Preemption of State Spam Laws by the Federal Can-Spam Act

Unsolicited bulk commercial email is an increasing problem, and though many states have passed laws aimed at curbing its use and abuse, for several years the federal government took no action. In 2003 that changed when Congress passed the CAN-SPAM Act. Though the law contains many different restrictions on spam messages, including some restriction of nearly every type that states had adopted, the Act was widely criticized as weak. Many of the CAN-SPAM Act\u27s provisions are weaker than corresponding provisions of state law, and the Act preempts most state spam laws that would go farther, including two state laws that would have banned all spam. Despite these weaknesses, this Comment argues that when properly interpreted the CAN-SPAM Act leaves key state law provisions in force, and accordingly is stronger than many spam opponents first thought. First, the law explicitly preserves state laws to the extent that they prohibit falsity or deception in any portion of a commercial electronic mail message or information attached thereto. Though Congress was primarily concerned with saving state consumer protection laws, this language can be applied much more broadly. Second, the law is silent on the question of state law enforcement methods. State enforcement can be, and frequently is, substantially stronger than federal enforcement, which is largely limited to actions by the federal government, internet service providers, and state agencies. The Comment concludes by arguing that this narrow interpretation of its preemption clause is most consistent with the CAN-SPAM Act\u27s twin policy goals. By limiting the substantive provisions states may adopt, the Act prevents states from enacting inconsistent laws and enforces a uniform national spam policy. At the same time, narrowly interpreting the preemption clause permits states to experiment within the limits of that policy, in hopes of finding the most effective set of spam regulations

Finding and Analyzing Evil Cities on the Internet

IP Geolocation is used to determine the geographical location of Internet users based on their IP addresses. When it comes to security, most of the traditional geolocation analysis is performed at country level. Since countries usually have many cities/towns of different sizes, it is expected that they behave differently when performing malicious activities. Therefore, in this paper we refine geolocation analysis to the city level. The idea is to find the most dangerous cities on the Internet and observe how they behave. This information can then be used by security analysts to improve their methods and tools. To perform this analysis, we have obtained and evaluated data from a real-world honeypot network of 125 hosts and from production e-mail servers

Support Vector Machine Algorithm for SMS Spam Classification in The Telecommunication Industry

In recent years, we have withnessed a dramatic increment volume in the number of mobile users grows in telecommunication industry. However, this leads to drastic increase to the number of spam SMS messages. Short Message Service (SMS) is considered one of the widely used communication in telecommunication service. In reality, most of the users ignore the spam because of the lower rate of SMS and limited amount of spam classification tools. In this paper, we propose a Support Vector Machine (SVM) algorithm for SMS Spam Classification. Support Vector Machine is considered as the one of the most effective for data mining techniques. The propose algorithm have been evaluated using public dataset from UCI machine learning repository. The performance achieved is compared with other three data mining techniques such as Naïve Bayes, Multinominal Naïve Bayes and K-Nearest Neighbor with the different number of K= 1,3 and 5. Based on the measuring factors like higher accuracy, less processing time, highest kappa statistics, low error and the lowest false positive instance, it’s been identified that Support Vector Machines (SVM) outperforms better than other classifiers and it is the most accurate classifier to detect and label the spam messages with an average an accuracy is 98.9%. Comparing both the error parameter overall, the highest error has been found on the algorithm KNN with K=3 and K=5. Whereas the model with less error is SVM followed by Multinominal Naïve Bayes. Therefore, this propose method can be used as a best baseline for further comparison based on SMS spam classification

BlogForever D2.4: Weblog spider prototype and associated methodology

The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype

Computing with Granular Words

Computational linguistics is a sub-field of artificial intelligence; it is an interdisciplinary field dealing with statistical and/or rule-based modeling of natural language from a computational perspective. Traditionally, fuzzy logic is used to deal with fuzziness among single linguistic terms in documents. However, linguistic terms may be related to other types of uncertainty. For instance, different users search ‘cheap hotel’ in a search engine, they may need distinct pieces of relevant hidden information such as shopping, transportation, weather, etc. Therefore, this research work focuses on studying granular words and developing new algorithms to process them to deal with uncertainty globally. To precisely describe the granular words, a new structure called Granular Information Hyper Tree (GIHT) is constructed. Furthermore, several technologies are developed to cooperate with computing with granular words in spam filtering and query recommendation. Based on simulation results, the GIHT-Bayesian algorithm can get more accurate spam filtering rate than conventional method Naive Bayesian and SVM; computing with granular word also generates better recommendation results based on users’ assessment when applied it to search engine