A. ARUL ANITHA

I am currently working as an Assistant Professor in the Department of Computer Science at Jayaraj Annapackiam College for Women (Autonomous), Periyakulam, Theni District, Tamil Nadu, India. I cleared the UGC NET for assistant professors and have six years of teaching experience. I have published around eight papers in reputed international journals, including six papers indexed in Scopus, and published a book chapter and a text book on Python programming for graduate students.

EDUCATION

Doctorate in Computer Science from Bharathidasan University, Tiruchirappalli, Tamil Nadu, India

Post Graduation in Computer Application from Manonmaniam Sundaranar University, Tirunelvel, Tamil Nadu, India

Graduated in Computer Science from Madurai Kamaraj University, Madurai, Tamil Nadu, India

RESEARCH INTERESTS

Computer Networks, Intrusion Detection System, Internet of Things, Machine Learning

Scopus Publications

DISTec: A Detection and Mitigation Technique for DIS Flooding Attacks in the Internet of Things
A. Arul Anitha
Iraqi Journal of Science, 2025
The Internet of Things (IoT) is an innovative trend that promotes technological and industrial advancements. As IoT networks expand, RPL (Routing Protocol for Low-Power and Lossy Networks) becomes crucial for efficient routing but is susceptible to a number of security threats and attacks. The DIS (DODAG Information Solicitation) flooding attack is one such attack that compromises network performance. This paper introduces DISTec, a novel technique designed to detect and mitigate DIS flooding attacks in RPL-based IoT networks. DISTec employs an n x m matrix to track DIS message frequencies from nodes, analyzing deviations during the DIS_INTERVAL to identify potential attackers. The Contiki OS Cooja Simulator implemented DISTec on a network consisting of 51 legitimate nodes and one malicious node. DISTec demonstrated a detection accuracy of 98.48%. It effectively isolates attackers by discarding their malicious messages and excluding them from the DODAG, thus enhancing network stability and performance. This research highlights DISTec’s effectiveness in maintaining network integrity and reducing the impact of flooding attacks. Future work will explore additional RPL attack scenarios and integrate AI-driven techniques to further improve attack detection and network security.
A Review on Intrusion Detection Systems to Secure IoT Networks
A. Arul Anitha, L. Arockiam
International Journal of Computer Networks and Applications, 2022
The Internet of Things (IoT) and its rapid advancements will lead to everything being connected in the near future. The number of devices connected to the global network is increasing every day. IoT security challenges arise as a result of the large-scale incorporation of smart devices. Security issues on the Internet of Things have been the most focused area of research over the last decade. As IoT devices have less memory, processing capacity, and power consumption, the traditional security mechanisms are not suitable for IoT. A security mechanism called an Intrusion Detection System (IDS) has a crucial role in protecting the IoT nodes and networks. The lightweight nature of IoT nodes should be considered while designing IDS for the IoT. In this paper, the types of IDS, the major attacks on IoT, the recent research, and contributions to IDS in IoT networks are discussed, and an analytical survey is given based on the study. Though it is a promising area for research, IDS still needs further refinement to ensure high security for IoT networks and devices. Hence, further research, development, and lightweight mechanisms are required for IDS to provide a higher level of security to the resource-limited IoT
Ada-IDS: AdaBoost Intrusion Detection System for ICMPv6 based Attacks in Internet of Things
A. Arul Anitha, L. Arockiam
International Journal of Advanced Computer Science and Applications, 2021
The magical buzzword Internet of Things (IoT) connects any objects which are diverse in nature. The restricted capacity, heterogeneity and large scale implementation of the IoT technology tend to have lot of security threats to the IoT networks. RPL is the routing protocol for the constraint devices like IoT nodes. ICMPv6 protocol plays a major role in constructing the tree-like topology called DODAG. It is vulnerable to several security attacks. Version Number Attack, DIS flooding attack and DAO attack are the ICMPv6 based attacks discussed in this paper. The network traffic is collected from the simulated environment in the normal and attacker settings. An AdaBoost ensemble model termed Ada-IDS is developed in this research to detect these three ICMPv6 based security attacks in RPL based Internet of Things. The proposed model detects the attacks with 99.6% accuracy and there is no false alarm rate. The Ada-IDS ensemble model is deployed in the Border Router of the IoT network to safeguard the IoT nodes and network. Keywords—IoT; ICMPv6; version number attack; DIS attack; DAO attack; Ada-IDS
ANNIDS: Artificial neural network based intrusion detection system for internet of things
A. Arul Anitha, , Dr. L. Arockiam, and
International Journal of Innovative Technology and Exploring Engineering, 2019
Internet of Things (IoT) makes everything in the real world to get connected. The resource constrained characteristics and the different types of technology and protocols tend to the IoT be more vulnerable than the conventional networks. Intrusion Detection System (IDS) is a tool which monitors analyzes and detects the abnormalities in the network activities. Machine Learning techniques are implemented with the Intrusion detection systems to enhance the performance of IDS. Various studies on IoT reveals that Artificial Neural Network (ANN) provides better accuracy and detection rate than other approaches. In this paper, an Artificial Neural Network based IDS (ANNIDS) technique based on Multilayer Perceptron (MLP) is proposed to detect the attacks initiated by the Destination Oriented Direct Acyclic Graph Information Solicitation (DIS) attack and Version attack in IoT environment. Contiki O.S/Cooja Simulator 3.0 is used for the IoT simulation.
A hybrid method for smart irrigation system
A. Arul Anitha*, , A. Stephen*, Dr. L. Arockiam*, , and
International Journal of Recent Technology and Engineering, 2019
Internet of Things (IoT) is a boon to the technological developments during the past decade. Though the adoption of this technology in agriculture has gone up immensely in recent years, the implementation of the smart irrigation system remains its initial stage in this agricultural setup. The sprinkler or dripper irrigation methods are widely used in the smart irrigation environment. In this paper a hybrid method is proposed to select the irrigation method automatically based on the climate changes and soil moisture level. By enhancing this method using the rapid growing technologies and IoT enabled smart irrigation controllers, the agriculture sector will be improved over the foreseeable future.

Publications

A. Arul Anitha, A. Stephen and Dr. L. Arockiam, “A Hybrid Method on Smart Irrigation System”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume 8, Issue 3, 2019, pp. 2995 – 2998. (Scopus Indexed)

A. Arul Anitha and Dr. L. Arockiam, “ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume 8, Issue 11, 2019, . (Scopus Indexed)

A. Arul Anitha and Dr. L. Arockiam, “Promoting a Clean and Hygienic Environment using IoT”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume 8, Issue 5, 2020, pp. 4722-4726. (Scopus Indexed)

A. Arul Anitha and Dr. L. Arockiam, “VeNADet: Version Number Attack Detection for RPL based Internet of Things”, Solid State Technology, Volume 64, Issue 2, 2021, . (Scopus Indexed)

A. Arul Anitha and Dr. L. Arockiam, "Ada-IDS: An AdaBoost Intrusion Detection System for ICMPv6 based Attacks in Internet of Things”, International Journal of Advanced Computer Science and Applications, Volume 12, Issue 11, 2021, pp. 499-506. (Scopus and WoS Indexed)

A. Arul Anitha and Dr. L. Arockiam, “A Review on Intrusion Detection System to secure IoT Networks”, International Journal of Computer Network and Applications, Volume 9, Issue 1, 2022, pp. 38-50. (Scopus Indexed)