8 research outputs found
SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning
Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are
increasingly being deployed across multiple functionalities, ranging from
healthcare devices and wearables to critical infrastructures, e.g., nuclear
power plants, autonomous vehicles, smart cities, and smart homes. These devices
are inherently not secure across their comprehensive software, hardware, and
network stacks, thus presenting a large attack surface that can be exploited by
hackers. In this article, we present an innovative technique for detecting
unknown system vulnerabilities, managing these vulnerabilities, and improving
incident response when such vulnerabilities are exploited. The novelty of this
approach lies in extracting intelligence from known real-world CPS/IoT attacks,
representing them in the form of regular expressions, and employing machine
learning (ML) techniques on this ensemble of regular expressions to generate
new attack vectors and security vulnerabilities. Our results show that 10 new
attack vectors and 122 new vulnerability exploits can be successfully generated
that have the potential to exploit a CPS or an IoT ecosystem. The ML
methodology achieves an accuracy of 97.4% and enables us to predict these
attacks efficiently with an 87.2% reduction in the search space. We demonstrate
the application of our method to the hacking of the in-vehicle network of a
connected car. To defend against the known attacks and possible novel exploits,
we discuss a defense-in-depth mechanism for various classes of attacks and the
classification of data targeted by such attacks. This defense mechanism
optimizes the cost of security measures based on the sensitivity of the
protected resource, thus incentivizing its adoption in real-world CPS/IoT by
cybersecurity practitioners.Comment: This article has been accepted in IEEE Transactions on Emerging
Topics in Computing. 17 pages, 12 figures, IEEE copyrigh
Hybrid Deep Learning for Botnet Attack Detection in the Internet of Things Networks
Deep Learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained IoT devices. In this paper, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of Long Short-Term Memory Autoencoder (LAE). In order to classify network traffic samples correctly, we analyse the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep Bidirectional Long Short-Term Memory (BLSTM). Extensive experiments are performed with the BoT-IoT dataset to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92-27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model under-fitting and over-fitting. It also achieves good generalisation ability in binary and multi-class classification scenarios
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
Recently, tremendous interest has been devoted to develop data fusion
strategies for energy efficiency in buildings, where various kinds of
information can be processed. However, applying the appropriate data fusion
strategy to design an efficient energy efficiency system is not
straightforward; it requires a priori knowledge of existing fusion strategies,
their applications and their properties. To this regard, seeking to provide the
energy research community with a better understanding of data fusion strategies
in building energy saving systems, their principles, advantages, and potential
applications, this paper proposes an extensive survey of existing data fusion
mechanisms deployed to reduce excessive consumption and promote sustainability.
We investigate their conceptualizations, advantages, challenges and drawbacks,
as well as performing a taxonomy of existing data fusion strategies and other
contributing factors. Following, a comprehensive comparison of the
state-of-the-art data fusion based energy efficiency frameworks is conducted
using various parameters, including data fusion level, data fusion techniques,
behavioral change influencer, behavioral change incentive, recorded data,
platform architecture, IoT technology and application scenario. Moreover, a
novel method for electrical appliance identification is proposed based on the
fusion of 2D local texture descriptors, where 1D power signals are transformed
into 2D space and treated as images. The empirical evaluation, conducted on
three real datasets, shows promising performance, in which up to 99.68%
accuracy and 99.52% F1 score have been attained. In addition, various open
research challenges and future orientations to improve data fusion based energy
efficiency ecosystems are explored