Mitigating and Detecting DDoS attack on IoT Environment

Rohit, Mehboob Hasan; Fahim, Sakif Md.; Khan, Abu Hurayra Asif

doi:10.1109/raaicon48939.2019.5

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…To provide security to IoT devices and SDNs, a three-tier IDPS uses user, packet, and flow validation [30]. The blockchain-based solutions also provide decentralized and transparent detection and prevention systems [31] [32]. With the aid of Internet Service Providers (ISPs), a unique solution known as "UM-BRELLA" executes security [33].…”

Section: A Conventional Solutionsmentioning

confidence: 99%

PREVIR: Fortifying Vehicular Networks Against Denial of Service Attacks

Verma,

Saha,

Kumar

et al. 2024

IEEE Access

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Vehicular networks are expanding their applications for future sustainability. The reported increasing rate of data breaches through vehicular networks by Distributed Denial of Service (DDoS) type of intrusion creates concern for such networks. Existing security solutions focus only on intrusion detection. However, prevention solutions are more proactive and provide security by probabilistic analysis. Existing prevention models for vehicular networks have low accuracy and are unable to handle zero-day attacks and advanced persistent threats. In this paper, we solve the problems mentioned above and introduce Predictive Risk Evaluation for Vehicular Infrastructure Resilience (PREVIR), the first amalgamated model of logit method (statistical analysis) and LogitBoost method (machine learning) to prevent DDoS attacks in vehicular networks. In PREVIR, the logit model predicts the packet probabilities for identifying maliciousness. The machine learning method improves PREVIR's performance through iterative refinement of the model's periodic updates based on new traffic parameters. We run a set of experiments on PREVIR. We use our NS3-generated dataset, NSL-KDD public dataset, and CIC-DDoS public dataset. PREVIR analyses multiple attack types, including UDP flood, TCP flood, mixed flooding, U2R, Probe, and R2L attacks. The results show that PREVIR classifies packets with accuracy up to 99.99%. Our proposed PREVIR model achieves a True Positive Ratio (TPR) up to 100% and an average False Positive Ratio (FPR) of 35%. The comparative analysis shows that PREVIR's efficiency is 20% better on average in the prevention of malicious packets as compared to the state-of-the-art models.

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Section: A Conventional Solutionsmentioning

confidence: 99%

PREVIR: Fortifying Vehicular Networks Against Denial of Service Attacks

Verma,

Saha,

Kumar

et al. 2024

IEEE Access

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“…[44][45][46] Cybercrime Criminal activity either targets or uses a computer, a computer network, or a networked device. [47,48] Malware Malicious software designed to infiltrate a device without knowledge [49,50] DDoS Denial of service attack on a computer system or network that causes a service or resource tobe inaccessible to legitimate users [51][52][53][54][55] Ransomware A type of malicious program that restricts access to certain parts of files of the infected operating system and demands a ransom in exchange for removing this restriction [56][57][58][59][60] Mobile malware Its name suggests malicious software that targets explicitly the operating systems on mobile phones [61][62][63][64] Watering hole Refers to a tactic used during targeted attack campaigns where the APT is distributed through a trusted website that is usually visited by employees of the target company or entity [65,66] We contrasted this result with an international organization related to cybersecurity. We found that some of them were considered the most relevant cyberattacks in the year 2020, according to The European Union Agency for Cybersecurity (ENISA) [34].…”

Section: Type Description Referencementioning

confidence: 99%

An Exploratory Study of Cognitive Sciences Applied to Cybersecurity

et al. 2022

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Cognitive security is the interception between cognitive science and artificial intelligence techniques used to protect institutions against cyberattacks. However, this field has not been addressed deeply in research. This study aims to define a Cognitive Cybersecurity Model by exploring fundamental concepts for applying cognitive sciences in cybersecurity. For achieving this, we developed exploratory research based on two steps: (1) a text mining process to identify main interest areas of research in the cybersecurity field and (2) a valuable review of the papers chosen in a systematic literature review that was carried out using PRISMA methodology. The model we propose tries to fill the gap in automatizing cognitive science without taking into account the users’ learning processes. Its definition is supported by the main findings of the literature review, as it leads to more in-depth future studies in this area.

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