Spatiotemporal Patterns and Predictability of Cyberattacks

Y.C.L. was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-10-1-0083 and Army Research Office (ARO) under grant no. W911NF-14-1-0504. S.X. was supported by Army Research Office (ARO) under grant no. W911NF-13-1-0141. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Bayesian based intrusion detection system

AbstractIn this paper an intrusion detection system is developed using Bayesian probability. The system developed is a naive Bayesian classifier that is used to identify possible intrusions. The system is trained a priori using a subset of the KDD dataset. The trained classifier is then tested using a larger subset of KDD dataset. The Bayesian classifier was able to detect intrusion with a superior detection rate

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements

Dynamic Access Control in Industry 4.0 Systems

Industry 4.0 enacts ad-hoc cooperation between machines, humans, and organizations in supply and production chains. The cooperation goes beyond rigid hierarchical process structures and increases the levels of efficiency, customization, and individualisation of end-products. Efficient processing and cooperation requires exploiting various sensor and process data and sharing them across various entities including computer systems, machines, mobile devices, humans, and organisations. Access control is a common security mechanism to control data sharing between involved parties. However, access control to virtual resources is not sufficient in presence of Industry 4.0 because physical access has a considerable effect on the protection of information and systems. In addition, access control mechanisms have to become capable of handling dynamically changing situations arising from ad-hoc horizontal cooperation or changes in the environment of Industry 4.0 systems. Established access control mechanisms do not consider dynamic changes and the combination with physical access control yet. Approaches trying to address these shortcomings exist but often do not consider how to get information such as the sensitivity of exchanged information. This chapter proposes a novel approach to control physical and virtual access tied to the dynamics of custom product engineering, hence, establishing confidentiality in ad-hoc horizontal processes. The approach combines static design-time analyses to discover data properties with a dynamic runtime access control approach that evaluates policies protecting virtual and physical assets. The runtime part uses data properties derived from the static design-time analysis, as well as the environment or system status to decide about access

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Thin Hypervisor-Based Security Architectures for Embedded Platforms

Virtualization has grown increasingly popular, thanks to its benefits of isolation, management, and utilization, supported by hardware advances. It is also receiving attention for its potential to support security, through hypervisor-based services and advanced protections supplied to guests. Today, virtualization is even making inroads in the embedded space, and embedded systems, with their security needs, have already started to benefit from virtualization’s security potential. In this thesis, we investigate the possibilities for thin hypervisor-based security on embedded platforms. In addition to significant background study, we present implementation of a low-footprint, thin hypervisor capable of providing security protections to a single FreeRTOS guest kernel on ARM. Backed by performance test results, our hypervisor provides security to a formerly unsecured kernel with minimal performance overhead, and represents a first step in a greater research effort into the security advantages and possibilities of embedded thin hypervisors. Our results show that thin hypervisors are both possible and beneficial even on limited embedded systems, and sets the stage for more advanced investigations, implementations, and security applications in the future

Exploiting tactics, techniques, and procedures for malware detection

There has been a meteoric rise in the use of malware to perpetrate cybercrime and more generally, serve the interests of malicious actors. As a result, malware has evolved both in terms of its sheer variety and sophistication. There is hence a need for developing effective malware detection systems to counter this surge. Typically, most such systems nowadays are purely data-driven - they utilise Machine Learning (ML) based approaches which rely on large volumes of data, to spot patterns, detect anomalies, and thus detect malware. In this thesis, we propose a methodology for malware detection on networks that combines human domain knowledge with conventional malware detection approaches to more effectively identify, reason about, and be resilient to malware. Specifically, we use domain knowledge in the form of the Tactics, Techniques, and Procedures (TTPs) described in the MITRE ATT\&CK ontology of adversarial behaviour to build Network Intrusion Detection Systems (NIDS). Through the course of our research, we design and evaluate the first such NIDS that can effectively exploit TTPs for the purpose of malware detection. We then attempt to expand the scope of usability of these TTPs to systems other than our specialised NIDS, and develop a methodology that lets any generic ML-based NIDS exploit these TTPs as model features. We further expand and generalise our approach by modelling it as a multi-label classification problem, which enables us to: (i) detect malware more precisely on the basis of individual TTPs, and (ii) identify the malicious usage of uncommon or rarely-used TTPs. Throughout all our experiments, we rigorously evaluate all our systems on several metrics using large datasets of real-world malware and benign samples. We empirically demonstrate the usefulness of TTPs in the malware detection process, the benefits of a TTP-based approach in reasoning about malware and responding to various challenging conditions, and the overall robustness of our systems to adversarial attack. As a consequence, we establish and improve the state-of-the-art when it comes to detecting network-based malware using TTP-based information. This thesis overall represents a step forward in building automated systems that combine purely-data driven approaches with human expertise in the field of malware analysis

A PUF-based Secure Communication Protocol for IoT

Security features are of paramount importance for IoT, and implementations are challenging given the resource-constrained IoT set-up. We have developed a lightweight identity-based cryptosystem suitable for IoT, to enable secure authentication and message exchange among the devices. Our scheme employs Physically Unclonable Function (PUF), to generate the public identity of each device, which is used as the public key for each device for message encryption. We have provided formal proofs of security in the Session Key security and Universally Composable Framework of the proposed protocol, which demonstrates the resilience of the scheme against passive as well as active attacks. We have demonstrated the set up required for the protocol implementation and shown that the proposed protocol implementation incurs low hardware and software overhead

Trustworthiness in Mobile Cyber Physical Systems

Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS

SoK: Security Models for Pseudo-Random Number Generators

Randomness plays an important role in multiple applications in cryptography. It is required in fundamental tasks such as key generation, masking and hiding values, nonces and initialization vectors generation. Pseudo-random number generators have been studied by numerous authors, either to propose clear security notions and associated constructions or to point out potential vulnerabilities. In this systematization of knowledge paper, we present the three notions of generators that have been successively formalized: standard generators, stateful generators and generators with input. For each notion, we present expected security properties, where adversaries have increasing capabilities (including access to partial information on the internal variables) and we propose secure and efficient constructions, all based on the block cipher AES. In our description of generators with input, we revisit the notions of accumulator and extractor and we point out that security crucially relies on the independence between the randomness source and the seeds of the accumulator and the extractor. To illustrate this requirement, we identify a potential vulnerability of the NIST standard CTR_DRBG