Application of Machine Learning Approaches in Intrusion Detection System

The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offers a healthy market climate and prevents misgivings in the network. Recently, IDS is used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aims to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has checked with the Weak software. In comparison of techniques such as J48, SVM and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%)

A neural-visualization IDS for honeynet data

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzedRegional Government of Gipuzkoa, the Department of Research, Education and Universities of the Basque Government, and the Spanish Ministry of Science and Innovation (MICINN) under projects TIN2010-21272-C02-01 and CIT-020000-2009-12 (funded by the European Regional Development Fund). This work was also supported in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by the Operational Program 'Research and Development for Innovations' funded through the Structural Funds of the European Union and the state budget of the Czech RepublicElectronic version of an article published as International Journal of Neural Systems, Volume 22, Issue 02, April 2012 10.1142/S0129065712500050 ©copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijn

Resilient and Trustworthy Dynamic Data-driven Application Systems (DDDAS) Services for Crisis Management Environments

Future crisis management systems needresilient and trustworthy infrastructures to quickly develop reliable applications and processes, andensure end-to-end security, trust, and privacy. Due to the multiplicity and diversity of involved actors, volumes of data, and heterogeneity of shared information;crisis management systems tend to be highly vulnerable and subjectto unforeseen incidents. As a result, the dependability of crisis management systems can be at risk. This paper presents a cloud-based resilient and trustworthy infrastructure (known as rDaaS) to quickly develop secure crisis management systems. The rDaaS integrates the Dynamic Data-Driven Application Systems (DDDAS) paradigm into a service-oriented architecture over cloud technology and provides a set of resilient DDDAS-As-A Service (rDaaS) components to build secure and trusted adaptable crisis processes. The rDaaS also ensures resilience and security by obfuscating the execution environment and applying Behavior Software Encryption and Moving Technique Defense. A simulation environment for a nuclear plant crisis management case study is illustrated to build resilient and trusted crisis response processes

DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

Towards a Hierarchical Approach for Outlier Detection in Industrial Production Settings

In the context of Industry 4.0, the degree of cross-linking between machines, sensors, and production lines increases rapidly.However, this trend also offers the potential for the improve-ment of outlier scores, especially by combining outlier detectioninformation between different production levels. The latter, in turn, offer various other useful aspects like different time series resolutions or context variables. When utilizing these aspects, valuable outlier information can be extracted, which can be then used for condition-based monitoring, alert management, or predictive maintenance. In this work, we compare different types of outlier detection methods and scores in the light of the aforementioned production levels with the goal to develop a modelfor outlier detection that incorporates these production levels.The proposed model, in turn, is basically inspired by a use casefrom the field of additive manufacturing, which is also known asindustrial 3D-printing. Altogether, our model shall improve the detection of outliers by the use of a hierarchical structure that utilizes production levels in industrial scenarios

Developing Cyberspace Data Understanding: Using CRISP-DM for Host-based IDS Feature Mining

Current intrusion detection systems generate a large number of specific alerts, but do not provide actionable information. Many times, these alerts must be analyzed by a network defender, a time consuming and tedious task which can occur hours or days after an attack occurs. Improved understanding of the cyberspace domain can lead to great advancements in Cyberspace situational awareness research and development. This thesis applies the Cross Industry Standard Process for Data Mining (CRISP-DM) to develop an understanding about a host system under attack. Data is generated by launching scans and exploits at a machine outfitted with a set of host-based data collectors. Through knowledge discovery, features are identified within the data collected which can be used to enhance host-based intrusion detection. By discovering relationships between the data collected and the events, human understanding of the activity is shown. This method of searching for hidden relationships between sensors greatly enhances understanding of new attacks and vulnerabilities, bolstering our ability to defend the cyberspace domain

Artificial Intelligence and Machine Learning in Cybersecurity: Applications, Challenges, and Opportunities for MIS Academics

The availability of massive amounts of data, fast computers, and superior machine learning (ML) algorithms has spurred interest in artificial intelligence (AI). It is no surprise, then, that we observe an increase in the application of AI in cybersecurity. Our survey of AI applications in cybersecurity shows most of the present applications are in the areas of malware identification and classification, intrusion detection, and cybercrime prevention. We should, however, be aware that AI-enabled cybersecurity is not without its drawbacks. Challenges to AI solutions include a shortage of good quality data to train machine learning models, the potential for exploits via adversarial AI/ML, and limited human expertise in AI. However, the rewards in terms of increased accuracy of cyberattack predictions, faster response to cyberattacks, and improved cybersecurity make it worthwhile to overcome these challenges. We present a summary of the current research on the application of AI and ML to improve cybersecurity, challenges that need to be overcome, and research opportunities for academics in management information systems

Intrusion alert prioritisation and attack detection using post-correlation analysis

Event Correlation used to be a widely used technique for interpreting alert logs and discovering network attacks. However, due to the scale and complexity of today's networks and attacks, alert logs produced by these modern networks are much larger in volume and difficult to analyse. In this research we show that adding post-correlation methods can be used alongside correlation to significantly improve the analysis of alert logs. We proposed a new framework titled A Comprehensive System for Analysing Intrusion Alerts (ACSAnIA). The post-correlation methods include a new prioritisation metric based on anomaly detection and a novel approach to clustering events using correlation knowledge. One of the key benefits of the framework is that it significantly reduces false-positive alerts and it adds contextual information to true-positive alerts. We evaluated the post-correlation methods of ACSAnIA using data from a 2012 cyber range experiment carried out by industrial partners of the British Telecom Security Practice Team. In one scenario, our results show that false-positives were successfully reduced by 97% and in another scenario, 16%. It also showed that clustering correlated alerts aided in attack detection. The proposed framework is also being developed and integrated into a pre-existing Visual Analytic tool developed by the British Telecom SATURN Research Team for the analysis of cyber security data