An Artificial Intelligence-based Intrusion Detection System

Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible to define every attack in the database and make the process costly also. Recently, research in conjunction with artificial intelligence and network security has evolved. As a result, it created many possibilities to enable machine learning approaches to detect the new attacks in network traffic. Machine learning has already shown successful results in the domain of recommendation systems, speech recognition, and medical systems. So, in this paper, we utilize machine learning approaches to detect attacks and classify them. This paper uses the CSE-CIC-IDS dataset, which contains normal and malicious attacks samples. Multiple steps are performed to train the network traffic classifier. Finally, the model is deployed for testing on sample data.

Advancements in Encryption Techniques for Enhanced Data Security Over Cloud

With the advancements in internet technologies and increased transactions over the internet, the threats for data security increased many folds than ever. Nowadays message application services are in great demand, as they offered end-to-end encryption (E2EE) that is essential to provide security to the users while communication takes place between parties. Today messaging application service is in great use for communication. For making communication over the network. This paper presents that security is essential while communication takes place between users and how E2EE offers security to the users. Consumers' concerns related to the security and privacy of their data are growing day by day with increased inter-connectivity. We examine the existing mobile message service encryption protocols that provide security and the features which preserve privacy for messenger applications and also evaluate the technical challenges involved in its implementations.

Mitigating Hot Spot Problem in Wireless Sensor Networks using Political Optimizer Based Unequal Clustering Technique

Wireless Sensor Network (WSN) encompasses a set of wirelessly connected sensor nodes in the network for tracking and data gathering applications. The sensors in WSN are constrained in energy, memory, and processing capabilities. Despite the benefits of WSN, the sensors closer to the base station (BS) expels their energy faster. It suffers from hot spot issues and can be resolved by the use of unequal clustering techniques. In this aspect, this paper presents a political optimizer-based unequal clustering scheme (POUCS) for mitigating hot spot problems in WSN. The goal of the POUCS technique is to choose cluster heads (CHs) and determine unequal cluster sizes. The POUCS technique derives a fitness function involving different input parameters to minimize energy consumption and maximize the lifetime of the network. To showcase the enhanced performance of the POUCS technique, a comprehensive experimental analysis takes place, and the detailed comparison study reported the better performance of the POUCS technique over the recent techniques.

Cybersecurity in Networking Devices

Cybersecurity is training defensive arrangements, systems, and plans to save the information from cyber outbreaks. These virtual outbreaks are typically intended to retrieve, alter, or otherwise extinguish delicate data, extracting currency from manipulators, or disturb usual commercial procedures. System Security defends one’s system and information from breaks, interruptions also other intimidations. Network Security contains admission controller, computer virus and defiant computer virus software program, system safety, system analytics, system-connected protection categories, firewalls, and VPN encoding. System substructure strategies stand the mechanisms of a net that conveyance transportations desired intended for information, submissions, facilities, and multimedia. In this paper, we reflect on Cybersecurity in Networking Devices.

A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.

Design of Optimal Machine Learning based Cybersecurity Intrusion Detection Systems

Cybersecurity is the process of protecting critical systems and confidential data from digital attacks. With the advent of machine learning, cybersecurity systems can examine the patterns and learns them from preventing similar attacks and responds to fluctuating behavior. Cybersecurity intrusion detection system helps to detect the existence of intrusions in the network and achieves security in confidential data storage and transmission. In this view, this study designs an efficient cockroach optimization (CSO) with kernel extreme learning machine (KELM) model for cybersecurity intrusion detection. The proposed CSO-KELM model can accomplish cybersecurity by the detection and classification of intrusions. The proposed CSO-KELM technique encompasses a three-level process, namely preprocessing, classification, and parameter tuning. The design of the CSO algorithm for the appropriate selection of KELM parameters results in improved classification performance. For examining the betterment of the CSO-KELM technique, a series of experiments were performed on benchmark datasets. The experimental results pointed out the superiority of the CSO-KELM technique concerning several measures.

Performance Analysis of Machine Learning based Botnet Detection and Classification Models for Information Security

Botnet detection becomes a challenging issue in several domains like cybersecurity, finance, healthcare, law, order, etc. The botnet represents a set of cooperated Internet-linked devices managed by cyber criminals to start coordinated attacks and carry out different malicious events. As the botnets are seamlessly dynamic with the developing countermeasures presented by network and host-based detection schemes, conventional methods have failed to achieve enough safety for botnet threats. Therefore, machine learning (ML) models have been developed to detect and classify botnets for cybersecurity. In this view, this paper performs a comprehensive evaluation of different ML-based botnet detection and classification models. The botnet detection model involves a three-stage process, namely preprocessing, feature extraction, and classification. In this study, four ML models such as C4.5 Decision Tree, bagging, boosting, and Adaboost are employed for classification purposes. To highlight the performance of the four ML models, an extensive set of simulations was performed. The obtained results pointed out that the ML models can attain enhanced botnet detection performance.