M Nirmala

Bio: M Nirmala is an academic researcher from VIT University. The author has contributed to research in topics: Self-organizing map & Honeypot. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

Comparison between genetic algorithm and self organizing map to detect botnet network traffic

Abstract: In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

Network traffic intelligence using a low interaction honeypot

Abstract: Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a low-interaction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot-honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the network.

Machine Learning Algorithms for Network Intrusion Detection

Abstract: Network intrusion is a growing threat with potentially severe impacts, which can be damaging in multiple ways to network infrastructures and digital/intellectual assets in the cyberspace The approach most commonly employed to combat network intrusion is the development of attack detection systems via machine learning and data mining techniques These systems can identify and disconnect malicious network traffic, thereby helping to protect networks This chapter systematically reviews two groups of common intrusion detection systems using fuzzy logic and artificial neural networks, and evaluates them by utilizing the widely used KDD 99 benchmark dataset Based on the findings, the key challenges and opportunities in addressing cyberattacks using artificial intelligence techniques are summarized and future work suggested

Bots a New Evolution of Robots: A Survey

Abstract: Today is the age of virtual robots, i.e., bots. Bots are the software or app which runs automated job or task over the internet. A surprising change made by AI (Artificial Intelligence) in different fields is automation. Developing of bots is one of them. Different AI techniques are used to develop different bots. This paper will try to explore different bots based on AI algorithms and try to know why the specific algorithms used in bots and also sketch a view of different types of bots in growing demand for automation.