The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject - appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection.

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A Comprehensive Survey for Intelligent Spam Email Detection

Machine-learning based network intrusion detection systems (ML-NIDS) are increasingly popular in the fight against network attacks. In particular, promising detection results have been demonstrated in conjunction with Software-Defined Networks (SDN), in which the logically centralized control plane provides access to data from across the network. However, research into adversarial attacks against machine learning classifiers has highlighted vulnerabilities in a number of fields. These vulnerabilities raise concerns about the implementation of similar classifiers in anomaly-based NIDSs within SDNs. In this work, we investigate the viability of adversarial attacks against classifiers in this field. We implement an anomaly-based NIDS, Neptune, as a target platform that utilises a number of different machine learning classifiers and traffic flow features. We develop an adversarial test tool, Hydra, to evaluate the impact of adversarial evasion classifier attacks against Neptune with the goal of lowering the detection rate of malicious network traffic. The results demonstrate that with the perturbation of a few features, the detection accuracy of a specific SYN flood Distributed Denial of Service (DDoS) attack by Neptune decreases from 100% to 0% across a number of classifiers. Based on these results, recommendations are made as to how to increase the robustness of classifiers against the demonstrated attacks.

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Investigating Adversarial Attacks against Network Intrusion Detection Systems in SDNs

Email is the most used source of official communication method for business purposes The usage of the email continuously increases despite of other methods of communications Automated management of emails is important in the today’s context as the volume of emails grows day by day Out of the total emails, more than 55 percent is identified as spam This shows that these spams consume email user time and resources generating no useful output The spammers use developed and creative methods in order to fulfil their criminal activities using spam emails, Therefore, it is vital to understand different spam email classification techniques and their mechanism This paper mainly focuses on the spam classification approached using machine learning algorithms Furthermore, this study provides a comprehensive analysis and review of research done on different machine learning techniques and email features used in different Machine Learning approaches Also provides future research directions and the challenges in the spam classification field that can be useful for future researchers

A Comprehensive Review on Email Spam Classification using Machine Learning Algorithms

Email spam filters based on machine learning techniques are widely deployed in today's organizations. As our society relies more on artificial intelligence (AI), the security of AI, especially the machine learning algorithms, becomes increasingly important and remains largely untested. Adversarial machine learning, on the other hand, attempts to defeat machine learning models through malicious input. In this paper, we experiment how adversarial scenario may impact the security of machine learning based mechanisms such as email spam filters. Using natural language processing (NLP) and Baysian model as an example, we developed and tested three invasive techniques, i.e., synonym replacement, ham word injection and spam word spacing. Our adversarial examples and results suggest that these techniques are effective in fooling the machine learning models. The study calls for more research on understanding and safeguarding machine learning based security mechanisms in the presence of adversaries.

Adversarial machine learning for spam filters

Aspects of the subject disclosure may include, for example, predicting opportunities for a mobile communication device to access a network via available pathways during a first time period according to a history of network connectivity of the mobile communication device, selecting a first pathway of the available pathways according to the opportunities that are predicted for the mobile communication device to access the network via the available pathways during the first time period, and directing transmission of first data to the mobile communication device via the first pathway during the first time period, wherein a presence of a second data at the mobile communication device enables an application to access the first data at the mobile communication device. Other embodiments are disclosed.

Method and apparatus for distributing content via diverse networks

Spam Filtering is an adversary application in which data can be purposely employed by humans to attenuate their operation. Statistical spam filters are manifest to be vulnerable to adversarial attacks. To evaluate security issues related to spam filtering numerous machine learning systems are used. For adversary applications some Pattern classification systems are ordinarily used, since these systems are based on classical theory and design approaches do not take into account adversarial settings. Pattern classification system display vulnerabilities (i.e. a weakness that grants an attacker to reduce assurance on system's information) to several potential attacks, allowing adversaries to attenuate their effectiveness. In this paper, security evaluation of spam email using pattern classifier during an attack is addressed which degrade the performance of the system. Additionally a model of the adversary is used that allows defining spam attack scenario.

Pattern classification under attack on spam filtering

Climate observing has critical influence on mankind. Gathering of the various data of fleeting elements of the climate variations is extremely noteworthy. The essential point of this paper is to build up an installed framework to outline a climate observing framework which empowers the checking of climate parameters. This type of frame work includes various sensors involving temperature, Humidity, wind speed, wind bearing information can be signed into cloud so that any one (validated individual) from wherever can watch the particular information. If there should be an occurrence of any catastrophes like flame, substantial downpour, overwhelming wind, temperature or moistness might be wild, in these cases the moment data can be passed on all through the world utilizing cloud to the verified people, regardless of the fact that his equipment is wrecked in crisis.

https://www.ijcaonline.org/archives/volume153/number3/patil-2016-ijca-911980.pdf

Study of Recent Literature on Weather Monitoring Systems

Demand on video traffic over mobile network is increasing in year on year basis, wireless link capacity cannot satisfying the traffic requirement The difference in the traffic demand and the link capacity with varying link conditions results in poor quality of videos such as intermittent disruptions and long buffering time over mobile networks Due to such environments, we propose a new mobile video streaming and sharing system, which efficiently stores videos on the cloud (VC) and construct agent (subVC) for each mobile user at client side to offer “non-terminating” video streaming And also proposed system will monitor the social network communication among mobile users and their private agent prefetch videos in advance according to social activities The security of videos and private data of user is provided by using AES algorithm and secure key exchange by using Diffie Hellman Key Exchange algorithm

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Providing Security to Mobile Video Streaming and Video Sharing in the Cloud

The systems which can be used for pattern classification are used in adversarial application, for example spam filtering, network intrusion detection system, biometric authentication. This adversarial scenario’s exploitation may sometimes affect their performance and limit their practical utility. In case of pattern classification conception and contrive methods to adversarial environment is a novel and relevant research direction, which has not yet pursued in a systematic way. To address one main open issue: evaluating at contrive phase the security of pattern classifiers (for example the performance degradation under potential attacks which incurs during the operation). To propose a framework for evaluation of classifier security and also this framework can be applied to different classifiers on one of the application from the spam filtering,biometric authentication andnetwork intrusion detection.

/pdf/a-review-on-security-evaluation-for-pattern-classifier-rme16flajl.pdf

A Review on Security Evaluation for Pattern Classifier against Attack

The Pattern classification system classifies the pattern into feature space within a boundary. In case adversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS), Biometric Authentication, the pattern classification systems are used. Spam filtering is an adversary application in which data can be employed by humans to attenuate perspective operations. To appraise the security issue related Spam Filtering voluminous machine learning systems. We presented a framework for the experimental evaluation of the classifier security in an adversarial environments, that combines and constructs on the arms race and security by design, Adversary modelling and Data distribution under attack. Furthermore, we presented a SVM, LR and MILR classifier for classification to categorize email as legitimate (ham) or spam emails on the basis of thee text samples.

/pdf/spam-filtering-security-evaluation-framework-using-svm-lr-2lzubj0euz.pdf

Madhuri Patil

Papers

Pattern classification under attack on spam filtering

Study of Recent Literature on Weather Monitoring Systems

Providing Security to Mobile Video Streaming and Video Sharing in the Cloud

A Review on Security Evaluation for Pattern Classifier against Attack

Spam Filtering Security Evaluation Framework Using SVM, LR and MILR