The Proceedings of 14th Australian Digital Forensics Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia

Conference Foreword This is the fifth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 11 papers were submitted and following a double blind peer review process, 8 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, and I would like to take this opportunity to thank the conference committee for their tireless efforts in this regard. These efforts have included but not been limited to the reviewing and editing of the conference papers, and helping with the planning, organisation and execution of the conference. Particular thanks go to those international reviewers who took the time to review papers for the conference, irrespective of the fact that they are unable to attend this year. To our sponsors and supporters a vote of thanks for both the financial and moral support provided to the conference. Finally, to the student volunteers and staff of the ECU Security Research Institute, your efforts as always are appreciated and invaluable. Yours sincerely, Conference Chair Professor Craig Valli Director, Security Research Institut

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table

MACHINE LEARNING STATISTICAL DETECTION OF ANOMALIES USING NETFLOW RECORDS

NetFlow is a network protocol system that is used to represent an overall summary of computer network conversations. A NetFlow record can convert previously captured packet captures or obtain NetFlow session data in real time. This research examines the use of machine-learning techniques to identify anomalies in NetFlow records and classify malware behavior for further investigation. The intent is to identify low-cost solutions leveraging open-source software capable of deployment on computer hardware of currently in-use data networks. This work seeks to determine whether expert selection of features can improve machine-learning detection algorithm performance and evaluate the trade-offs associated with eliminating redundant or excessive numbers of features. We identify the Random Forest algorithm as the strongest single algorithm across three of four metrics, with our chosen NetFlow features cutting the testing and training times in half while incurring minor reductions in two metrics. The experiment demonstrates that the chosen NetFlow features are sufficiently discriminative to detect attacks with a success rate higher than 94%.NCWDGLieutenant, United States NavyApproved for public release. Distribution is unlimited

Optimized Monitoring and Detection of Internet of Things resources-constraints Cyber Attacks

This research takes place in the context of the optimized monitoring and detec- tion of Internet of Things (IoT) resource-constraints attacks. Meanwhile, the In- ternet of Everything (IoE) concept is presented as a wider extension of IoT. How- ever, the IoE realization meets critical challenges, including the limited network coverage and the limited resources of existing network technologies and smart devices. The IoT represents a network of embedded devices that are uniquely identifiable and have embedded software required to communicate between the transient states. The IoT enables a connection between billions of sensors, actu- ators, and even human beings to the Internet, creating a wide range of services, some of which are mission-critical. However, IoT networks are faulty; things are resource-constrained in terms of energy and computational capabilities. For IoT systems performing a critical mission, it is crucial to ensure connectivity, availability, and device reliability, which requires proactive device state moni- toring. This dissertation presents an approach to optimize the monitoring and detection of resource-constraints attacks in IoT and IoE smart devices. First, it has been shown that smart devices suffer from resource-constraints problems; therefore, using lightweight algorithms to detect and mitigate the resource-constraints at- tack is essential. Practical analysis and monitoring of smart device resources’ are included and discussed to understand the behaviour of the devices before and after attacking real smart devices. These analyses are straightforwardly extended for building lightweight detection and mitigation techniques against energy and memory attacks. Detection of energy consumption attacks based on monitoring the package reception rate of smart devices is proposed to de- tect energy attacks in smart devices effectively. The proposed lightweight algo- rithm efficiently detects energy attacks for different protocols, e.g., TCP, UDP, and MQTT. Moreover, analyzing memory usage attacks is also considered in this thesis. Therefore, another lightweight algorithm is also built to detect the memory-usage attack once it appears and stops. This algorithm considers mon- itoring the memory usage of the smart devices when the smart devices are Idle, Active, and Under attack. Based on the presented methods and monitoring analysis, the problem of resource-constraint attacks in IoT systems is systemat- ically eliminated by parameterizing the lightweight algorithms to adapt to the resource-constraint problems of the smart devices

INTRUSION PREDICTION SYSTEM FOR CLOUD COMPUTING AND NETWORK BASED SYSTEMS

Cloud computing offers cost effective computational and storage services with on-demand scalable capacities according to the customers’ needs. These properties encourage organisations and individuals to migrate from classical computing to cloud computing from different disciplines. Although cloud computing is a trendy technology that opens the horizons for many businesses, it is a new paradigm that exploits already existing computing technologies in new framework rather than being a novel technology. This means that cloud computing inherited classical computing problems that are still challenging. Cloud computing security is considered one of the major problems, which require strong security systems to protect the system, and the valuable data stored and processed in it. Intrusion detection systems are one of the important security components and defence layer that detect cyber-attacks and malicious activities in cloud and non-cloud environments. However, there are some limitations such as attacks were detected at the time that the damage of the attack was already done. In recent years, cyber-attacks have increased rapidly in volume and diversity. In 2013, for example, over 552 million customers’ identities and crucial information were revealed through data breaches worldwide [3]. These growing threats are further demonstrated in the 50,000 daily attacks on the London Stock Exchange [4]. It has been predicted that the economic impact of cyber-attacks will cost the global economy $3 trillion on aggregate by 2020 [5]. This thesis focused on proposing an Intrusion Prediction System that is capable of sensing an attack before it happens in cloud or non-cloud environments. The proposed solution is based on assessing the host system vulnerabilities and monitoring the network traffic for attacks preparations. It has three main modules. The monitoring module observes the network for any intrusion preparations. This thesis proposes a new dynamic-selective statistical algorithm for detecting scan activities, which is part of reconnaissance that represents an essential step in network attack preparation. The proposed method performs a statistical selective analysis for network traffic searching for an attack or intrusion indications. This is achieved by exploring and applying different statistical and probabilistic methods that deal with scan detection. The second module of the prediction system is vulnerabilities assessment that evaluates the weaknesses and faults of the system and measures the probability of the system to fall victim to cyber-attack. Finally, the third module is the prediction module that combines the output of the two modules and performs risk assessments of the system security from intrusions prediction. The results of the conducted experiments showed that the suggested system outperforms the analogous methods in regards to performance of network scan detection, which means accordingly a significant improvement to the security of the targeted system. The scanning detection algorithm has achieved high detection accuracy with 0% false negative and 50% false positive. In term of performance, the detection algorithm consumed only 23% of the data needed for analysis compared to the best performed rival detection method

Securing the Internet of Things: A Study on Machine Learning-Based Solutions for IoT Security and Privacy Challenges

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy

NetSentry: A deep learning approach to detecting incipient large-scale network attacks

Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden in data streams. These approaches are however routinely validated with data collected in the same environment, and their performance degrades when deployed in different network topologies and/or applied on previously unseen traffic, as we uncover. This suggests malicious/benign behaviors are largely learned superficially and ML-based Network Intrusion Detection System (NIDS) need revisiting, to be effective in practice. In this paper we dive into the mechanics of large-scale network attacks, with a view to understanding how to use ML for Network Intrusion Detection (NID) in a principled way. We reveal that, although cyberattacks vary significantly in terms of payloads, vectors and targets, their early stages, which are critical to successful attack outcomes, share many similarities and exhibit important temporal correlations. Therefore, we treat NID as a time-sensitive task and propose NetSentry, perhaps the first of its kind NIDS that builds on Bidirectional Asymmetric LSTM (Bi-ALSTM), an original ensemble of sequential neural models, to detect network threats before they spread. We cross-evaluate NetSentry using two practical datasets, training on one and testing on the other, and demonstrate F1 score gains above 33% over the state-of-the-art, as well as up to 3 times higher rates of detecting attacks such as XSS and web bruteforce. Further, we put forward a novel data augmentation technique that boosts the generalization abilities of a broad range of supervised deep learning algorithms, leading to average F1 score gains above 35%