social networking sites; spammers often disguise themselves by creating fake accounts and hijacking user’s accounts for personal gains. They behave like normal user and they continue to change their spamming strategy. Following spamming techniques are discussed in this paper like clickjacking, social engineered attacks, cross site scripting, URL shortening, and drive by download. We have used elgg framework for demonstration of some of spamming threats and respective implementation of solutions.

Keywords: Online social networking sites, spam attacks, Internet, clickjacking/likejacking, drive-by-download, URL shortening, cross site scripting, socially engineered attacks, elgg framework.

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Authors: Jae-Jeong Kim, Ki-Ro Kim, Hyoung-Kyu Song

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In this paper, we propose an efficient signal detector that switches M parameter of QRD-M detection scheme is proposed for MIMO-OFDM system. The proposed detection scheme calculates the threshold by 1-norm condition number and then switches M parameter of QRD-M detection scheme according to channel information. If channel condition is bad, the parameter M is set to high value to increase the accuracy of detection. If channel condition is good, the parameter M is set to low value to reduce complexity of detection. Therefore, the proposed detection scheme has better tradeoff between BER performance and complexity than the conventional detection scheme. The simulation result shows that the complexity of proposed detection scheme is lower than QRD-M detection scheme with similar BER performance.

Keywords: MIMO-OFDM, QRD-M, Channel condition.

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Authors: Saad M. Al-Garni, Adel A. Abdennour

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Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.

Keywords: Image processing, Automatic background extraction, Moving vehicle detection.

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Authors: Mohamed E. Salem Abozaed

Abstract:

Detection and classification of power quality (PQ) disturbances is an important consideration to electrical utilities and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. S-transform algorithm and continuous wavelet transforms (CWT) are time-frequency algorithms, and both of them are powerful in detection and classification of PQ disturbances. This paper presents detection and classification of PQ disturbances using S-transform and CWT algorithms. The results of detection and classification, provides that S-transform is more accurate in detection and classification for most PQ disturbance than CWT algorithm, where as CWT algorithm more powerful in detection in some disturbances like notching

Keywords: CWT, Disturbances classification, Disturbances detection, Power quality, S-transform.

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Authors: Kyogun Chang, Yoon Bok Lee

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Fault detection determines faultexistence and detecting time. This paper discusses two layered fault detection methods to enhance the reliability and safety. Two layered fault detection methods consist of fault detection methods of component level controllers and system level controllers. Component level controllers detect faults by using limit checking, model-based detection, and data-driven detection and system level controllers execute detection by stability analysis which can detect unknown changes. System level controllers compare detection results via stability with fault signals from lower level controllers. This paper addresses fault detection methods via stability and suggests fault detection criteria in nonlinear systems. The fault detection method applies tothe hybrid control unit of a military hybrid electric vehicleso that the hybrid control unit can detect faults of the traction motor.

Keywords: Two Layered Fault Detection, Stability Analysis, Fault-Tolerant Control

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Authors: Madihah Mohd Saudi, Andrea J Cullen, Mike E Woodward

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This paper presents a new STAKCERT KDD processes for worm detection. The enhancement introduced in the data-preprocessing resulted in the formation of a new STAKCERT model for worm detection. In this paper we explained in detail how all the processes involved in the STAKCERT KDD processes are applied within the STAKCERT model for worm detection. Based on the experiment conducted, the STAKCERT model yielded a 98.13% accuracy rate for worm detection by integrating the STAKCERT KDD processes.

Keywords: data mining, incident response, KDD processes, security metrics and worm detection.

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Authors: Jianhua Wang, Yan Yu

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This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.

Keywords: RBF, Elman, anomaly detection, misuse detection, hybrid neural network.

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Authors: Youguo Wang, Lenan Wu

Abstract:

Stochastic resonance (SR) is a phenomenon whereby the signal transmission or signal processing through certain nonlinear systems can be improved by adding noise. This paper discusses SR in nonlinear signal detection by a simple test statistic, which can be computed from multiple noisy data in a binary decision problem based on a maximum a posteriori probability criterion. The performance of detection is assessed by the probability of detection error Per . When the input signal is subthreshold signal, we establish that benefit from noise can be gained for different noises and confirm further that the subthreshold SR exists in nonlinear signal detection. The efficacy of SR is significantly improved and the minimum of Per can dramatically approach to zero as the sample number increases. These results show the robustness of SR in signal detection and extend the applicability of SR in signal processing.

Keywords: Probability of detection error, signal detection, stochastic resonance.

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Authors: Richard A. Wasniowski

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In this paper we propose a framework for multisensor intrusion detection called Fuzzy Agent-Based Intrusion Detection System. A unique feature of this model is that the agent uses data from multiple sensors and the fuzzy logic to process log files. Use of this feature reduces the overhead in a distributed intrusion detection system. We have developed an agent communication architecture that provides a prototype implementation. This paper discusses also the issues of combining intelligent agent technology with the intrusion detection domain.

Keywords: Intrusion detection, fuzzy logic, agents, networksecurity.

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Authors: Goh Yongli, Ooi Shih Yin, Pang Ying Han

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Unintentional falls are rife throughout the ages and have been the common factor of serious or critical injuries especially for the elderly society. Fortunately, owing to the recent rapid advancement in technology, fall detection system is made possible, enabling detection of falling events for the elderly, monitoring the patient and consequently provides emergency support in the event of falling. This paper presents a review of 3 main categories of fall detection techniques, ranging from year 2005 to year 2010. This paper will be focusing on discussing the techniques alongside with summary and conclusion for them.

Keywords: State of the art, fall detection, wearable devices, ambient analyser, motion detection.

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Authors: Omair Ghori, Anton Stadler, Stefan Wilk, Wolfgang Effelsberg

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Fire-related incidents account for extensive loss of life and material damage. Quick and reliable detection of occurring fires has high real world implications. Whereas a major research focus lies on the detection of outdoor fires, indoor camera-based fire detection is still an open issue. Cameras in combination with computer vision helps to detect flames and smoke more quickly than conventional fire detectors. In this work, we present a computer vision-based smoke detection algorithm based on contrast changes and a multi-step classification. This work accelerates computer vision-based fire detection considerably in comparison with classical indoor-fire detection.

Keywords: Contrast analysis, early fire detection, video smoke detection, video surveillance.

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Authors: Youguo Wang, Lenan Wu

Abstract:

We discuss the signal detection through nonlinear threshold systems. The detection performance is assessed by the probability of error Per . We establish that: (1) when the signal is complete suprathreshold, noise always degrades the signal detection both in the single threshold system and in the parallel array of threshold devices. (2) When the signal is a little subthreshold, noise degrades signal detection in the single threshold system. But in the parallel array, noise can improve signal detection, i.e., stochastic resonance (SR) exists in the array. (3) When the signal is predominant subthreshold, noise always can improve signal detection and SR always exists not only in the single threshold system but also in the parallel array. (4) Array can improve signal detection by raising the number of threshold devices. These results extend further the applicability of SR in signal detection.

Keywords: Probability of error, signal detection, stochasticresonance, threshold system.

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Authors: Ji-Hoon Hong, Dong-Hee Kim, Nam-Uk Kim, Tai-Myoung Chung

Abstract:

Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS.

Keywords: Android malware detection, software-defined network.

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Authors: P. Bergl

Abstract:

Numerous divergence measures (spectral distance, cepstral distance, difference of the cepstral coefficients, Kullback-Leibler divergence, distance given by the General Likelihood Ratio, distance defined by the Recursive Bayesian Changepoint Detector and the Mahalanobis measure) are compared in this study. The measures are used for detection of abrupt spectral changes in synthetic AR signals via the sliding window algorithm. Two experiments are performed; the first is focused on detection of single boundary while the second concentrates on detection of a couple of boundaries. Accuracy of detection is judged for each method; the measures are compared according to results of both experiments.

Keywords: Abrupt changes detection, autoregressive model, divergence measure.

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Authors: Emhimed Saffor, Abdelkader Salama

Abstract:

In this paper problem of edge detection in digital images is considered. Edge detection based on morphological operators was applied on two sets (brain & chest) ct images. Three methods of edge detection by applying line morphological filters with multi structures in different directions have been used. 3x3 filter for first method, 5x5 filter for second method, and 7x7 filter for third method. We had applied this algorithm on (13 images) under MATLAB program environment. In order to evaluate the performance of the above mentioned edge detection algorithms, standard deviation (SD) and peak signal to noise ratio (PSNR) were used for justification for all different ct images. The objective method and the comparison of different methods of edge detection,  shows that high values of both standard deviation and PSNR values of edge detection images were obtained. 

Keywords: Medical images, Matlab, Edge detection.

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Authors: Hazem M. El-Bakry

Abstract:

Principal Component Analysis (PCA) has many different important applications especially in pattern detection such as face detection / recognition. Therefore, for real time applications, the response time is required to be as small as possible. In this paper, new implementation of PCA for fast face detection is presented. Such new implementation is designed based on cross correlation in the frequency domain between the input image and eigenvectors (weights). Simulation results show that the proposed implementation of PCA is faster than conventional one.

Keywords: Fast Face Detection, PCA, Cross Correlation, Frequency Domain

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