Computer Network Attack Detection Using Enhanced Clustering Technologies

The need for security means has brought from the fact of privacy of data especially after the communication revolution in the recent times. The advancement of data mining and machine learning technology has paved the road for establishment an efficient attack prediction paradigm for protecting of large scaled networks. In this project, computer network intrusions had been eliminated by using smart machine learning algorithm. Referring a big dataset named as KDD computer intrusion dataset which includes large number of connections that diagnosed with several types of attacks; the model is established for predicting the type of attack by learning through this data. Feed forward neural network model is outperformed over the other proposed clustering models in attack prediction accuracy.

Detection of Network Attacks using Machine Learning: A New Approach

The Cyber-attacks become the most important security problems in the today’s world. With the increase in use of computing resources connected to the Internet like computers, mobiles, sensors, IoTs in networks, Big Data, Web Applications/Server, Clouds and other computing resources, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. These intrusions detection techniques have been applied on various IDS datasets. UNSW-NB15 is the latest dataset. This data set contains different modern attack types and wide varieties of real normal activities. In this paper, we compare Naïve Bays algorithm with proposed probability based supervised machine learning algorithms using reduced UNSW NB15 dataset. Keywords: UNSW NB-15, Machine Learning, Naïve Bayes, All to Single (AS) features probability Algorithm

Formal Model of IDS Based on BDI Logic

Computer network security is an important aspect of computer science. Many researchers are trying to increase security using different methods, technologies, or tools. One of the most common practices is the deployment of an Intrusion Detection System (IDS). The current state of IDS brings only passive protection from network intrusions, i.e., IDS can only detect possible intrusions. Due to that, the manual intervention of an administrator is needed. In our paper, we present a logical model of an active IDS based on category theory, coalgebras, linear logic, and Belief–Desire–Intention (BDI) logic. Such an IDS can not only detect intrusions but also autonomously react to them according to a defined security policy. We demonstrate our approach on a motivating example with real network intrusions.

Web-Browsing Application Using Web Scraping Technology in Korean Network Separation Application

Attackers’ intrusion into the Enterprise LAN is increasing every year, and the method is becoming more intelligent and crafty. Various security measures against external network intrusions, such as firewalls, are being studied and applied to protect against external attacks, but it is difficult to respond to increasing attacks. Most institutions block access from the external network for the safety of the internal network and allow access from the internal network to the external network through some restricted ports. In particular, restricted ports in subject to a variety of security techniques to block intrusion into the internal network, but in the process, access to the internal network is only applied by restricted ports, making it inefficient to handle internal requests. Although various studies have been conducted on network isolation to address these challenges, it is difficult to perform tasks efficiently as security functions, such as detecting whether request data is attacked or not, during actual application. The proposed technique is a network-blocking-based network separation technique that converts data from the external network connected to the Internet into symmetry data from which malicious code is removed through an agent and delivers it to the client of the internal network. We propose a technique to provide access.

Network intrusion detection using machine learning

In this research we have studied the use of machine learning techniques in detecting network intrusions. Most research in the field has used the very outdated dataset (KDDCup99) which consists of a set handcrafted features. In our research we present models that work well on both the older dataset and on newer datasets such as ISCX2014 and ISCX2012. We also present methods for extracting features from these datasets. Another issue we found with most research in this field is that they do not study the effect of surges in regular network traffic and how that might affect the model. We put our model to test in 10x traffic and show its effectiveness under these conditions. We also study how semi-supervised models can be used in training NIDS models without directly showing them labeled data.

Network intrusion detection using machine learning

In this research we have studied the use of machine learning techniques in detecting network intrusions. Most research in the field has used the very outdated dataset (KDDCup99) which consists of a set handcrafted features. In our research we present models that work well on both the older dataset and on newer datasets such as ISCX2014 and ISCX2012. We also present methods for extracting features from these datasets. Another issue we found with most research in this field is that they do not study the effect of surges in regular network traffic and how that might affect the model. We put our model to test in 10x traffic and show its effectiveness under these conditions. We also study how semi-supervised models can be used in training NIDS models without directly showing them labeled data.

Image Classifiers for Network Intrusions

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.

Using a Mobile-Based Online Platform to Mentor Girls: Challenges and Benefits

In some parts of Nigeria, many girls do not attend school, and among those sent to school, many still drop out early. This and other socio-cultural factors affect girls psychologically. There is no doubt that girls need consistent love and tutoring to guide them through the turbulent teen years and beyond. They need a mentor who acts as a friend and a role model. The Mobile-based Mentoring Platform seeks to leverage on mobile technology's affordances to focus on the needs of the girl-child, such as improvement in academic achievement, guidance in career choice, development of self-concept, and esteem. The girl-mentees comments revealed that using the platform provided them frequent access to mentors and access to learning opportunities. The challenges they faced include epileptic internet network, intrusions by parents, and others. Therefore, this paper examined the challenges and benefits of mentoring girls via a mentoring platform.

Efficient Anomaly Detection for Smart Hospital IoT Systems

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.