A hybrid intrusion detection system

Anomaly intrusion detection normally has high false alarm rates, and a high volume of false alarms will prevent system administrators identifying the real attacks. Machine learning methods provide an effective way to decrease the false alarm rate and improve the detection rate of anomaly intrusion detection. In this research, we propose a novel approach using kernel methods and Support Vector Machine (SVM) for improving anomaly intrusion detectors\u27 accuracy. Two kernels, STIDE kernel and Markov Chain kernel, are developed specially for intrusion detection applications. The experiments show the STIDE and Markov Chain kernel based two class SVM anomaly detectors have better accuracy rate than the original STIDE and Markov Chain anomaly detectors.;Generally, anomaly intrusion detection approaches build normal profiles from labeled training data. However, labeled training data for intrusion detection is expensive and not easy to obtain. We propose an anomaly detection approach, using STIDE kernel and Markov Chain kernel based one class SVM, that does not need labeled training data. To further increase the detection rate and lower the false alarm rate, an approach of integrating specification based intrusion detection with anomaly intrusion detection is also proposed.;This research also establish a platform which generates automatically both misuse and anomaly intrusion detection software agents. In our method, a SIFT representing an intrusion is automatically converted to a Colored Petri Net (CPNs) representing an intrusion detection template, subsequently, the CPN is compiled into code for misuse intrusion detection software agents using a compiler and dynamically loaded and launched for misuse intrusion detection. On the other hand, a model representing a normal profile is automatically generated from training data, subsequently, an anomaly intrusion detection agent which carries this model is generated and launched for anomaly intrusion detection. By engaging both misuse and anomaly intrusion detection agents, our system can detect known attacks as well as novel unknown attacks

Data mining approaches for detecting intrusion using UNIX process execution traces

Intrusion detection systems help computer systems prepare for and deal with malicious attacks. They collect information from a variety of systems and network sources, then analyze the information for signs of intrusion and misuse. A variety of techniques have been employed to analyze the information from traditional statistical methods to new emerged data mining approaches. In this thesis, we describe several algorithms designed for this task, including neural networks, rule induction with C4.5, and Rough sets methods. We compare the classification accuracy of the various methods in a set of UNIX process execution traces. We used two kinds of evaluation methods. The first evaluation criterion characterizes performances over a set of individual classifications in terms of average testing accuracy rate. The second measures the true and false positive rates of the classification output over certain threshold. Experiments were run on data sets of system calls created by synthetic sendmail programs. There were two types of representation methods used. Different combinations of parameters were tested during the experiment. Results indicate that for a wide range of conditions, Rough sets have higher classification accuracy than that of Neural networks and C4.5. In terms of true and false positive evaluations, Rough sets and Neural networks turned out to be better than C4.5

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

A Costing Framework for the Dynamic Computational Efficiency of the Network Security Detection Function

This study developed a comprehensive framework to systematically evaluate the economic implications of security policy implementation in IT-centric business processes. Focusing on the detection aspect of the NIST cybersecurity framework, the research explored the interrelation between business operations, computational efficiency, and security protocols. The framework comprises nine components, addressing the gap between cost projections and security policy enforcement. The insights provided valuable perspectives on managing security expenses and resource allocation in information security, ensuring alignment with revenue and expenditure outcomes while emphasizing the need for a comprehensive approach to cost management in information security management

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

The progress made by levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology is described. Emphasis is placed upon the Space Station Freedom Program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) progress report 10, the flight telerobotic servicer, and the Advanced Development Program. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom

WiFi Miner: An online apriori and sensor based wireless network Intrusion Detection System

This thesis proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to wireless network packets captured through hardware sensors for purposes of real time detection of intrusive or anomalous packets. Contributions of the proposed system includes effectively adapting an efficient data mining association rule technique to important problem of intrusion detection in a wireless network environment using hardware sensors, providing a solution that eliminates the need for hard-to-obtain training data in this environment, providing increased intrusion detection rate and reduction of false alarms. The proposed system, WiFi Miner, solution approach is to find frequent and infrequent patterns on pre-processed wireless connection records using infrequent pattern finding Apriori algorithm also proposed by this thesis. The proposed Online Apriori-Infrequent algorithm improves the join and prune step of the traditional Apriori algorithm with a rule that avoids joining itemsets not likely to produce frequent itemsets as their results, thereby improving efficiency and run times significantly. A positive anomaly score is assigned to each packet (record) for each infrequent pattern found while a negative anomaly score is assigned for each frequent pattern found. So, a record with final positive anomaly score is considered as anomaly based on the presence of more infrequent patterns than frequent patterns found

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Improving Intrusion Prevention, Detection and Response

Merged with duplicate record 10026.1/479 on 10.04.2017 by CS (TIS)In the face of a wide range of attacks. Intrusion Detection Systems (IDS) and other Internet security tools represent potentially valuable safeguards to identify and combat the problems facing online systems. However, despite the fact that a variety o f commercial and open source solutions are available across a range of operating systems and network platforms, it is notable that the deployment of IDS is often markedly less than other well-known network security countermeasures and other tools may often be used in an ineffective manner. This thesis considers the challenges that users may face while using IDS, by conducting a web-based questionnaire to assess these challenges. The challenges that are used in the questionnaire were gathered from the well-established literature. The participants responses varies between being with or against selecting them as challenges but all the listed challenges approved that they are consider problems in the IDS field. The aim of the research is to propose a novel set of Human Computer Interaction-Security (HCI-S) usability criteria based on the findings of the web-based questionnaire. Moreover, these criteria were inspired from previous literature in the field of HCI. The novelty of the criteria is that they focus on the security aspects. The new criteria were promising when they were applied to Norton 360, a well known Internet security suite. Testing the alerts issued by security software was the initial step before testing other security software. Hence, a set of security software were selected and some alerts were triggered as a result of performing a penetration test conducted within a test-bed environment using the network scanner Nmap. The findings reveal that four of the HCI-S usability criteria were not fully addressed by all of these security software. Another aim of this thesis is to consider the development of a prototype to address the HCI-S usability criteria that seem to be overlooked in the existing security solutions. The thesis conducts a practical user trial and the findings are promising and attempt to find a proper solution to solve this problem. For instance, to take advantage of previous security decisions, it would be desirable for a system to consider the user's previous decisions on similar alerts, and modify alerts accordingly to account for the user's previous behaviour. Moreover, in order to give users a level of fiexibility, it is important to enable them to make informed decisions, and to be able to recover from them if needed. It is important to address the proposed criteria that enable users to confirm / recover the impact of their decision, maintain an awareness of system status all the time, and to offer responses that match users' expectations. The outcome of the current study is a set of a proposed 16 HCI-S usability criteria that can be used to design and to assess security alerts issued by any Internet security suite. These criteria are not equally important and they vary between high, medium and low.The embassy of the arab republic of Egypt (cultural centre & educational bureau) in Londo