Novel DSIDS- Deep Sniffer Intrusion Detection System


  • Abhijit Kadam Bharati Vidyapeeth Deemed to be University (College of Engineering) Pune-411043, India
  • Bindu Garg Bharati Vidyapeeth Deemed to be University (College of Engineering) Pune-411043, India
  • Milind Gayakwad Bharati Vidyapeeth Deemed to be University (College of Engineering) Pune-411043, India
  • Ketan Kotecha Symbiosis Institute of Technology, Pune, Symbiosis International (Deemed University), Pune, India
  • Rahul Joshi Symbiosis Institute of Technology, Pune, Symbiosis International (Deemed University), Pune, India


Deep Sniffer, Intrusion detection, hybrid model


Intrusion detection identifies malicious activity in a computer system or network. It is a critical component of any information security system, as it can help to protect against unauthorised access, data theft, and other forms of cyberattacks. Traditional intrusion detection techniques have limitations, such as signature-based and anomaly-based detection. Identity-based mechanisms function correctly only if the intrusion matches the identity in the database. If the intruder applies mutation in the identity of an intrusion, this mechanism may not work. The Anomaly-based mechanism increases the complexity by tagging valid traffic or requests as a threat.

Deep learning is a machine learning technique effective in various tasks, including image classification, natural language processing, and speech recognition. Deep learning has also been applied to intrusion detection in recent years with promising results. Deep learning models can learn to identify malicious activity by extracting complex features from raw data, such as network traffic or system logs. This makes them less susceptible to evasion by attackers than traditional intrusion detection techniques. This research article reviews the literature on intrusion detection using deep learning techniques. The article also discusses the challenges and limitations of deep learning for intrusion detection and proposes some directions for future research.

The research Article covers the overview of the intrusions in India, and Global in recent years across the platforms. Considering the potential threats, the Deep Sniffer Intrusion Detection System (DSIDS) model is devised to identify the intrusion on the KDD 99 Dataset with an accuracy of 88.97 %.


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How to Cite

Kadam, A. ., Garg, B. ., Gayakwad, M. ., Kotecha, K. ., & Joshi, R. . (2024). Novel DSIDS- Deep Sniffer Intrusion Detection System. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 400–407. Retrieved from



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