Dr Kakulla Sireesha
@aliet.ac.in
Assistant Professor
AndhraLoyola institute of Engineering and Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Networks and Communications, Computer Science Applications, Computer Engineering, Computer Science
Scopus Publications
Scopus Publications
Sireesha Kakulla and Srinivas Malladi
International Information and Engineering Technology Association
VANET (Vehicular Ad-hoc Network) is a subclass of MANET in which many cars can connect with one another via node to node or equipment erected on the side of the road. However, due to the adaptability of centres and the unexpected trade in geography, there may be opportunities for attacks in VANET. One of the ostensible assaults is the Sybil attack, in which the attacker fabricates unequivocally unique equal personalities to undermine the value of VANET. Sybil creates fictitious identities inside the community as well in order to sabotage attempts to mediate conversations between community nodes. Sybil assaults have an impact on carrier transportation in relation to things like traffic congestion, road safety, and multimedia entertainment. VANETs therefore announce a security mechanism to protect you from Sybil attacks. In this regard, this work puts forth the SDTC method, which completely relies on machine learning techniques to prevent Sybil assaults in VANETs. In order to reduce identification time, increase detection accuracy, and enhance scalability, the SDTC (Sybil node detecting the use of Classification) mechanism uses a few vehicle-specific Extreme Learning Machine (ELM) features. The results suggest that SDTC is a suitable strategy to reduce Sybil assaults and sustain provider service in VANETs.
Sireesha Kakulla and Srinivas Malladi
ENGG Journals Publications
Sireesha Kakulla and Srinivas Malladi
International Information and Engineering Technology Association
Communication between vehicles is the core of VANETs, which are built on notions of mobile ad hoc networks (MANETs). Each car on the network is now seen as a mobile network node. All participating cars become wireless routers or nodes, depending on your viewpoint. VANET creates a huge network with a long-range by connecting all cars within range to a fixed unit. VANET assists with traffic regulation, communication between vehicles and the sharing of road data. There is a possibility that the VANET network could be compromised by identity and information concerns, resulting in data delays or theft. Attacks like Sybil and phishing are possible because of this network's weakness. Due to the two recent strikes, infrastructure and human lives may be in jeopardy. There are two novel algorithms developed by the authors to tackle Sybil and Phishing assaults on VANET networks: Phishing and Sybil Enhanced Behavior Processing and Footprints (P&SEBP&F). Originally known as the Phishing and Sybil Enhanced Behavior Processing and Footprint, P&STL&T was renamed P&STL&T. Phishing and Sybil were used as well as varied attacker-to-victim node ratios to test the effectiveness of the new tactics for assessing effectiveness. Compared to previous study and the work of the other authors cited, there were approximately 30% fewer attackers detected during the research.
K. Sireesha and Srinivas Malladi
IEEE
Vehicular Ad hoc Network (VANET) plays a vital role in communication between moving vehicles on a network enabled and regulated by wireless network protocols. However, as the size of the network increases, the vulnerability of the node attacks and data attacks also increases exponentially. Most of the conventional models are depend on the network size, its topology structure and data size. In this paper, a study of different VANET security models and its issues are studied based on the network parameters and security metrics. Different types of VANET security models, network attacks and encryption models are discussed in this paper. Finally, the limitations of the conventional VANET security models are discussed on large VANETs.