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Email Threat Detection Using Distinct Neural Network Approaches

Esteban Castillo, Sreekar Dhaduvai, Peng Liu, Kartik-Singh Thakur, Adam Dalton, Tomek Strzalkowski
2020 International Conference on Language Resources and Evaluation  
Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email.  ...  This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools.  ...  Overall, our results surpass baseline techniques showing the relevance of the neural networks approach combined with word embeddings for detecting distinctive elements on email exchanges.  ... 
dblp:conf/lrec/CastilloDLTDS20 fatcat:pofegjtnifhurbgztjpoxibmuy

An Collaborative and Early Detection of Email Spam Using Multitask Learning

Hariharan N, Kamaraj G, Ramanuja Babu R D
2021 International Journal of New Technology and Research  
solution to filter possible spam e-mails.In this paper a hybrid solution which uses machine learning algorithms like Deep Neural Network, Convolution Neural Network are used to produce an improved result  ...  spamming (direct spamming) techniques to a more scalable and indirect approach of botnets for distributing Email spam message is the major reason for it.The aim of this research is to find an effective  ...  DISCUSSIONS The major finding is spam email detection using machine learning techniques like Deep Neural Network, Convolution Neural Network.Machine Learning techniques are used since number of available  ... 
doi:10.31871/ijntr.7.4.11 fatcat:xr3ms4rjfbh6jkeeqckgv3keqq

The Panacea Threat Intelligence and Active Defense Platform [article]

Adam Dalton, Ehsan Aghaei, Ehab Al-Shaer, Archna Bhatia, Esteban Castillo, Zhuo Cheng, Sreekar Dhaduvai, Qi Duan, Md Mazharul Islam, Younes Karimi, Amir Masoumzadeh, Brodie Mather (+4 others)
2020 arXiv   pre-print
The novelty of the Panacea system is that uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker's time and resources.  ...  We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry.  ...  ., 2013 ) trained on email samples from different companies (e.g., Enron) are extracted using neural networks (Sherstinsky, 2013) , i.e., back-propagation model with average word vectors as features.  ... 
arXiv:2004.09662v1 fatcat:pxmy4zfgnncyhld7zvv3ouz3p4

Active Defense Against Social Engineering: The Case for Human Language Technology

Adam Dalton, Ehsan Aghaei, Ehab Al-Shaer, Archna Bhatia, Esteban Castillo, Zhuo Cheng, Sreekar Dhaduvai, Qi Duan, Bryanna Hebenstreit, Md. Mazharul Islam, Younes Karimi, Amir Masoumzadeh (+6 others)
2020 International Conference on Language Resources and Evaluation  
The novelty of the Panacea system is that uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker's time and resources.  ...  We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry.  ...  ., 2013) trained on email samples from different companies (e.g., Enron) are extracted using neural networks (Sherstinsky, 2013) , i.e., back-propagation model with average word vectors as features.  ... 
dblp:conf/lrec/DaltonAABCCDDHI20 fatcat:qc7itugvnndm3i4l3nc56ja5xa

Detecting threatening insiders with lightweight media forensics

Simson L. Garfinkel, Nicole Beebe, Lishu Liu, Michele Maasberg
2013 2013 IEEE International Conference on Technologies for Homeland Security (HST)  
In this paper we describe the underlying approach and demonstrate how the storage profile is created and collected using specially modified open source tools.  ...  This research uses machine learning and outlier analysis to detect potentially hostile insiders through the automated analysis of stored data on cell phones, laptops, and desktop computers belonging to  ...  A notable exception to this is Kohonen's Self-Organizing Map (SOM) approach-an unsupervised neural network approach [20] .  ... 
doi:10.1109/ths.2013.6698981 fatcat:rk2xtmgrdffytjcjo6koxk42ty

Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey

Said Salloum, Tarek Gaber, Sunil Vadera, Khaled Shaalan
2021 Procedia Computer Science  
To the best of our knowledge, this is the first survey that focuses on using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect phishing emails.  ...  To the best of our knowledge, this is the first survey that focuses on using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect phishing emails.  ...  , Recurrent Neural Networks (RNNs), Convolutional neural networks (CNN), and Deep Reinforcement Learning models.  ... 
doi:10.1016/j.procs.2021.05.077 fatcat:xiypnoobzfdn3h7ggopppaxdnq

ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level [article]

Mathieu Garchery, Michael Granitzer
2020 arXiv   pre-print
We describe how ADSAGE can be used for fine-grained, event level insider threat detection in different audit logs from the CERT use case.  ...  We evaluate ADSAGE on authentication, email traffic and web browsing logs from the CERT insider threat datasets, as well as on real-world authentication events.  ...  Using a recurrent neural network (RNN), ADSAGE is able to take into account sequences of events.  ... 
arXiv:2007.06985v1 fatcat:vtvmfrx2andzbhhglouie3t6i4

SoK: A Systematic Review of Insider Threat Detection

Aram Kim, Junhyoung Oh, Jinho Ryu, Jemin Lee, Kookheui Kwon, Kyungho Lee
2019 Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications  
Finally, the detection approaches used in related studies are examined from the perspective of technology, learning, input category, detection target, and interpretability.  ...  Second, we explore the sensors which make possible detecting insider threats in an automated way, and the public datasets available for research.  ...  [82] described their Deep Neural Network (DNN) model to detect insider threat and they used CERT dataset too. The dataset used in Eldardiry et al.  ... 
doi:10.22667/jowua.2019.12.31.046 dblp:journals/jowua/KimORLKL19 fatcat:qdw2eruvijhdjc3qsiit6yblda

Botnet Spam E-Mail Detection Using Deep Recurrent Neural Network

MOHAMMAD ALAUTHMAN
2020 International Journal of Emerging Trends in Engineering Research  
Therefore, the researcher of this study develops a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) with SVM for Bot Spam email detection.  ...  Through conducting extensive experiments, the researcher concludes that the proposed approach shows an excellent capability of detecting spam email.  ...  For detecting the spam email messages through using the neural network, there must be 2 stages; the training and testing stages.  ... 
doi:10.30534/ijeter/2020/83852020 fatcat:32hg3zixerfijpdbhjyggdmqaa

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

M.A. Nivedha, S. Raja
2022 International journal of computer science and mobile computing  
With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages  ...  These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine  ...  Network (RNN) approach is used for detecting electronic junk emails which is also known as spam emails.  ... 
doi:10.47760/ijcsmc.2022.v11i02.002 fatcat:nqnacdqscfarneroogxr4can3q

An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach

Gori Mohamed, J. Visumathi, Miroslav Mahdal, Jose Anand, Muniyandy Elangovan
2022 Processes  
Many existing phishing approaches have failed in providing a useful way to the issues facing e-mails attacks. Currently, hardware-based phishing approaches are used to face software attacks.  ...  The results suggest that a machine learning approach is best for detecting phishing.  ...  The same technology should be used on the sender side and the receiver side. Zhang et al. (2017) [15] Neural Networks A Neural Network was classified with the Monte Carlo algorithm.  ... 
doi:10.3390/pr10071356 fatcat:m6n3pmlagban7bita6hi65e5ve

ScaleNet: Scalable and Hybrid Frameworkfor Cyber Threat Situational AwarenessBased on DNS, URL,and Email Data Analysis

R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran, Vysakh S. Mohan, Amara Dinesh Kumar
2018 Journal of Cyber Security and Mobility  
However, there is a chance of detecting the malicious activities quickly by analyzing the events of DNS logs, Emails, and URLs.  ...  These approaches are adopted by anti-malware products. The conventional methods of were only efficient till a certain extent.  ...  We are also grateful to Computational Engineering and Networking (CEN) department for encouraging the research.  ... 
doi:10.13052/jcsm2245-1439.823 fatcat:dpsz7dfa2bhufg2fljdafxt2zi

On the computational models for the analysis of illicit activities [article]

Sarwat Nizamani, Saad Nizamani, Sehrish Nizamani, Imdad Ali Ismaili
2019 arXiv   pre-print
for suspicious email detection: A data Appavu et al.(2007) Gershenson, C. (2003) Artificial neural networks for beginners. arXiv preprint cs/0308031.  ...  Modeling suspicious email detection using enhanced feature selection Nizamani et al. (2012) Text analysis(Suspicious email detection) Data mining/machine learning Association rule mining  ... 
arXiv:1902.05691v1 fatcat:wo37ogtmpfhpvc2lsjmlvizq6q

Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis [article]

R G Gayathri, Atul Sajjanhar, Yong Xiang
2022 arXiv   pre-print
Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confidentiality concerns of organizations.  ...  Therefore, there arises demand to generate synthetic data to explore enhanced approaches for threat analysis.  ...  Classification using neural networks is a popular approach used in anomaly detection [5] .  ... 
arXiv:2203.02855v1 fatcat:naeat2lz4jg65bp2gaw2tzp25q

Enhanced Intrusion Network System using Fuzzy –K-Mediod Clustering Method

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
It became difficult to detect intrusion in large amount of data in a computer network.. Hence, the proposed research on different machine learning approach is used for network intrusion detection.  ...  Intrusion detection scheme (IDS) is software applications that are used for monitoring of the network to recognize the malicious activity in the system.  ...  Moreover, the performance of the anomaly detection technique can be improved using deep learning approach with neural network.  ... 
doi:10.35940/ijitee.l2583.1081219 fatcat:nyexbpjj3ngkrko3do6r7hvjiq
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