Threat Assessment and Denial of Service Attacks in Software Defined Network based Smart Grid: A Survey

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@article{1723648,
      author = {Faheem Ahmad Qureshi},
      title = {Threat Assessment and Denial of Service Attacks in Software Defined Network based Smart Grid: A Survey},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {62-137},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723648.pdf},
      abstract = {Smart Grid (SG) is the super critical infrastruc-ture (CI) that fulfils the energy demands of the public, state departments, and also energises and regulates the operations of other CIs. This significant role of SG demands an efficient and reliable underlying infrastructure; power system, communication layer, and SG applications; to enable a secure and operational SG. Although SG has revolutionised the traditional electric grid but its communication layer still lacks efficient management, network automation, programmability, scalability, monitoring, and control. Software defined networks (SDN), offering a greater degree of flexibility, control, and programmability, has a great scope to complement the communication domain of SG; mean-while also introduces certain vulnerabilities. The emerging SDN based SG (SDN-SG) paradigm is in infancy stages in terms of threat assessment and defence mechanisms. Like, one of the severest cyber attacks, denial of service (DoS) attacks, which overwhelms the network resources to curtail the availability of the network for legit users, are still not well-addressed for SDN-SG in the literature. This paper comprehensively surveys the vulnerabilities and associated assessment models of SDN-SG. First, the existing threat assessment models and their adaptability for SDN-SG is evaluated. Afterwards, DoS attacks and associated mitigation techniques for SDN and their scope for SDN-SG are comprehensively studied. Various DoS attacks are identified which are still unaddressed in SDN-SG. In the end, machine learning (ML) techniques are reviewed for securing SDN-SG against a wide spectrum of DoS attacks. Furthermore, we also present challenges, issues, and future research directions related to threat assessment, DoS attacks, and ML based mitigation techniques for SDN-SG.},
      keywords = {Software defined network (SDN), smart grid (SG), Cyber Security, Threat Assessment, Denial of Service (DoS) Attacks.},
      month = {October},
      doi = {https://doi.org/10.64388/IREV10I4-1723648}
  }