Spam Detector Research Articles

Security Assessment in Software Defined Networks (SDN): Vulnerabilities, Challenges and Research Prospects

Conventional internet architecture facilitating users for different services and applications but facing number of challenges like network management, QoS management for network virtualization, IP multicasting, deployment of IPV6, crucial security measures, end to end connectivity, inter and inter domain routing. To meet the demand of these services due to rapid growth of technologies and traffic, an emerging network architecture termed as Software Defined Network (SDN) with programmable technology has brought unprecedented management to control networks. Due to separation of data, control and application planes software defined network provides cost effective, openness, centralized automation, programmable features as per users own demands and high resilience to network administrators. OpenFlow evolved as a first standard protocol for software defined network control and data planes communication to meet changing business requirements. Although, Software Defined Network brings enormous advancements in networks to support business applications but it is severely affected with cyber-attacks at data, control and application planes. Middle boxes plays a significant role to manage network effectiveness and provide adequate security control from external and internal security threats but require proper management and configuration otherwise it leads to devastating effect. The main contribution of this paper are: (1) It explores various potential security attacks at SDN layers and inconsistent policies, (2) It provides various security concerns in SDN planes and preventive measures against these prevailing attacks, (3) It discusses security threats challenges and research opportunities in software defined network keeping in view critical security controls like spam detector, IDS/IPS, Firewall and policy management. This article assists researchers to comprehend security concerns and state of the art development and challenges explored by scientific community in SDN.

Implementation of a Spam Detector in Python for Corporate Companies

Many companies, while not categorizing spam as a form of attack, may still consider it a significant threat. When referring to spam, it represents an excessive volume of unwanted and unsolicited electronic mail sent to company email accounts [1]. Although it is often used for marketing purposes, in many cases, it is also employed by malicious actors for detrimental purposes against the company. For instance, spam can be used to propagate viruses or infiltrate corporate computers [1] [2]. Furthermore, spammers may send emails with the aim of directing users to phishing web pages, attempting to obtain confidential information from authorized personnel within the company. In general, spam is, at its simplest, an annoying form of mail, and in its worst cases, it serves as an enabler for spyware, viruses, intrusion, and phishing threats [3].

An efficient spam detector model for accurate categorization of spam tweets using quantum chaotic optimization-based stacked recurrent network

An efficient spam detector model for accurate categorization of spam tweets using quantum chaotic optimization-based stacked recurrent network

Spam/Ham email classification using BERT

Email is a popular method for communicating with each other. However, as sending email is free of charge as long as an email server and a domain name are available, spam mail is becoming a critical problem in the email network. Conventionally, the industry uses spam filters based on rules and Bayesian inference to counteract spam mail, reaching an accuracy of 98.76%, which is far from satisfactory. Hence, to better protect email users from unsolicited messages containing advertisements, sensitive content, phishing content, and viruses, a new approach is proposed, in which email content is filtered by a spam detector using bidirectional encoder representations from transformers (BERT). BERT is a new language representation model published by Google that has achieved great success because of its powerful capabilities in understanding natural language. After the model is trained on a corpus from Kaggle, the spam detector equipped with the BERT model reaches a binary accuracy of 99.40% when classifying spam mail.

Adversarial Spam Detector With Character Similarity Network

More and more social platforms suffer from the spam messages and trouble in detecting them. Lizhi, one of the most famous audio APPs in China, also suffers from the spam messages. The spam detector in Lizhi faces two major challenges: adversarial actions taken by the spammers and lack of labeled data. In this article, we propose a novel adversarial spam detector based on character similarity network for detecting adversarial spam messages in Lizhi, namely Lizhi adversarial spam detector. The character similarity network is designed to solve the adversarial actions before being taken by the spammers, and the character embedding model is proposed to learn the embeddings of all the characters in the corpus. Then, the model generates the sentence embedding of the messages sent by users. At last the classifier will predict whether the message is a spam. Also, the model utilizes active learning to solve the problem of lack of labeled data. Both of offline and online experiments are conducted to confirm the effectiveness of the proposed method.

Content Based E Mail Classification

Electronic Mail (E-mail) has established a significant place in information user’s life. Mails are used as a major and important mode of information sharing because emails are faster and effective way of communication. Email plays its important role of communication in both personal and professional aspects of one’s life. The rapid increase in the number of account holders from last few decades and the increase in the volume of mails have generated various serious issues too. The content base mail classification can be classified into four ways namely Private, Public, Newsletter, and Anonymous. Every user has the right to choose their keyword (a semi-private password). Those contacts who know the user’s keyword will be classified as private contacts and those users who are unknown them classified anonymous contacts. A contact can be classified as public or private, upon verification of an anonymous contact. Any newsletter or group mails are classified into newsletter contacts. It is highly likely that the rests are junk mail or spam. In this project, a spam detector to identify an email as either spam or ham is built using n-gram analysis. The system involves the classification of mails based on user’s contacts. This way any mail from a contact whom the user knows very well is being displayed.

Spam Detector: A Solution for Finding Bogus Opinions and Spammer Classification in E-Commerce

In this paper we propose a system called Spam Detector to find out spam reviews of particular product. This framework uses spam features which helps to review datasets. In proposed system four different categories of features are included such as review behavioral (based on behaviour of review) , user behavioral (based on behaviour of user), review linguistic (based on linguistic pattern of review) and user linguistic (based on linguistic pattern of user). In today’s world, most of people take decision based on information available through social media reviews on a topic or product. Due to this it is possible that anyone can post a review for a particular product which give a chance for spammers to post junk reviews related to particular product and facilities. To find out such spammers and the spam review is becoming important. Some methods exist to cope up with this problem, but these methods hardly detect spam reviews, and are not based on the extracted feature type. Weight of spam features are used to gain better results in the form of different aspects tested on real world review datasets. Here the used dataset is from Amazon websites. The experimental results show that Spam Detector beats the present method.

Practical Web Spam Lifelong Machine Learning System with Automatic Adjustment to Current Lifecycle Phase

Machine learning techniques are a standard approach in spam detection. Their quality depends on the quality of the learning set, and when the set is out of date, the quality of classification falls rapidly. The most popular public web spam dataset that can be used to train a spam detector—WEBSPAM-UK2007—is over ten years old. Therefore, there is a place for a lifelong machine learning system that can replace the detectors based on a static learning set. In this paper, we propose a novel web spam recognition system. The system automatically rebuilds the learning set to avoid classification based on outdated data. Using a built-in automatic selection of the active classifier the system very quickly attains productive accuracy despite a limited learning set. Moreover, the system automatically rebuilds the learning set using external data from spam traps and popular web services. A test on real data from Quora, Reddit, and Stack Overflow proved the high recognition quality. Both the obtained average accuracy and the F-measure were 0.98 and 0.96 for semiautomatic and full–automatic mode, respectively.

Feature Weight Optimization Mechanism for Email Spam Detection based on Two-step Clustering Algorithm and Logistic Regression Method

This research proposed an improved filtering spam technique for suspected emails, messages based on feature weight and the combination of two-step clustering and logistic regression algorithm. Unique, important features are used as the optimum input for a hybrid proposed approach. This study adopted a spam detector model based on distance measure and threshold value. The aim of this model was to study and select distinct features for email filtering using feature weight method as dimension reduction. Two-step clustering algorithm was used to generate a new feature called “Label” to cluster and differentiate the diversity emails and group them based on the inter samples similarity. Thereby the spam filtering process was simplified using the Logistic regression classifier in order to distinguish the hidden patterns of spam and non-spam emails. Experimental design was conducted based on the UCI spam dataset. The outcome of the findings shows that the results of the email filtering are promising compared to other modern spam filtering methods.

On robust image spam filtering via comprehensive visual modeling

The Internet has brought about fundamental changes in the way peoples generate and exchange media information. Over the last decade, unsolicited message images (image spams) have become one of the most serious problems for Internet service providers (ISPs), business firms and general end users. In this paper, we report a novel system called RoBoTs (Robust BoosTrap based spam detector) to support accurate and robust image spam filtering. The system is developed based on multiple visual properties extracted from different levels of granularity, aiming to capture more discriminative contents for effective spam image identification. In addition, a resampling based learning framework is developed to effectively integrate random forest and linear discriminative analysis (LDA) to generate comprehensive signature of spam images. It can facilitate more accurate and robust spam classification process with very limited amount of initial training examples. Using three public available test collections, the proposed system is empirically compared with the state-of-the-art techniques. Our results demonstrate its significantly higher performance from different perspectives.