Smart City Gnosys
Smart city article details
| Title | A Unified Predictability For Bot Attack On Iot Devices And Its Platforms |
|---|---|
| ID_Doc | 5719 |
| Authors | Aarthi S.; Krishna A.V.; Prasad M.H.; Nithish M.S. |
| Year | 2022 |
| Published | AIP Conference Proceedings, 2405 |
| DOI | http://dx.doi.org/10.1063/5.0073070 |
| Abstract | IoT which is briefly known as the Internet of Things, it is one of the few main resources that transmutes everything by making day to day life easier through monitoring and controlling of the smart objects that are interlinked to each other. They exist from households, e-healthcare, industrial cars, and smart cities to wearable's, farming, transportation, smart control systems, etc. All of these IoT device's implementation is mounting in extensive quantity of data. Therefore, processing these annually generated data are increasing alongside. They carry comfort in human lives; however, they are easily prone towards diverse threats like safety encounters in subtle surroundings (e-health and smart home) and other areas by posing as a threat for IoT in approaching times. Henceforward, we meticulously review the safety necessities, threats, attack vector challenges that are relevant to IoT systems. Based off breach breakdown, we conclude to a suitable solution. Through the tremendous growth of DDoS attacks and IoT botnets in recent times, IoT safety is the utmost troubled issues in a network safety field. A whole lot of safety methods came to be projected on these new evolving issues of IoT malware, but they still lack in dealing with them. E.g. Zero-Day Attacks. Here the extent of an innovative tactic for malware detection method like using the machine learning techniques can be used towards contending DDoS Attacks (Zero-Day) and mirai effects that develops an uncluttered task securing IoT network devices. These days we benefit from IoT technologies, however, newer safety difficulties arise as new IoT systems are introduced. Most of IoT systems that are linked in Net have trivial safety measures, therefore smart homes are exceedingly susceptible towards comprisable attacks. These threats include generic CIA attacks and IoT botnets. The study discovers botnet recognition by testing with an administered machine learning with deranged dataset which contains cordial data usually assorted in slight volumes of malicious data. Here the established classifiers are able to identify distinct malicious action from normal action in a minor IoT dataset network and collective traffic attacks. © 2022 Author(s). |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
5280  | 0.894 | Kumar Yadav R.; Karamveer K. | A Survey On Iot Botnets And Their Detection Approaches | Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 (2022) |
| 33508 | 0.894 | Saini K.S.; Chaudhary S. | Investigation On Attack Detection In Iot Networks: A Study And Analysis Of The Existing Machine Learning And Deep Learning Techniques | 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025) |
| 34135 | 0.891 | Rattanalerdnusorn E.; Pattaranantakul M.; Thaenkaew P.; Vorakulpipat C. | Iotdept: Detecting Security Threats And Pinpointing Anomalies In An Iot Environment | ACM International Conference Proceeding Series (2020) |
| 34132 | 0.89 | Ashraf, J; Keshk, M; Moustafa, N; Abdel-Basset, M; Khurshid, H; Bakhshi, AD; Mostafa, RR | Iotbot-Ids: A Novel Statistical Learning-Enabled Botnet Detection Framework For Protecting Networks Of Smart Cities | SUSTAINABLE CITIES AND SOCIETY, 72 (2021) |
| 34139 | 0.89 | Selvam R.; Velliangiri S. | Iotsdl: Internet Of Things Security For Deep Learning Techniques-A Research Perspectives | 2023 International Conference on Computer Communication and Informatics, ICCCI 2023 (2023) |
| 35958 | 0.887 | Lefoane M.; Ghafir I.; Kabir S.; Awan I.-U. | Machine Learning For Botnet Detection: An Optimized Feature Selection Approach | ACM International Conference Proceeding Series (2021) |
| 11011 | 0.887 | Binu P.K.; Kiran M.; Sreehari M.V. | Attack And Anomaly Prediction In Iot Networks Using Machine Learning Approaches | 2021 4th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2021 (2021) |
| 12793 | 0.885 | Sharma A.; Babbar H. | Bot-Iot: Detection Of Attacks In Iot-Cybersecurity For Smart Transportation | Proceedings of the 8th International Conference on Communication and Electronics Systems, ICCES 2023 (2023) |
| 15037 | 0.885 | Abinaya M.; Prabakeran S.; Kalpana M. | Comparative Evaluation On Various Machine Learning Strategies Based On Identification Of Ddos Attacks In Iot Environment | 2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023 (2023) |
| 32898 | 0.884 | Kaur B.; Dadkhah S.; Shoeleh F.; Neto E.C.P.; Xiong P.; Iqbal S.; Lamontagne P.; Ray S.; Ghorbani A.A. | Internet Of Things (Iot) Security Dataset Evolution: Challenges And Future Directions | Internet of Things (Netherlands), 22 (2023) |