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Automated State Machines Applied in Client Honeypots

Yaser Alosefer, Omer Rana
2010 2010 5th International Conference on Future Information Technology  
If known malicious similarities are found, then it predicts the possible future malicious behaviour of the current web page and reports this back to the system.  ...  By using this label, we can identify similarity between groups and then use the label to match with new data.  ... 
doi:10.1109/futuretech.2010.5482695 fatcat:n6xgujmyeng6lmqgqwoou52cfy

Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

Nivedita Mishra, Sharnil Pandya
2021 IEEE Access  
learning techniques for data pre-processing and malware detection has been discussed.  ...  It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same.  ...  In the future, we plan to implement these solutions and develop a robust and generalized intrusion detection model.  ... 
doi:10.1109/access.2021.3073408 fatcat:ebzvtidh2relplv3kn3t6plygu

Leveraging CybOX™ to standardize representation and exchange of digital forensic information

Eoghan Casey, Greg Back, Sean Barnum
2015 Digital Investigation. The International Journal of Digital Forensics and Incident Response  
The capability to represent provenance by leveraging CybOX is also demonstrated, including specifics of the tool used to process digital evidence and the resulting output.  ...  With the growing number of digital forensic tools and the increasing use of digital forensics in various contexts, including incident response and cyber threat intelligence, there is a pressing need for  ...  Conclusions and future work To be effective, digital forensic information needs to be represented and shared in a form that is consistent across all applicable contexts and tools.  ... 
doi:10.1016/j.diin.2015.01.014 fatcat:4tljk4lhdngvrdheqwwacxtana

Online Advertising Security: Issues, Taxonomy, and Future Directions [article]

Zahra Pooranian, Mauro Conti, Hamed Haddadi, Rahim Tafazolli
2021 arXiv   pre-print
to discover new security vulnerabilities in the model, to propose countermeasures and to forecast future trends in research.  ...  To complete our work, we identify some open issues and outline some possible directions for future research towards improving security methods for online advertising systems.  ...  The authors in [137] used the UI-based methodology to detect malware on the mobile operating system. They claimed that it is essential to find the source of the attack in order to detect it.  ... 
arXiv:2006.03986v4 fatcat:vyxorsmo4rgdllv7lmntc5bv7i

NLP Methods in Host-based Intrusion Detection Systems: A Systematic Review and Future Directions [article]

Zarrin Tasnim Sworna, Zahra Mousavi, Muhammad Ali Babar
2022 arXiv   pre-print
We highlight the prevalent practices and the future research areas.  ...  We conduct a systematic review of the literature on NLP-based HIDS in order to build a systematized body of knowledge.  ...  ACKNOWLEDGMENTS The work has been supported by the Cyber Security Research Centre Limited whose activities are partially funded by the Australian Government's Cooperative Research Centres Programme.  ... 
arXiv:2201.08066v1 fatcat:t6nsqoj5hnhnhcv4hsugsnau6m

Learning-Based Methods for Cyber Attacks Detection in IoT Systems: Methods, Analysis, and Future Prospects

Usman Inayat, Muhammad Fahad Zia, Sajid Mahmood, Haris M. Khalid, Mohamed Benbouzid
2022 Electronics  
Finally, future research directions are also provided in the paper.  ...  For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems.  ...  In [54] , the hierarchical stacking-temporal convolutional network (HS-TCN) was developed to detect anomalies in the communication of smart homes.  ... 
doi:10.3390/electronics11091502 fatcat:stweql4ru5behg2scpumznzy6i

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Javed Asharf, Nour Moustafa, Hasnat Khurshid, Essam Debie, Waqas Haider, Abdul Wahab
2020 Electronics  
This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.  ...  The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances.  ...  This leads to exposing all information stored on the phone with the possibility of malware compromise.  ... 
doi:10.3390/electronics9071177 fatcat:isocsvn75ja6bfqsk2wekuufyy

Current and Future Challenges in Mining Large Networks

Lawrence B. Holder, Tina Eliassi-Rad, Aditya Prakash, Rajmonda Caceres, David F. Gleich, Jason Riedy, Maleq Khan, Nitesh V. Chawla, Ravi Kumar, Yinghui Wu, Christine Klymko
2016 SIGKDD Explorations  
This half-day workshop consisted of a keynote talk, four technical paper presentations, one demonstration, and a panel on future challenges in mining large networks.  ...  The current and future challenges discussed at the workshop and elaborated here provide valuable guidance for future research in the field.  ...  Graphs are a popular representation for such data because of their ability to represent different entity and relationship types, including the temporal relationships necessary to represent the dynamics  ... 
doi:10.1145/2980765.2980770 fatcat:e7g5ju6icnclpmc6z56rprwqu4

Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction

Kazi Istiaque Ahmed, Mohammad Tahir, Mohamed Hadi Habaebi, Sian Lun Lau, Abdul Ahad
2021 Sensors  
With the ongoing efforts for widespread Internet of Things (IoT) adoption, one of the key factors hindering the wide acceptance of IoT is security.  ...  To address the security issue of IoT, several studies are being carried out that involve the use of, but are not limited to, blockchain, artificial intelligence, and edge/fog computing.  ...  the dynamic malware detection game.  ... 
doi:10.3390/s21155122 fatcat:auzbla5c4rhzfdtnhmxeedyymq

Deep Learning-Based Network Security Data Sampling and Anomaly Prediction in Future Network

Lan Liu, Jun Lin, Pengcheng Wang, Langzhou Liu, Rongfu Zhou
2020 Discrete Dynamics in Nature and Society  
With the comparison of various algorithms and the adjustment of hyperparameters, the data characteristics and classification algorithms corresponding to different network security attacks are found.  ...  Deep learning methods are used on the selected nodes to collect data and analyze the characteristics of the network data.  ...  In order to ensure the security of the network, it is necessary to detect packets in the future network.  ... 
doi:10.1155/2020/4163825 fatcat:ob7ggsp2cfbs3nnedq72o2ik4y

D2.1 5G Security: Current Status and Future Trends

Grant Millar, Anastasios Kafchitsas, Orestis Mavrooulos, Anastasios Kourtis, George Xilouris, Maria Christopoulou, Stavros Kolometsos, Edgardo Montes De Oca, Huu Nghia Nguyen, Antonio Pastor, Sonia Fernandez, Diego Lopez (+18 others)
2020 Zenodo  
domain of 5G security, the relevant 5G projects, and open source initiatives; and a description of future trends and technologies in 5G networks, their limitations, and gaps related to the security of  ...  This deliverable aims to provide a basis for the identification of use cases and the development of 5G security enablers in INSPIRE-5Gplus.  ...  In the context of future networks, where there will be a huge amount of data, it is impractical to manually label a dataset.  ... 
doi:10.5281/zenodo.3947893 fatcat:bg7jnn5ph5fv3gjp7pqacy3us4

AIs 10 to Watch: The Future of AI

2018 IEEE Intelligent Systems  
These initial successes point the way to important future research and applications in a wide range of security arenas.  ...  The 2018 group consists of 10 young stars who have demonstrated outstanding AI achievements.  ... 
doi:10.1109/mis.2018.012001549 fatcat:mfx4dnb5lrgjbizilzh2anbcz4

D2.1 5G Security: Current Status and Future Trends

Grant Millar, Anastasios Kafchitsas, Orestis Mavrooulos, Anastasios Kourtis, George Xilouris, Maria Christopoulou, Stavros Kolometsos, Edgardo Montes De Oca, Huu Nghia Nguyen, Antonio Pastor, Sonia Fernandez, Diego Lopez (+18 others)
2020 Zenodo  
domain of 5G security, the relevant 5G projects, and open source initiatives; and a description of future trends and technologies in 5G networks, their limitations, and gaps related to the security of  ...  This deliverable aims to provide a basis for the identification of use cases and the development of 5G security enablers in INSPIRE-5Gplus.  ...  In the context of future networks, where there will be a huge amount of data, it is impractical to manually label a dataset.  ... 
doi:10.5281/zenodo.4569519 fatcat:7aersbhzyrccrn563shazvj4dq

Network Forensics Method Based on Evidence Graph and Vulnerability Reasoning

Jingsha He, Chengyue Chang, Peng He, Muhammad Pathan
2016 Future Internet  
volume and to back track the attacks.  ...  Zhang et al. applied machine learning methods to detecting malware activities through network traffic analysis [20] and developed an approach to analyze programs as black boxes from the network perspective  ...  Acknowledgments: The work presented in this paper has been supported by funding from National High-Tech R&D Program (863 Program) (2015AA017204), National Natural Science Foundation of China (61272500)  ... 
doi:10.3390/fi8040054 fatcat:efksu26pofhtxekc3geldfxdsa

Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [article]

Shaashwat Agrawal, Sagnik Sarkar, Ons Aouedi, Gokul Yenduri, Kandaraj Piamrat, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu
2021 arXiv   pre-print
Machine Learning and Deep Learning with Intrusion Detection Systems have gained great momentum due to their achievement of high classification accuracy.  ...  The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.  ...  i th client, back to the server.  ... 
arXiv:2106.09527v1 fatcat:vsy4l2ew4nbh5j3gzdlao4ngxe
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