Hybrid Spam Filtering for Mobile Communication

Spam messages are an increasing threat to mobile communication. Several mitigation techniques have been proposed, including white and black listing, challenge-response and content-based filtering. However, none are perfect and it makes sense to use a combination rather than just one. We propose an anti-spam framework based on the hybrid of content-based filtering and challenge-response. There is the trade-offs between accuracy of anti-spam classifiers and the communication overhead. Experimental results show how, depending on the proportion of spam messages, different filtering %%@ parameters should be set.Comment: 6 pages, 5 figures, 1 tabl

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

SMS, one of the most popular and fast-growing GSM value-added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F-measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine-tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state-of-the-art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F-measure.publishedVersio

Antyscam – practical web spam classifier

To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.

SMS Spam Filtering: Methods and Data

Mobile or SMS spam is a real and growing problem primarily due to the availability of very cheap bulk pre-pay SMS packages and the fact that SMS engenders higher response rates as it is a trusted and personal service. SMS spam filtering is a relatively new task which inherits many issues and solu- tions from email spam filtering. However it poses its own specific challenges. This paper motivates work on filtering SMS spam and reviews recent devel- opments in SMS spam filtering. The paper also discusses the issues with data collection and availability for furthering research in this area, analyses a large corpus of SMS spam, and provides some initial benchmark results

SUBSCRIBERS ATTITUDE TOWARD UNSOLICITED TEXT MESSAGES (UTM) AMONG NIGERIAN TELECOMMUNICATION FIRMS

As more and more telecom firms turn to unsolicited text messages (UTM) to increase profits and capture new customers, the emphasis on UTM means more opportunities for telecom firms to disseminate info at the most reduced cost and to targets consumers on one-to-one basis. This paper investigates the consumers’ attitude towards unsolicited text messages (SMS spamming). The research is based on a wide and broad literature review of the latest trends in the Nigeria telecom industry. The study evaluates the effect of three components of attitude (cognitive, affective and conative) on the consumers’ preferences and loyalty to telephone services. To achieve the spelt objectives, the study utilizes survey design; and data was collected though a self-administered questionnaire from a number of 302 respondents who were subscribers of four major telecom operators (MTN, GLO, AIRTEL and ETISALAT) in Ogbomoso Metropolis, Oyo State Nigeria. Statistical technique software SPSS was employed to aid the data analysis. Having analyzed the data, the study found out that unsolicited text messages (UTM) impact on subscribers’ cognitive attitude for telecom services among telecom firms in Nigeria. It was also discovered that UTM have affective action (negative effect) on consumers’ preference for telephone services. The work among other things, recommends that mobile firms should be cautious about the information content of their advertising message. This is aimed at producing advertising message that contains sufficient, pleasant and valuable information needed to positively engage the cognition of the consumer. The work in addition to that also advised that telecom firms should reexamine the usefulness of UTM as a standalone mode because of the observed inherent limitations with regards to emotional appeal and shortage in information capacity, and they should possibly switch to a more effective mobile application.  Article visualizations

Preventing SMishing Attack Using Fake User Information Based on Common Behavior of Phishing Websites

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 조유근.스미싱 공격은 모바일 피싱 공격의 한 유형으로 스마트폰 사용자의 개인정보를 탈취하거나 소액결제 등으로 금전적 피해를 입히는 것을 목적으로 한다. URL이 포함된 메시지로 사용자를 속여 악의적인 웹사이트에 접속을 유도하는 방법으로 최근 피해 사례가 크게 증가하고 있다. 본 논문에서는 스마트폰의 스미싱 공격 방지 기법으로 몇 가지 기법을 합쳐 Phishing URL Detector를 제안한다. 첫 단계에서는 접속할 URL을 분석하여 알려진 피싱 URL 패턴이 포함된 경우 접속을 차단한다. 두 번째 단계로 모바일 웹 브라우저의 북마크와 접속 이력을 기반으로 한 whitelist를 검색해 접속할 URL이 포함돼 있으면 안전한 것으로 간주하고 접속을 허용한다. 세 번째 단계에서는 blacklist를 검색하여 접속할 URL이 포함되어 있으면 위험한 URL로 간주하고 접속을 차단한다. 마지막은 URL의 안전성을 판단할 수 없는 단계이므로 우선 URL에 접속한 뒤 허위 사용자 정보를 입력한 뒤 웹사이트의 동작을 분석하고 피싱 웹사이트인지 판별한다. 이 기법은 입력하는 정보의 유효성을 실시간으로 확인할 수 없고, 모두 입력 되었는지 확인만 가능하다는 피싱 웹사이트의 특징을 이용한다. 따라서, 허위 사용자 정보에도 유효성 검사 후 재입력 요구 없이 다른 동작이 진행된다면 피싱 웹사이트로 판별할 수 있다. URL에 접속해 보기 전에는 피싱 URL 판별이 어렵다는 기존의 문제점을 해결할 수 있는 본 기법은 스미싱 공격을 효과적으로 방지할 수 있는 첫 번째 방어막이 될 수 있다.요약 목차 그림 목차 표 목차 제 1 장 서론 제 2 장 스미싱 공격 2.1 모바일 피싱 공격 2.2 스미싱 공격 2.3 관련 연구 제 3 장 스미싱 공격 방지 기법 3.1 스미싱 공격 특성 분석 3.1.1 스미싱 공격 흐름 분석 3.1.2 스미싱 공격 특징 분석 3.1.3 스미싱 SMS 특징 분석 3.2 Phishing URL Detector 제안 3.2.1 피싱 웹사이트 공통 동작 패턴 분석 3.2.2 Phishing URL Detector 제 4 장 실험 및 구현 4.1 피싱 웹사이트 공통 동작 패턴 확인 실험 4.2 Phishing URL Detector 구현 제 5 장 결론 참고문헌 AbstractMaste

LC an effective classification based association rule mining algorithm

Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider

Continuous User Authentication Using Multi-Modal Biometrics

It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security. This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data

既知用語辞書を用いた情報フィルタリングによる研究シーズ用語の抽出方法

筑波大学 (University of Tsukuba)201