Enhancing OSN Security: Detecting Email Hijacking and DNS Spoofing Using Energy Consumption and Opcode Sequence Analysis
Abstract
The rapid increase in automation within Online Social Networks (OSNs) has led to a surge in cyber threats, notably Email Hijacking and DNS (Domain Name System) Spoofing, which leverage malicious scripts to manipulate traffic, steal credentials, and evade detection. Traditional security mechanisms fail to effectively identify such automation-based attacks, necessitating an advanced detection framework. Objective & Purpose-This study introduces the Automated Social Network Attack Detection Model (ASNADM), which combines Energy Consumption Footprint (EComp-FP) Analysis and Automated Software Opcode Sequence Analysis (ASOSA-OSM- opcode sequence mining) for high-precision OSN security. EComp-FP detects deviations in power consumption linked to malicious automation tools, while ASOSA-OSM analyzes opcode sequences to differentiate between benign and attack behaviors. The Self-Adaptive Fuzzy Pattern Matching Clustering (SAFPMC) Algorithm enhances classification accuracy, reducing false alarms and improving real-time threat detection. Methodology and DatasetThe model was rigorously evaluated using the SPEMC-15K-E (Spam Email Classification dataset in English) dataset (15,000 samples: 7,500 benign, 7,500 malicious). EComp-FP achieved 99.87% accuracy with a 1.4W power deviation, while ASOSA-OSM attained 99.81% accuracy, detecting automation tools with an Opcode Frequency Variance (OFV) of 8.7 in malicious samples versus 3.5 in benign ones. The hybrid EComp (Energy Consumption) + OSA (Opcode Sequence Analysis) model outperformed both standalone methods, achieving 99.93% accuracy, 99.91% F1-score, a false positive rate of just 0.07%, and a false negative rate of 0.05%. Among classifiers, the SelfAdaptive Soft Fuzzy C-Means (SSFCM) Hybrid model achieved the highest performance, with 99.93% accuracy, 99.85% precision, 99.9% recall, and the lowest misclassification rate of 0.05%, surpassing Decision Tree (DT), KNearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). Result - Optimization techniques significantly improved real-time detection efficiency. The SAFPMC algorithm reduced detection latency by 35%, while parallel processing lowered computational overhead by 31%. Feature selection improved classification speed by 27%, and federated learning reduced processing load by 25%, enabling scalable, real-time OSN threat monitoring. This study presents an advanced hybrid detection framework for OSN security, combining energy consumption profiling and opcode sequence analysis to detect email hijacking and DNS spoofing attacks. The model achieves a 99.92% detection precision, a 99.89% real-time accuracy, and reduces computational overhead by 31%, making it a robust and efficient solution for securing online social networks. These findings confirm that combining energy profiling and opcode sequence analysis is highly effective in detecting automation-based OSN threats. Future work will focus on integrating deep learning (DL) for anomaly detection, AI (artificial intelligence)- driven botnet defense, and enhancing large-scale OSN threat mitigation strategies.
Full Text:
PDFReferences
Bridges, R. A., Oesch, S., Iannacone, M. D., Huffer, K. M., Jewell, B., Nichols, J. A., ... & Smith, J. M. (2023). Beyond the Hype: An Evaluation of Commercially Available Machine Learning–based Malware Detectors. Digital Threats: Research and Practice, 4(2), 1-22.
https://scispace.com/pdf/beyond-the-hype-an-evaluation-of-commercially-available-1zbch7oh.pdf
Yan, X., Gao, Y., & Xu, H. (2022, December). Research on power grid anomaly detection based on high-dimensional random matrix theory. In 2022 2nd International Conference on Electrical Engineering and Control Science (IC2ECS) (pp. 427-431). IEEE.
https://doi.org/10.1016/j.sysarc.2019.01.008
Kakisim, A. G., Gulmez, S., & Sogukpinar, I. (2022). Sequential opcode embedding-based malware detection method. Computers & Electrical Engineering, 98, 107703.
https://doi.org/10.1016/j.compeleceng.2022.107703
Shetty, N. P., Muniyal, B., Anand, A., & Kumar, S. (2022). An enhanced sybil guard to detect bots in online social networks. Journal of Cyber Security and Mobility, 105-126.
https://doi.org/10.13052/jcsm2245-1439.1115
Riggs, H., Tufail, S., Parvez, I., Tariq, M., Khan, M. A., Amir, A., ... & Sarwat, A. I. (2023). Impact, vulnerabilities, and mitigation strategies for cyber-secure critical infrastructure. Sensors, 23(8), 4060.
https://doi.org/10.3390/s23084060
Parildi, E. S., Hatzinakos, D., & Lawryshyn, Y. (2021). Deep learning-aided runtime opcode-based windows malware detection. Neural Computing and Applications, 33(18), 11963-11983.
https://doi.org/10.1007/s00521-021-05861-7
Boahen, E. K., Sosu, R. N. A., Ocansey, S. K., Xu, Q., & Wang, C. (2024). ASRL: Adaptive Swarm Reinforcement Learning For Enhanced OSN Intrusion Detection. IEEE Transactions on Information Forensics and Security.
1109/TIFS.2024.3488506
Sufi, F. (2023). A new social media-driven cyber threat intelligence. Electronics, 12(5), 1242.
https://doi.org/10.3390/electronics12051242
Iqbal, A., Tehsin, S., Kausar, S., & Mishal, N. (2021, April). Malicious Image Detection Using Convolutional Neural Network. In 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) (pp. 1-6). IEEE.10.1109/AIMS52415.2021.9466042
Liu, Q., Li, J., Wang, X., & Zhao, W. (2023). Attentive Neighborhood Feature Augmentation for Semi-supervised Learning. Intelligent Automation & Soft Computing, 37(2).
32604/iasc.2023.039600
Ben Chaabene, N. E. H., Bouzeghoub, A., Guetari, R., & Ghezala, H. H. B. (2022). Deep learning methods for anomalies detection in social networks using multidimensional networks and multimodal data: A survey. Multimedia systems, 28(6), 2133-2143.
https://doi.org/10.1007/s00530-020-00731-z
Varshitha, K., Talada, S. V., & Mitra, A. (2025). Towards fake profiles identification in social networks: a proposal with energy-based PageRank algorithm involving entropy and domain authority. Risk Sciences, 100013.
https://doi.org/10.1016/j.risk.2025.100013
Lee, K., Lee, J., & Yim, K. (2023). Classification and analysis of malicious code detection techniques based on the APT attack. Applied Sciences, 13(5), 2894.
https://doi.org/10.3390/app13052894
Sangher, K. S., Singh, A., & Pandey, H. M. (2024). LSTM and BERT based transformers models for cyber threat intelligence for intent identification of social media platforms exploitation from darknet forums. International Journal of Information Technology, 16(8), 5277-5292.
https://doi.org/10.1007/s41870-024-02077-5
Li, K., Zheng, J., Ni, W., Huang, H., Liò, P., Dressler, F., & Akan, O. B. (2024). Biasing federated learning with a new adversarial graph attention network. IEEE Transactions on Mobile Computing.
1109/TMC.2024.3499371
Huang, H., Tian, H., Zheng, X., Zhang, X., Zeng, D. D., & Wang, F. Y. (2024). CGNN: A compatibility-aware graph neural network for social media bot detection. IEEE Transactions on Computational Social Systems.
1109/TCSS.2024.3396413
Rawat, R., & Rajavat, A. (2024). Illicit Events Evaluation Using NSGA-2 Algorithms Based on Energy Consumption. Informatica, 48(18).
https://doi.org/10.31449/inf.v48i18.6234
Sadia, H., Farhan, S., Haq, Y. U., Sana, R., Mahmood, T., Bahaj, S. A. O., & Rehman, A. (2024). Intrusion detection system for wireless sensor networks: A machine learning based approach. IEEE Access.
1109/ACCESS.2024.3380014
Song, S., Gao, N., Zhang, Y., & Ma, C. (2024). BRITD: behavior rhythm insider threat detection with time awareness and user adaptation. Cybersecurity, 7(1), 2.
https://doi.org/10.1186/s42400-023-00190-9
Ponnapalli, S., Dornala, R. R., & Sai, K. T. (2024, March). A Hybrid Learning Model for Detecting Attacks in Cloud Computing. In 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 318-324). IEEE.
1109/ICSADL61749.2024.00058
Denysiuk, D., Bobrovnikova, K., Lysenko, S., Savenko, O., Gaj, P., Havryliuk, R., & Boichuk, Y. (2021, September). The Approach for IoT Malware Detection Based on Opcodes Sequences Pattern Mining. In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 2, pp. 779-784). IEEE.
1109/IDAACS53288.2021.9660956
Majeed, A., Khan, S., & Hwang, S. O. (2022). A comprehensive analysis of privacy-preserving solutions developed for online social networks. Electronics, 11(13), 1931.
https://doi.org/10.3390/electronics11131931
Qian, K., Yang, H., Li, R., Chen, W., Luo, X., & Yin, L. (2024). Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning. Applied Sciences, 14(4), 1615.
https://doi.org/10.3390/app14041615
Pooyandeh, M., Han, K. J., & Sohn, I. (2022). Cybersecurity in the AI-Based metaverse: A survey. Applied Sciences, 12(24), 12993.
https://doi.org/10.3390/app122412993
Prabhu Kavin, B., Karki, S., Hemalatha, S., Singh, D., Vijayalakshmi, R., Thangamani, M., ... & Adigo, A. G. (2022). Machine learning‐based secure data acquisition for fake accounts detection in future mobile communication networks. Wireless Communications and Mobile Computing, 2022(1), 6356152.
https://doi.org/10.1155/2022/6356152
Montes, C. D., Silvosa, J. V., Abalorio, C. C., & Nakazato, R. B. (2024, August). Application of BERT Model for Unsupervised Text Classification using Hierarchical Clustering for Automatic Classification of Thesis Manuscript. In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 278-284). IEEE.10.1109/ICESC60852.2024.10690039
Alsadhan, A. A., Al-Atawi, A. A., Jameel, A., Zada, I., & Nguyen, T. N. (2024). Malware Attacks Detection in IoT Using Recurrent Neural Network (RNN). Intelligent Automation & Soft Computing, 39(2).10.32604/iasc.2023.041130
Rawat, R., Sikarwar, R., Maravi, P. K., Ingle, M., Bhardwaj, V., Rawat, A., & Rawat, H. (2024). Online social network automation attack detection methods for energy analysis and consumption modelling. International Journal of Information Technology, 1-13.https://doi.org/10.1007/s41870-024-02311-0
Chaudhary, K., Alam, M., Al-Rakhami, M. S., & Gumaei, A. (2021). Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics. Journal of Big data, 8(1), 73.https://doi.org/10.1186/s40537-021-00466-2
Jianwu, Z. H. A. N. G., Yanjun, A. N., & Huangyan, D. E. N. G. (2022). A survey on DNS attack detection and security protection. Telecommunications Science , 38(9).10.11959/j.issn.1000--0801.2022248
Alshaibi, A., Al-Ani, M., Al-Azzawi, A., Konev, A., & Shelupanov, A. (2022). The comparison of cybersecurity datasets. Data, 7(2), 22.https://doi.org/10.3390/data7020022
Jain, M., Kaur, G., & Saxena, V. (2022). A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection. Expert Systems with Applications, 193, 116510.https://doi.org/10.1016/j.eswa.2022.116510
Alowibdi, J. S. (2024). Real Time Arabic Communities Attack Detection on Online Social Networks. International Journal of Computer Science & Network Security, 24(8), 61-71.
https://doi.org/10.22937/IJCSNS.2024.24.8.7
Vc, J., Nair, K. S., Karthik, N., & Vani, V. (2024, July). Unsolicited Email Filtering. In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) (pp. 1-6). IEEE.
1109/IConSCEPT61884.2024.10627840
DOI: https://doi.org/10.31449/inf.v49i2.6956

This work is licensed under a Creative Commons Attribution 3.0 License.