Visible to the public Biblio

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2021-03-09
Cui, L., Huang, D., Zheng, X..  2020.  Reliability Analysis of Concurrent Data based on Botnet Modeling. 2020 Fourth International Conference on Inventive Systems and Control (ICISC). :825—828.

Reliability analysis of concurrent data based on Botnet modeling is conducted in this paper. At present, the detection methods for botnets are mainly focused on two aspects. The first type requires the monitoring of high-privilege systems, which will bring certain security risks to the terminal. The second type is to identify botnets by identifying spam or spam, which is not targeted. By introducing multi-dimensional permutation entropy, the impact of permutation entropy on the permutation entropy is calculated based on the data communicated between zombies, describing the complexity of the network traffic time series, and the clustering variance method can effectively solve the difficulty of the detection. This paper is organized based on the data complex structure analysis. The experimental results show acceptable performance.

2021-02-03
Adil, M., Khan, R., Ghani, M. A. Nawaz Ul.  2020.  Preventive Techniques of Phishing Attacks in Networks. 2020 3rd International Conference on Advancements in Computational Sciences (ICACS). :1—8.

Internet is the most widely used technology in the current era of information technology and it is embedded in daily life activities. Due to its extensive use in everyday life, it has many applications such as social media (Face book, WhatsApp, messenger etc.,) and other online applications such as online businesses, e-counseling, advertisement on websites, e-banking, e-hunting websites, e-doctor appointment and e-doctor opinion. The above mentioned applications of internet technology makes things very easy and accessible for human being in limited time, however, this technology is vulnerable to various security threats. A vital and severe threat associated with this technology or a particular application is “Phishing attack” which is used by attacker to usurp the network security. Phishing attacks includes fake E-mails, fake websites, fake applications which are used to steal their credentials or usurp their security. In this paper, a detailed overview of various phishing attacks, specifically their background knowledge, and solutions proposed in literature to address these issues using various techniques such as anti-phishing, honey pots and firewalls etc. Moreover, installation of intrusion detection systems (IDS) and intrusion detection and prevention system (IPS) in the networks to allow the authentic traffic in an operational network. In this work, we have conducted end use awareness campaign to educate and train the employs in order to minimize the occurrence probability of these attacks. The result analysis observed for this survey was quite excellent by means of its effectiveness to address the aforementioned issues.

2020-11-09
Ankam, D., Bouguila, N..  2018.  Compositional Data Analysis with PLS-DA and Security Applications. 2018 IEEE International Conference on Information Reuse and Integration (IRI). :338–345.
In Compositional data, the relative proportions of the components contain important relevant information. In such case, Euclidian distance fails to capture variation when considered within data science models and approaches such as partial least squares discriminant analysis (PLS-DA). Indeed, the Euclidean distance assumes implicitly that the data is normally distributed which is not the case of compositional vectors. Aitchison transformation has been considered as a standard in compositional data analysis. In this paper, we consider two other transformation methods, Isometric log ratio (ILR) transformation and data-based power (alpha) transformation, before feeding the data to PLS-DA algorithm for classification [1]. In order to investigate the merits of both methods, we apply them in two challenging information system security applications namely spam filtering and intrusion detection.
2020-11-04
Apruzzese, G., Colajanni, M., Ferretti, L., Marchetti, M..  2019.  Addressing Adversarial Attacks Against Security Systems Based on Machine Learning. 2019 11th International Conference on Cyber Conflict (CyCon). 900:1—18.

Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.

2020-05-18
Lee, Hyun-Young, Kang, Seung-Shik.  2019.  Word Embedding Method of SMS Messages for Spam Message Filtering. 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). :1–4.
SVM has been one of the most popular machine learning method for the binary classification such as sentiment analysis and spam message filtering. We explored a word embedding method for the construction of a feature vector and the deep learning method for the binary classification. CBOW is used as a word embedding technique and feedforward neural network is applied to classify SMS messages into ham or spam. The accuracy of the two classification methods of SVM and neural network are compared for the binary classification. The experimental result shows that the accuracy of deep learning method is better than the conventional machine learning method of SVM-light in the binary classification.
Sel, Slhami, Hanbay, Davut.  2019.  E-Mail Classification Using Natural Language Processing. 2019 27th Signal Processing and Communications Applications Conference (SIU). :1–4.
Thanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.
Thejaswini, S, Indupriya, C.  2019.  Big Data Security Issues and Natural Language Processing. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). :1307–1312.
Whenever we talk about big data, the concern is always about the security of the data. In recent days the most heard about technology is the Natural Language Processing. This new and trending technology helps in solving the ever ending security problems which are not completely solved using big data. Starting with the big data security issues, this paper deals with addressing the topics related to cyber security and information security using the Natural Language Processing technology. Including the well-known cyber-attacks such as phishing identification and spam detection, this paper also addresses issues on information assurance and security such as detection of Advanced Persistent Threat (APT) in DNS and vulnerability analysis. The goal of this paper is to provide the overview of how natural language processing can be used to address cyber security issues.
2020-05-08
Katasev, Alexey S., Emaletdinova, Lilia Yu., Kataseva, Dina V..  2018.  Neural Network Spam Filtering Technology. 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). :1—5.

In this paper we solve the problem of neural network technology development for e-mail messages classification. We analyze basic methods of spam filtering such as a sender IP-address analysis, spam messages repeats detection and the Bayesian filtering according to words. We offer the neural network technology for solving this problem because the neural networks are universal approximators and effective in addressing the problems of classification. Also, we offer the scheme of this technology for e-mail messages “spam”/“not spam” classification. The creation of effective neural network model of spam filtering is performed within the databases knowledge discovery technology. For this training set is formed, the neural network model is trained, its value and classifying ability are estimated. The experimental studies have shown that a developed artificial neural network model is adequate and it can be effectively used for the e-mail messages classification. Thus, in this paper we have shown the possibility of the effective neural network model use for the e-mail messages filtration and have shown a scheme of artificial neural network model use as a part of the e-mail spam filtering intellectual system.

2020-04-13
Lange, Thomas, Kettani, Houssain.  2019.  On Security Threats of Botnets to Cyber Systems. 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). :176–183.
As the dynamics of cyber warfare continue to change, it is very important to be aware of the issues currently confronting cyberspace. One threat which continues to grow in the danger it poses to cyber security are botnets. Botnets can launch massive Distributed Denial of Service (DDoS) attacks against internet connected hosts anonymously, undertake intricate spam campaigns, launch mass financial fraud campaigns, and even manipulate public opinion via social media bots. The network topology and technology undergirding each botnet varies greatly, as do the motivations commonly behind such networks. Furthermore, as botnets have continued to evolve, many newer ones demonstrate increased levels of anonymity and sophistication, making it more difficult to effectively counter them. Increases in the production of vulnerable Internet of Things (IoT) devices has made it easier for malicious actors to quickly assemble sizable botnets. Because of this, the steps necessary to stop botnets also vary, and in some cases, it may be extremely difficult to effectively defeat a fully functional and sophisticated botnet. While in some cases, the infrastructure supporting the botnet can be targeted and remotely disabled, other cases require the physical assistance of law enforcement to shut down the botnet. In the latter case, it is often a significant challenge to cheaply end a botnet. On the other hand, there are many steps and mitigations that can be taken by end-users to prevent their own devices from becoming part of a botnet. Many of these solutions involve implementing basic cybersecurity practices like installing firewalls and changing default passwords. More sophisticated botnets may require similarly sophisticated intrusion detection systems, to detect and remove malicious infections. Much research has gone into such systems and in recent years many researchers have begun to implement machine learning techniques to defeat botnets. This paper is intended present a review on botnet evolution, trends and mitigations, and offer related examples and research to provide the reader with quick access to a broad understanding of the issues at hand.
2020-04-03
Sattar, Naw Safrin, Arifuzzaman, Shaikh, Zibran, Minhaz F., Sakib, Md Mohiuddin.  2019.  An Ensemble Approach for Suspicious Traffic Detection from High Recall Network Alerts. {2019 IEEE International Conference on Big Data (Big Data. :4299—4308}}@inproceedings{wu_ensemble_2019.
Web services from large-scale systems are prevalent all over the world. However, these systems are naturally vulnerable and incline to be intruded by adversaries for illegal benefits. To detect anomalous events, previous works focus on inspecting raw system logs by identifying the outliers in workflows or relying on machine learning methods. Though those works successfully identify the anomalies, their models use large training set and process whole system logs. To reduce the quantity of logs that need to be processed, high recall suspicious network alert systems can be applied to preprocess system logs. Only the logs that trigger alerts are retrieved for further usage. Due to the universally usage of network traffic alerts among Security Operations Center, anomalies detection problems could be transformed to classify truly suspicious network traffic alerts from false alerts.In this work, we propose an ensemble model to distinguish truly suspicious alerts from false alerts. Our model consists of two sub-models with different feature extraction strategies to ensure the diversity and generalization. We use decision tree based boosters and deep neural networks to build ensemble models for classification. Finally, we evaluate our approach on suspicious network alerts dataset provided by 2019 IEEE BigData Cup: Suspicious Network Event Recognition. Under the metric of AUC scores, our model achieves 0.9068 on the whole testing set.
2020-02-10
Yao, Chuhao, Wang, Jiahong, Kodama, Eiichiro.  2019.  A Spam Review Detection Method by Verifying Consistency among Multiple Review Sites. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :2825–2830.

In recent years, websites that incorporate user reviews, such as Amazon, IMDB and YELP, have become exceedingly popular. As an important factor affecting users purchasing behavior, review information has been becoming increasingly important, and accordingly, the reliability of review information becomes an important issue. This paper proposes a method to more accurately detect the appearance period of spam reviews and to identify the spam reviews by verifying the consistency of review information among multiple review sites. Evaluation experiments were conducted to show the accuracy of the detection results, and compared the newly proposed method with our previously proposed method.

Suryawanshi, Shubhangi, Goswami, Anurag, Patil, Pramod.  2019.  Email Spam Detection : An Empirical Comparative Study of Different ML and Ensemble Classifiers. 2019 IEEE 9th International Conference on Advanced Computing (IACC). :69–74.

Recent Development in Hardware and Software Technology for the communication email is preferred. But due to the unbidden emails, it affects communication. There is a need for detection and classification of spam email. In this present research email spam detection and classification, models are built. We have used different Machine learning classifiers like Naive Bayes, SVM, KNN, Bagging and Boosting (Adaboost), and Ensemble Classifiers with a voting mechanism. Evaluation and testing of classifiers is performed on email spam dataset from UCI Machine learning repository and Kaggle website. Different accuracy measures like Accuracy Score, F measure, Recall, Precision, Support and ROC are used. The preliminary result shows that Ensemble Classifier with a voting mechanism is the best to be used. It gives the minimum false positive rate and high accuracy.

Shyry, S. Prayla, Charan K, Venkat Sai, Kumar, V. Sudheer.  2019.  Spam Mail Detection and Prevention at Server Side. 2019 Innovations in Power and Advanced Computing Technologies (i-PACT). 1:1–6.

Spam is a genuine and irritating issue for quite a longtime. Despite the fact that a lot of arrangements have been advanced, there still remains a considerable measure to be advanced in separating spam messages all the more proficiently. These days a noteworthy issue in spam separating also as content characterization in common dialect handling is the colossal size of vector space because of the various element terms, which is normally the reason for broad figuring and moderate order. Extracting semantic implications from the substance of writings and utilizing these as highlight terms to develop the vector space, rather than utilizing words as highlight terms in convention ways, could decrease the component of vectors viably and advance the characterization in the meantime. In spite of the fact that there are a wide range of techniques to square spam messages, a large portion of program designers just mean to square spam messages from being conveyed to their customers. In this paper, we present an effective way to deal with keep spam messages from being exchanged.In this work, a Collaborative filtering approach with semantics-based text classification technology was proposed and the related feature terms were selected from the semantic meanings of the text content.

Shahariar, G. M., Biswas, Swapnil, Omar, Faiza, Shah, Faisal Muhammad, Binte Hassan, Samiha.  2019.  Spam Review Detection Using Deep Learning. 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). :0027–0033.

A robust and reliable system of detecting spam reviews is a crying need in todays world in order to purchase products without being cheated from online sites. In many online sites, there are options for posting reviews, and thus creating scopes for fake paid reviews or untruthful reviews. These concocted reviews can mislead the general public and put them in a perplexity whether to believe the review or not. Prominent machine learning techniques have been introduced to solve the problem of spam review detection. The majority of current research has concentrated on supervised learning methods, which require labeled data - an inadequacy when it comes to online review. Our focus in this article is to detect any deceptive text reviews. In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and a variant of Recurrent Neural Network (RNN) that is Long Short-Term Memory (LSTM). We have also applied some traditional machine learning classifiers such as Nave Bayes (NB), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) to detect spam reviews and finally, we have shown the performance comparison for both traditional and deep learning classifiers.

Li, Meng, Wu, Bin, Wang, Yaning.  2019.  Comment Spam Detection via Effective Features Combination. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–6.

Comment spam is one of the great challenges faced by forum administrators. Detecting and blocking comment spam can relieve the load on servers, improve user experience and purify the network conditions. This paper focuses on the detection of comment spam. The behaviors of spammer and the content of spam were analyzed. According to analysis results, two types of effective features are extracted which can make a better description of spammer characteristics. Additionally, a gradient boosting tree algorithm was used to construct the comment spam detector based on the extracted features. Our proposed method is examined on a blog spam dataset which was published by previous research, and the result illustrates that our method performs better than the previous method on detection accuracy. Moreover, the CPU time is recorded to demonstrate that the time spent on both training and testing maintains a small value.

Lekha, J., Maheshwaran, J, Tharani, K, Ram, Prathap K, Surya, Murthy K, Manikandan, A.  2019.  Efficient Detection of Spam Messages Using OBF and CBF Blocking Techniques. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). :1175–1179.

Emails are the fundamental unit of web applications. There is an exponential growth in sending and receiving emails online. However, spam mail has turned into an intense issue in email correspondence condition. There are number of substance based channel systems accessible to be specific content based filter(CBF), picture based sifting and many other systems to channel spam messages. The existing technological solution consists of a combination of porter stemer algorithm(PSA) and k means clustering which is adaptive in nature. These procedures are more expensive in regard of the calculation and system assets as they required the examination of entire spam message and calculation of the entire substance of the server. These are the channels must additionally not powerful in nature life on the grounds that the idea of spam block mail and spamming changes much of the time. We propose a starting point based spam mail-sifting system benefit, which works considering top head notcher data of the mail message paying little respect to the body substance of the mail. It streamlines the system and server execution by increasing the precision, recall and accuracy than the existing methods. To design an effective and efficient of autonomous and efficient spam detection system to improve network performance from unknown privileged user attacks.

Krause, Tim, Uetz, Rafael, Kretschmann, Tim.  2019.  Recognizing Email Spam from Meta Data Only. 2019 IEEE Conference on Communications and Network Security (CNS). :178–186.

We propose a new spam detection approach based solely on meta data features gained from email headers. The approach achieves above 99 % classification accuracy on the CSDMC2010 dataset, which matches or surpasses state-of-the-art spam classifiers. We utilize a static set of engineered features, supplemented with automatically extracted features. The approach is just as effective for spam detection in end-to-end encryption, as our feature set remains unchanged for encrypted emails. In contrast to most established spam detectors, we disregard the email body completely and can therefore deliver very high classification speeds, as computationally expensive text preprocessing is not necessary.

Ishtiaq, Asra, Islam, Muhammad Arshad, Azhar Iqbal, Muhammad, Aleem, Muhammad, Ahmed, Usman.  2019.  Graph Centrality Based Spam SMS Detection. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST). :629–633.

Short messages usage has been tremendously increased such as SMS, tweets and status updates. Due to its popularity and ease of use, many companies use it for advertisement purpose. Hackers also use SMS to defraud users and steal personal information. In this paper, the use of Graphs centrality metrics is proposed for spam SMS detection. The graph centrality measures: degree, closeness, and eccentricity are used for classification of SMS. Graphs for each class are created using labeled SMS and then unlabeled SMS is classified using the centrality scores of the token available in the unclassified SMS. Our results show that highest precision and recall is achieved by using degree centrality. Degree centrality achieved the highest precision i.e. 0.81 and recall i.e., 0.76 for spam messages.

Eshmawi, Ala', Nair, Suku.  2019.  The Roving Proxy Framewrok for SMS Spam and Phishing Detection. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–6.

This paper presents the details of the roving proxy framework for SMS spam and SMS phishing (SMishing) detection. The framework aims to protect organizations and enterprises from the danger of SMishing attacks. Feasibility and functionality studies of the framework are presented along with an update process study to define the minimum requirements for the system to adapt with the latest spam and SMishing trends.

Elakkiya, E, Selvakumar, S.  2019.  Initial Weights Optimization Using Enhanced Step Size Firefly Algorithm for Feed Forward Neural Network Applied to Spam Detection. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). :942–946.

Spams are unsolicited and unnecessary messages which may contain harmful codes or links for activation of malicious viruses and spywares. Increasing popularity of social networks attracts the spammers to perform malicious activities in social networks. So an efficient spam detection method is necessary for social networks. In this paper, feed forward neural network with back propagation based spam detection model is proposed. The quality of the learning process is improved by tuning initial weights of feed forward neural network using proposed enhanced step size firefly algorithm which reduces the time for finding optimal weights during the learning process. The model is applied for twitter dataset and the experimental results show that, the proposed model performs well in terms of accuracy and detection rate and has lower false positive rate. 

Dan, Kenya, Kitagawa, Naoya, Sakuraba, Shuji, Yamai, Nariyoshi.  2019.  Spam Domain Detection Method Using Active DNS Data and E-Mail Reception Log. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:896–899.

E-mail is widespread and an essential communication technology in modern times. Since e-mail has problems with spam mails and spoofed e-mails, countermeasures are required. Although SPF, DKIM and DMARC have been proposed as sender domain authentication, these mechanisms cannot detect non-spoofing spam mails. To overcome this issue, this paper proposes a method to detect spam domains by supervised learning with features extracted from e-mail reception log and active DNS data, such as the result of Sender Authentication, the Sender IP address, the number of each DNS record, and so on. As a result of the experiment, our method can detect spam domains with 88.09% accuracy and 97.11% precision. We confirmed that our method can detect spam domains with detection accuracy 19.40% higher than the previous study by utilizing not only active DNS data but also e-mail reception log in combination.

2019-12-16
Malviya, Vikas, Rai, Sawan, Gupta, Atul.  2018.  Development of a Plugin Based Extensible Feature Extraction Framework. Proceedings of the 33rd Annual ACM Symposium on Applied Computing. :1840–1847.

An important ingredient for a successful recipe for solving machine learning problems is the availability of a suitable dataset. However, such a dataset may have to be extracted from a large unstructured and semi-structured data like programming code, scripts, and text. In this work, we propose a plug-in based, extensible feature extraction framework for which we have prototyped as a tool. The proposed framework is demonstrated by extracting features from two different sources of semi-structured and unstructured data. The semi-structured data comprised of web page and script based data whereas the other data was taken from email data for spam filtering. The usefulness of the tool was also assessed on the aspect of ease of programming.

2019-11-26
Baykara, Muhammet, Gürel, Zahit Ziya.  2018.  Detection of Phishing Attacks. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1-5.

Phishing is a form of cybercrime where an attacker imitates a real person / institution by promoting them as an official person or entity through e-mail or other communication mediums. In this type of cyber attack, the attacker sends malicious links or attachments through phishing e-mails that can perform various functions, including capturing the login credentials or account information of the victim. These e-mails harm victims because of money loss and identity theft. In this study, a software called "Anti Phishing Simulator'' was developed, giving information about the detection problem of phishing and how to detect phishing emails. With this software, phishing and spam mails are detected by examining mail contents. Classification of spam words added to the database by Bayesian algorithm is provided.

2019-08-05
Liu, Jienan, Rahbarinia, Babak, Perdisci, Roberto, Du, Haitao, Su, Li.  2018.  Augmenting Telephone Spam Blacklists by Mining Large CDR Datasets. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :273–284.

Telephone spam has become an increasingly prevalent problem in many countries all over the world. For example, the US Federal Trade Commission's (FTC) National Do Not Call Registry's number of cumulative complaints of spam/scam calls reached 30.9 million submissions in 2016. Naturally, telephone carriers can play an important role in the fight against spam. However, due to the extremely large volume of calls that transit across large carrier networks, it is challenging to mine their vast amounts of call detail records (CDRs) to accurately detect and block spam phone calls. This is because CDRs only contain high-level metadata (e.g., source and destination numbers, call start time, call duration, etc.) related to each phone calls. In addition, ground truth about both benign and spam-related phone numbers is often very scarce (only a tiny fraction of all phone numbers can be labeled). More importantly, telephone carriers are extremely sensitive to false positives, as they need to avoid blocking any non-spam calls, making the detection of spam-related numbers even more challenging. In this paper, we present a novel detection system that aims to discover telephone numbers involved in spam campaigns. Given a small seed of known spam phone numbers, our system uses a combination of unsupervised and supervised machine learning methods to mine new, previously unknown spam numbers from large datasets of call detail records (CDRs). Our objective is not to detect all possible spam phone calls crossing a carrier's network, but rather to expand the list of known spam numbers while aiming for zero false positives, so that the newly discovered numbers may be added to a phone blacklist, for example. To evaluate our system, we have conducted experiments over a large dataset of real-world CDRs provided by a leading telephony provider in China, while tuning the system to produce no false positives. The experimental results show that our system is able to greatly expand on the initial seed of known spam numbers by up to about 250%.

2019-02-25
Liu, Ninghao, Yang, Hongxia, Hu, Xia.  2018.  Adversarial Detection with Model Interpretation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :1803–1811.
Machine learning (ML) systems have been increasingly applied in web security applications such as spammer detection, malware detection and fraud detection. These applications have an intrinsic adversarial nature where intelligent attackers can adaptively change their behaviors to avoid being detected by the deployed detectors. Existing efforts against adversaries are usually limited by the type of applied ML models or the specific applications such as image classification. Additionally, the working mechanisms of ML models usually cannot be well understood by users, which in turn impede them from understanding the vulnerabilities of models nor improving their robustness. To bridge the gap, in this paper, we propose to investigate whether model interpretation could potentially help adversarial detection. Specifically, we develop a novel adversary-resistant detection framework by utilizing the interpretation of ML models. The interpretation process explains the mechanism of how the target ML model makes prediction for a given instance, thus providing more insights for crafting adversarial samples. The robustness of detectors is then improved through adversarial training with the adversarial samples. A data-driven method is also developed to empirically estimate costs of adversaries in feature manipulation. Our approach is model-agnostic and can be applied to various types of classification models. Our experimental results on two real-world datasets demonstrate the effectiveness of interpretation-based attacks and how estimated feature manipulation cost would affect the behavior of adversaries.