Intelligent network intrusion detection using an evolutionary computation approach

With the enormous growth of users\u27 reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems. Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely remove the threat of network attacks. A good security mechanism requires an Intrusion Detection System (IDS) in order to monitor security breaches when the prevention schemes in the preparation phase are bypassed. To be able to react to network attacks as fast as possible, an automatic detection system is of paramount importance. The later an attack is detected, the less time network administrators have to update their signatures and reconfigure their detection and remediation systems. An IDS is a tool for monitoring the system with the aim of detecting and alerting intrusive activities in networks. These tools are classified into two major categories of signature-based and anomaly-based. A signature-based IDS stores the signature of known attacks in a database and discovers occurrences of attacks by monitoring and comparing each communication in the network against the database of signatures. On the other hand, mechanisms that deploy anomaly detection have a model of normal behaviour of system and any significant deviation from this model is reported as anomaly. This thesis aims at addressing the major issues in the process of developing signature based IDSs. These are: i) their dependency on experts to create signatures, ii) the complexity of their models, iii) the inflexibility of their models, and iv) their inability to adapt to the changes in the real environment and detect new attacks. To meet the requirements of a good IDS, computational intelligence methods have attracted considerable interest from the research community. This thesis explores a solution to automatically generate compact rulesets for network intrusion detection utilising evolutionary computation techniques. The proposed framework is called ESR-NID (Evolving Statistical Rulesets for Network Intrusion Detection). Using an interval-based structure, this method can be deployed for any continuous-valued input data. Therefore, by choosing appropriate statistical measures (i.e. continuous-valued features) of network trafc as the input to ESRNID, it can effectively detect varied types of attacks since it is not dependent on the signatures of network packets. In ESR-NID, several innovations in the genetic algorithm were developed to keep the ruleset small. A two-stage evaluation component in the evolutionary process takes the cooperation of rules into consideration and results into very compact, easily understood rulesets. The effectiveness of this approach is evaluated against several sources of data for both detection of normal and abnormal behaviour. The results are found to be comparable to those achieved using other machine learning methods from both categories of GA-based and non-GA-based methods. One of the significant advantages of ESR-NIS is that it can be tailored to specific problem domains and the characteristics of the dataset by the use of different fitness and performance functions. This makes the system a more flexible model compared to other learning techniques. Additionally, an IDS must adapt itself to the changing environment with the least amount of configurations. ESR-NID uses an incremental learning approach as new flow of traffic become available. The incremental learning approach benefits from less required storage because it only keeps the generated rules in its database. This is in contrast to the infinitely growing size of repository of raw training data required for traditional learning

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

Computer network security is a very serious concern in many commercial, industrial, and military environments. This paper proposes a new computer network security approach defined by self-organized agent swarms (SOMAS) which provides a novel computer network security management framework based upon desired overall system behaviors. The SOMAS structure evolves based upon the partially observable Markov decision process (POMDP) formal model and the more complex Interactive-POMDP and Decentralized-POMDP models, which are augmented with a new F(*-POMDP) model. Example swarm specific and network based behaviors are formalized and simulated. This paper illustrates through various statistical testing techniques, the significance of this proposed SOMAS architecture, and the effectiveness of self-organization and entangled hierarchies

Evaluating the Impacts of Detecting X.509 Covert Channels

This quasi-experimental before-and-after study examined the performance impacts of detecting X.509 covert channels in the Suricata intrusion detection system. Relevant literature and previous studies surrounding covert channels and covert channel detection, X.509 certificates, and intrusion detection system performance were evaluated. This study used Jason Reaves’ X.509 covert channel proof of concept code to generate malicious network traffic for detection (2018). Various detection rules for intrusion detection systems were created to aid in the detection of the X.509 covert channel. The central processing unit (CPU) and memory utilization impacts that each rule had on the intrusion detection system was studied and analyzed. Statistically significant figures found that the rules do have an impact on the performance of the system, some more than others. Finally, pathways towards future related research in creating efficient covert channel detection mechanisms were identified

Using CLIPS to Detect Network Intrusions

We describe how to build a network intrusion detection sensor by slightly modifying NASA's CLIPS source code introducing some new features. An overview of the system is presented emphasizing the strategies used to inter-operate between the packet capture engine written in C and CLIPS. Some extensions were developed in order to manipulate timestamps, multiple string pattern matching and certainty factors. Several Snort functions and plugins were adapted and used for packet decoding and preprocessing. A rule translator was also built to reuse most of the Snort's attack signatures. Despite some performance drawbacks, results prove that CLIPS can be used for real-time network intrusion detection under certain conditions. Several attack signatures using CLIPS rules are showed in the appendix. By mixing CLIPS with Snort features, it was possible to introduce flexibility and expressiveness to network intrusion detection

Evolution of a Hybrid Model for an Effective Perimeter Security Device

Clustering and classification models, or hybrid models are the most widely used models that can handle the diverse nature of NIDS dataset. Dirichlet process clustering technique is a non-parametric Bayesian mixture model that considers the data distribution of the dataset for the formation of distinct clusters. The number of clusters is not known a priori and it differs across different datasets. Determining the number of clusters based on the distribution of data instances can increase the performance of the model. Naive Bayes model, a supervised learning classification technique, maintains a better computational efficiency, by reducing the training time. In this paper, we propose a hybrid model to exploit the positive aspect of proper clustering of data instances and the computational efficiency in building a NIDS. RIPPER algorithm is used to extract rules from the traffic description for updation of the rule database. Experiments were conducted in the KDD CUP’99 and SSENet-2011 datasets to study the performance of the proposed model. Also, a comparison of three hybrid methods with the proposed hybrid model was carried out. The results showed that the proposed hybrid model is superior in building a robust perimeter security device

Modernization of Manufacturing with Cybersecurity at the Forefront

With the proliferation of Industrial Control Systems (ICSs), manufacturing processes have improved over the last 30 years, however, the organizational focus to securely exchange and process information to/from integrated systems has been consistently lacking. These environments continue to be susceptible to security vulnerabilities, despite history [15] showing that cybersecurity exposures in manufacturing have largely gone unaddressed and continue to rise [52]. This study evaluates cybersecurity challenges in the industry and proposes recommendations for practical and fiscally responsible defense-in-depth cybersecurity protections for manufacturing environments. The business operating model, how ICSs became pervasive, as well as the major components that enable the operational technology (OT) were evaluated. With an understanding of the traditional network architecture for the industry [37], the rapidly evolving challenges facing the industry were examined. These challenges are impactful to the traditional and slow to change manufacturing operating model that has not focused on the necessary cyber protections for their OT environments. In addition, the industry is now facing game-changing technological concepts such as advanced manufacturing and Industry 4.0 that bring new complex challenges and cyber threats, unfamiliar to most in the industry. This is all underpinned by an organizational divide where the personnel most knowledgeable with the modern technology and cyber risks, in the majority of cases, are not responsible for the OT architecture and security. These headwinds impact an industry which spends the least on IT and cyber security than any other industry, globally [22]. The cyber risks and challenges in the industry are diverse, spanning technological and organizational competencies, stemming from purpose built components which operate in an ecosystem where cybersecurity is an afterthought. As a means to close the gap, practical and reasonable recommendations to address these problems are discussed; some specific and unique to the manufacturing industry while others are fundamental applications discussed with a manufacturing industry lens, which are commonly ignored due to perceived complexity, cost or simply lack of awareness. Lastly, a number of these recommendations were selected for further evaluation and implementation; challenges, approach, benefits and outcomes are shared showing measureable improvements to the cybersecurity posture of the organization.Master of ScienceComputer and Information Science, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/147433/1/49698122_CIS699 - Mangano Thesis - Modernization of Manufacturing with Cybersecurity at the Forefront - Final 121018-v4.pdfDescription of 49698122_CIS699 - Mangano Thesis - Modernization of Manufacturing with Cybersecurity at the Forefront - Final 121018-v4.pdf : Thesi

A Survey of Botnet Detection Techniques by Command and Control Infrastructure

Botnets have evolved to become one of the most serious threats to the Internet and there is substantial research on both botnets and botnet detection techniques. This survey reviewed the history of botnets and botnet detection techniques. The survey showed traditional botnet detection techniques rely on passive techniques, primarily honeypots, and that honeypots are not effective at detecting peer-to-peer and other decentralized botnets. Furthermore, the detection techniques aimed at decentralized and peer-to-peer botnets focus on detecting communications between the infected bots. Recent research has shown hierarchical clustering of flow data and machine learning are effective techniques for detecting botnet peer-to-peer traffic

Machine Learning and other Computational-Intelligence Techniques for Security Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen