Machine learning with digital forensics for attack classification in cloud network environment

• Aleem Ali, Neeta Singh. (2016) QoS Analysis in MANETs Using Queueing Theoretic Approaches A review. International Journal of Latest Trend in Engineering and Technology (IJLTET), 7(1): 120–124. UGC listed

• Aleem Ali, Neeta Singh (2018) M/M/1/n+Flush/n model to enhance the QoS for Cluster Heads in MANETs. Published in International Journal of Advanced Computer Science and Applications (IJACSA), U.K. ESCI, Scopus

• Ahmadzadegan MH, Khorshidvand AA, and Valian MG (2015) Low-rate false alarm intrusion detection system with genetic algorithm approach. In: 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran, 1045–1048. https://doi.org/10.1109/KBEI.2015.7436188

• Alqatawna J, Hadi A, Al-Zwairi M and Khader M (2016) A Preliminary Analysis of Drive-by Email Attacks in Educational Institutes. In: Cybersecurity and Cyberforensics Conference (CCC), Amman, Jordan, 65–69, https://doi.org/10.1109/CCC.2016.16

• Amin A, Anwar S, Adnan A, Khan MA, and Iqbal Z (2015) Classification of cyber-attacks based on rough set theory. In: 2015 First International Conference on Anti-Cybercrime (ICACC), Riyadh, Saudi Arabia, 1–6. https://doi.org/10.1109/Anti-Cybercrime.2015.7351952

• Anfilofiev AE, Hodashinsky IA and Evsutin OO (2014) Algorithm for tuning fuzzy network attack classifiers based on invasive weed optimization. In: Dynamics of Systems, Mechanisms, and Machines (Dynamics), Omsk, Russia, 1–4. https://doi.org/10.1109/Dynamics.2014.7005632

• Ariafar E and Kiani R (2017) Intrusion detection system using an optimized framework based on data mining techniques. In: IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran, 0785–0791. https://doi.org/10.1109/KBEI.2017.8324903

• Chakraborty C, Abougreen A (2018) Intelligent internet of things and advanced machine learning techniques for COVID-19. EAI Endorsed Transactions Pervasive Health Technol. https://doi.org/10.4108/eai.28-1-2021.168505

  Article  Google Scholar 

• Dastanpour A, Ibrahim S, Mashinchi R and Selamat A (2014) Comparison of genetic algorithm optimization on artificial neural network and support vector machine in the intrusion detection system. In: IEEE Conference on Open Systems (ICOS), Subang, Malaysia, 72–77. https://doi.org/10.1109/ICOS.2014.7042412

• Desai AS and Gaikwad DP (2016) Real-time hybrid intrusion detection system using a signature matching algorithm and fuzzy-GA. In: IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), Pune, India, 291–294. https://doi.org/10.1109/ICAECCT.2016.7942601

• Desale KS and Ade R (2015) Genetic algorithm-based feature selection approach for an effective intrusion detection system. In: International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 1–6. https://doi.org/10.1109/ICCCI.2015.7218109

• Ding F, Zhu G, Alazab M, Li X, Yu K (2020) Deep-learning-empowered digital forensics for edge consumer electronics in 5G HetNets. IEEE Consumer Electron Magazine. https://doi.org/10.1109/MCE.2020.3047606

  Article  Google Scholar 

• Duo R, Nie X, Yang N, Yue C, Wang Y (2021) Anomaly detection and attack classification for train real-time Ethernet. IEEE Access 9:22528–22541. https://doi.org/10.1109/ACCESS.2021.3055209

  Article  Google Scholar 

• Fauzi MA, Hanuranto AT and Setianingsih C (2020) Intrusion Detection System using Genetic Algorithm and K-NN Algorithm on Dos Attack. In: 2nd International Conference on Cybernetics and Intelligent System (ICORIS), Manado, Indonesia, 1–6. https://doi.org/10.1109/ICORIS50180.2020.9320822

• Ferriyan A, Thamrin AH, Takeda K, and Murai J (2017) Feature selection using a genetic algorithm to improve classification in network intrusion detection system. International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Surabaya, Indonesia, 46–49. https://doi.org/10.1109/KCIC.2017.8228458

• A K, Gronkowski S, and Lekhac NA (2018) Enabling Trust in Deep Learning Models: A Digital Forensics Case Study. In: 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And ‘Engineering (TrustCom/BigDataSE), New York, NY, USA, 1250–1255. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00172

• Hedar A, Omer MA, Al-Sadek AF and Sewisy AA (2015) Hybrid evolutionary algorithms for data classification in intrusion detection systems. In: IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Takamatsu, Japan, 1–7. https://doi.org/10.1109/SNPD.2015.7176208

• Huang R, Fang F, Nguyen HH, Yamagishi J, and Echizen I (2020) Security of Facial Forensics Models Against Adversarial Attacks. In: IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2236–2240. https://doi.org/10.1109/ICIP40778.2020.9190678

• Kaur R, Ali A (2021) A novel blockchain model for securing IoT based data transmission. International Journal of Grid and Distributed Computing 14(1):1045–1055

  Google Scholar 

• Kaur R and Bansal M (2016) Multidimensional attacks classification based on genetic algorithm and SVM. In: 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India, 561–565, https://doi.org/10.1109/NGCT.2016.7877477

• Kishor A, Chakarbarty C (2021) Task offloading in fog computing for using smart ant colony optimization. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08714-7

  Article  Google Scholar 

• Kishor A, Chakraborty C (2021) Artificial intelligence and internet of things based healthcare 4.0 monitoring system. Wireless Personal Commun. https://doi.org/10.1007/s11277-021-08708-5

  Article  Google Scholar 

• Kishor A, Chakraborty C, Jeberson W (2021) Reinforcement learning for medical information processing over heterogeneous networks, Multimedia tools and applications. Springer, pp 23983–24004

  Google Scholar 

• Kumar A, Singh G, Kansal A, Singh K (2021) Digital image forensic approach to counter the JPEG Anti-Forensic attacks. IEEE Access 9:4364–4375. https://doi.org/10.1109/ACCESS.2020.3048246

  Article  Google Scholar 

• Nazia Parveen, Ashif Ali, Aleem Ali (2020) IOT Based Automatic Vehicle Accident Alert System. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 330–333, Greater Noida. https://doi.org/10.1109/ICCCA49541.2020.9250904 (Scopus Indexed)

• Rajawat AS and Upadhyay AR (2020) Web Personalization Model Using Modified S3VM Algorithm For developing Recommendation Process. In: 2nd International Conference on Data, Engineering and Applications (IDEA), Bhopal, India, 1–6. https://doi.org/10.1109/IDEA49133.2020.9170701

• Rajawat AS, Upadhyay P, Upadhyay A (2020) Novel Deep Learning Model for Uncertainty Prediction in Mobile Computing, Advances in Intelligent Systems and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_49

  Book  Google Scholar 

• Saibharath S and Geethakumari G (2015) Cloud forensics: Evidence collection and preliminary analysis. In: IEEE International Advance Computing Conference (IACC), Banglore, India, 464–467. https://doi.org/10.1109/IADCC.2015.7154751

• Sachdeva S, Ali A (2021) A Hybrid approach using digital Forensics for attack detection in a cloud network environment. International Journal of Future Generation Communication and Networking 14(1):1536–1546

  Google Scholar 

• Shona D and Kumar MS (2018) Efficient IDs for MANET Using Hybrid Firefly with a Genetic Algorithm. In: International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 191–194. https://doi.org/10.1109/ICIRCA.2018.8597268

• Tariang DB, Chakraborty RS, Naskar R (2019) A robust residual dense neural network for countering antiforensic attack on median filtered images. IEEE Signal Process Lett 26(8):1132–1136. https://doi.org/10.1109/LSP.2019.2922498

  Article  Google Scholar 

• Wiyono RT, and Cahyani NDW (2020) Performance Analysis of Decision Tree C4.5 as a Classification Technique to Conduct Network Forensics for Botnet Activities in the Internet of Things. In: International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia, 1–5. https://doi.org/10.1109/ICoDSA50139.2020.9212932.

Download references