Anomaly Detection Analysis with Graph-Based Cyber Threat Hunting Scheme

As advanced persistence threats become more prevalent and cyber-attacks become more severe, cyber defense analysts will be required to exert greater effort to protect their systems. A continuous defense mechanism is needed to ensure no incidents occur in the system, one of which is cyber threat hunting. To prove that cyber threat hunting is important, this research simulated a cyber-attack that has successfully entered the system but was not detected by the IDS device even though it already has relatively updated rules. Based on the simulation result, this research designed a data correlation model implemented in a graph visualization with enrichment on-demand features to help analysts conduct cyber threat hunting with graph visualization to detect cyber-attacks. The data correlation model developed in this research can overcome this gap and increase the percentage of detection that was originally undetected / 0% by IDS, to be detected by more than 45% and can even be assessed to be 100% detected based on the anomaly pattern that was successfully found

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

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Visualisasi Serangan Remote To Local (R2L) dengan Clustering K-means

Visualisasi merupakan salah satu teknik untuk meningkatkan akurasi deteksi serangan yang terjadi di jaringan. Visulisasi bertujuan untuk mempermudah dalam mengenali dan menyimpulkan serangan terjadi. Clustering k-means dapat digunakan untuk mendeteksi paket serngan dan paket normal. Serangan remote to local adalah serngan yang dilakukan oleh attacker untuk mendapatkan akses akun ke sebuah sistem yang sebelumnya tidak memiliki akun ke sistem tersebut. Pola serangan R2L pada dataset DARPA dapat dikenali dengan beberapa paramer seperti source address, destination address, flags, ip length, dan tcp length

Ensemble Feature Learning-Based Event Classification for Cyber-Physical Security of the Smart Grid

The power grids are transforming into the cyber-physical smart grid with increasing two-way communications and abundant data flows. Despite the efficiency and reliability promised by this transformation, the growing threats and incidences of cyber attacks targeting the physical power systems have exposed severe vulnerabilities. To tackle such vulnerabilities, intrusion detection systems (IDS) are proposed to monitor threats for the cyber-physical security of electrical power and energy systems in the smart grid with increasing machine-to-machine communication. However, the multi-sourced, correlated, and often noise-contained data, which record various concurring cyber and physical events, are posing significant challenges to the accurate distinction by IDS among events of inadvertent and malignant natures. Hence, in this research, an ensemble learning-based feature learning and classification for cyber-physical smart grid are designed and implemented. The contribution of this research are (i) the design, implementation and evaluation of an ensemble learning-based attack classifier using extreme gradient boosting (XGBoost) to effectively detect and identify attack threats from the heterogeneous cyber-physical information in the smart grid; (ii) the design, implementation and evaluation of stacked denoising autoencoder (SDAE) to extract highlyrepresentative feature space that allow reconstruction of a noise-free input from noise-corrupted perturbations; (iii) the design, implementation and evaluation of a novel ensemble learning-based feature extractors that combine multiple autoencoder (AE) feature extractors and random forest base classifiers, so as to enable accurate reconstruction of each feature and reliable classification against malicious events. The simulation results validate the usefulness of ensemble learning approach in detecting malicious events in the cyber-physical smart grid

Statistical anomaly denial of service and reconnaissance intrusion detection

This dissertation presents the architecture, methods and results of the Hierarchical Intrusion Detection Engine (HIDE) and the Reconnaissance Intrusion Detection System (RIDS); the former is denial-of-service (DoS) attack detector while the latter is a scan and probe (P&S) reconnaissance detector; both are statistical anomaly systems. The HIDE is a packet-oriented, observation-window using, hierarchical, multi-tier, anomaly based network intrusion detection system, which monitors several network traffic parameters simultaneously, constructs a 64-bin probability density function (PDF) for each, statistically compares it to a reference PDF of normal behavior using a similarity metric, then combines the results into an anomaly status vector that is classified by a neural network classifier. Three different data sets have been utilized to test the performance of HIDE; they are OPNET simulation data, DARPA\u2798 intrusion detection evaluation data and the CONEX TESTBED attack data. The results showed that HIDE can reliably detect DoS attacks with high accuracy and very low false alarm rates on all data sets. In particular, the investigation using the DARPA\u2798 data set yielded an overall total misclassification rate of 0.13%, false negative rate of 1.42%, and false positive rate of 0.090%; the latter implies a rate of only about 2.6 false alarms per day. The RIDS is a session oriented, statistical tool, that relies on training to model the parameters of its algorithms, capable of detecting even distributed stealthy reconnaissance attacks. It consists of two main functional modules or stages: the Reconnaissance Activity Profiler (RAP) and the Reconnaissance Alert Correlater (RAC). The RAP is a session-oriented module capable of detecting stealthy scanning and probing attacks, while the RAG is an alert-correlation module that fuses the RAP alerts into attack scenarios and discovers the distributed stealthy attack scenarios. RIDS has been evaluated against two data sets: (a) the DARPA\u2798 data, and (b) 3 weeks of experimental data generated using the CONEX TESTBED network. The RIDS has demonstrably achieved remarkable success; the false positive, false negative and misclassification rates found are low, less than 0.1%, for most reconnaissance attacks; they rise to about 6% for distributed highly stealthy attacks; the latter is a most challenging type of attack, which has been difficult to detect effectively until now

Holistic Network Defense: Fusing Host and Network Features for Attack Classification

This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon information among others. In addition, the work compares three different machine learning algorithms and updates the script required to obtain network features. Hybrid method results outperformed host only classification by 31.7% and network only classification by 25%. The new approach also reduces the number of alerts while remaining accurate compared with the commercial IDS SNORT. These results make it such that even the most typical users could understand alert classification messages