Abhijit S. Pande

Bio: Abhijit S. Pande is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 11 citations.

Network Attacks and Their Detection Mechanisms: A Review

Abstract: With the development of large open networks, security threats for the network have increased significantly in the past few years. Different types of attacks possess different types of threats to network and network resources. Many different detection mechanisms have been proposed by various researchers. This paper reviews different type of possible network attacks and detection mechanisms proposed by various researchers that are capable of detecting such attacks. General Terms Network resources, open network, security threats for network

A Two-Level Hybrid Model for Anomalous Activity Detection in IoT Networks

Abstract: In this paper we propose a two-level hybrid anomalous activity detection model for intrusion detection in IoT networks. The level-1 model uses flow-based anomaly detection, which is capable of classifying the network traffic as normal or anomalous. The flow-based features are extracted from the CICIDS2017 and UNSW-15 datasets. If an anomaly activity is detected then the flow is forwarded to the level-2 model to find the category of the anomaly by deeply examining the contents of the packet. The level-2 model uses Recursive Feature Elimination (RFE) to select significant features and Synthetic Minority Over-Sampling Technique (SMOTE) for oversampling and Edited Nearest Neighbors (ENN) for cleaning the CICIDS2017 and UNSW-15 datasets. Our proposed model precision, recall and F score for level-1 were measured 100% for the CICIDS2017 dataset and 99% for the UNSW-15 dataset, while the level-2 model precision, recall, and F score were measured at 100 % for the CICIDS2017 dataset and 97 % for the UNSW-15 dataset. The predictor we introduce in this paper provides a solid framework for the development of malicious activity detection in IoT networks.

Review of Detection DDOS Attack Detection Using Naive Bayes Classifier for Network Forensics

Abstract: Di s tributed Denial of Service (DDoS) is a type of attack using the volume, intensity, and m ore costs m itigation to increase in this era . A ttack ers used many zombie computers to exhaust the resources available to a network, application or service so that authorize users cannot gain access or the network service is down, and it is a great loss for Internet users in computer networks affected by DDoS attacks. In the Network Forensic, a crime that occurs in the system network services can be sued in the court and the attackers will be punished in accordance with law. This research has the goal to develop a new approach to detect DDoS attacks based on network traffic activity were statistically analyzed using Naive Bayes method. Data were taken from the training and testing of network traffic in a core router in Master of Information Technology Research Laboratory University of Ahmad Dahlan Yogyakarta. The new approach in detecting DDoS attacks is expected to be a relation with Intrusion Detection System (IDS) to predict the existence of DDoS attacks.

An Extensive Research on Cyber Threats using Learning Algorithm

Abstract: Quotidian, the perspective of cyber attacks. New malware divergent are provoked almost daily, the number of attacks is up by 56%. Many see cyber-security alliterative in the form of machine learning and artificial intelligence ability crease into modern-day tools and platforms. This research elucidates a literature anatomization of cyber threads attacks. In this paper, we cover a wide array of cyber-attack types including malware, threats, spam. There by learning techniques are been imparted for attack detection and mitigation in network system.

Intrusion Detection System using Metaheuristic Algorithm

Abstract: In most network management systems, the Intrusion Detection System (IDS) is utilized to detect and prevent suspicious activity. IDS fundamental concept is to use feature values from the capture function of the network packet to classify when an action is irregular. The bulk of conventional classification algorithms, however, are unable to classify unfamiliar behaviors. As a classification algorithm for IDS, the algorithm proposed in this paper would combine clustering, classification, and metaheuristic algorithms to create a high-performance classification algorithm named search economics with k-means support vector machine (SEKS) and search economics with the intrusion detection system (SEIDS). The IDS technique might improve the attack detection accuracy rate by combining methods for unsupervised learning and classification. SEKS and SEIDS are two phases that can be separated from the proposed system. In addition, the suggested algorithm’s hybrid methodology aims to improve the accuracy of detecting abnormal activity in such a system, reduce the classification algorithm’s processing time, and allow the IDS in a network context to detect unfamiliar and novel variant assaults. In terms of accuracy, the suggested approach beats all previous classification methods examined in this research.

Denial of Service (DoS) attack identification and analyse using sniffing technique in the network environment

Abstract: Network-based intruders such as (DoS) attacks have become one of the most significant internet interruptions. Some operations that rely on the internet, such as banking transactions, education, trade marketing, and social networking, have become the primary targets. The attacker is trying to surround and making it difficult for the system to defend. The research's objective is to recognize the characteristics and level of DoS attacks. In understanding the behavior of intruders against a target web server, Wireshark was used in all traffic networks—capturing the traffic in a networked environment. In this research, the user identifies the attack levels (TCP SYN, UDP, and HTTP protocol), ranging from low (Q1), medium (Q2), and high (Q4) attacks. The approach is to simulate the TCP, HTTP, and UDP flood attacks and analyze the attacks' effects on the network environment. In this work, normal scenarios and pattern attacks were compared. In this case, the intruder floods unwanted packets to the victim with a massive number of request packets; the SYN from the corresponding SYN-ACK replies are not achieved. This paper will identify the DoS attacks level and analyze the behavior of traffics.