Viewpoint | Personal Data and the Internet of Things: It is time to care about digital provenance

The Internet of Things promises a connected environment reacting to and addressing our every need, but based on the assumption that all of our movements and words can be recorded and analysed to achieve this end. Ubiquitous surveillance is also a precondition for most dystopian societies, both real and fictional. How our personal data is processed and consumed in an ever more connected world must imperatively be made transparent, and more effective technical solutions than those currently on offer, to manage personal data must urgently be investigated.Comment: 3 pages, 0 figures, preprint for Communication of the AC

A Markov model for inferring flows in directed contact networks

Directed contact networks (DCNs) are a particularly flexible and convenient class of temporal networks, useful for modeling and analyzing the transfer of discrete quantities in communications, transportation, epidemiology, etc. Transfers modeled by contacts typically underlie flows that associate multiple contacts based on their spatiotemporal relationships. To infer these flows, we introduce a simple inhomogeneous Markov model associated to a DCN and show how it can be effectively used for data reduction and anomaly detection through an example of kernel-level information transfers within a computer.Comment: 12 page

Querying Streaming System Monitoring Data for Enterprise System Anomaly Detection

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely abnormal system behavior detection over the stream of monitoring data. However, existing stream-based solutions lack explicit language constructs for expressing anomaly models that capture abnormal system behaviors, thus facing challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale monitoring data. To address these limitations, we build SAQL, a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomaly models. SAQL provides a domain-specific query language, Stream-based Anomaly Query Language (SAQL), that uniquely integrates critical primitives for expressing major types of anomaly models. In the demo, we aim to show the complete usage scenario of SAQL by (1) performing an APT attack in a controlled environment, and (2) using SAQL to detect the abnormal behaviors in real time by querying the collected stream of system monitoring data that contains the attack traces. The audience will have the option to interact with the system and detect the attack footprints in real time via issuing queries and checking the query results through a command-line UI.Comment: Accepted paper at ICDE 2020 demonstrations track. arXiv admin note: text overlap with arXiv:1806.0933

A Feasibility Study on the Application of the ScriptGenE Framework as an Anomaly Detection System in Industrial Control Systems

Recent events such as Stuxnet and the Shamoon Aramco have brought to light how vulnerable industrial control systems (ICSs) are to cyber attacks. Modern society relies heavily on critical infrastructure, including the electric power grid, water treatment facilities, and nuclear energy plants. Malicious attempts to disrupt, destroy and disable such systems can have devastating effects on a populations way of life, possibly leading to loss of life. The need to implement security controls in the ICS environment is more vital than ever. ICSs were not originally designed with network security in mind. Today, intrusion detection systems are employed to detect attacks that penetrate the ICS network. This research proposes the use of a novel algorithm known as the ScriptGenE framework as an anomaly-based intrusion detection system. The anomaly detection system (ADS) is implemented between an engineering workstation and programmable logic controller to monitor traffic and alert the operator to anomalous behavior. The ADS achieves true positive rates of 0.9011 and 1.00 with false positive rates of 0 and 0.054. This research demonstrates the viability of using the ScriptGenE framework as an anomaly detection system in a simulated ICS environment