Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

Building accurate radio environment maps from multi-fidelity spectrum sensing data

In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated

Packet analysis for network forensics: A comprehensive survey

Packet analysis is a primary traceback technique in network forensics, which, providing that the packet details captured are sufficiently detailed, can play back even the entire network traffic for a particular point in time. This can be used to find traces of nefarious online behavior, data breaches, unauthorized website access, malware infection, and intrusion attempts, and to reconstruct image files, documents, email attachments, etc. sent over the network. This paper is a comprehensive survey of the utilization of packet analysis, including deep packet inspection, in network forensics, and provides a review of AI-powered packet analysis methods with advanced network traffic classification and pattern identification capabilities. Considering that not all network information can be used in court, the types of digital evidence that might be admissible are detailed. The properties of both hardware appliances and packet analyzer software are reviewed from the perspective of their potential use in network forensics

Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning

Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms. One can access online banking or even make payments on a smartwatch without a paired phone. This makes smartwatches more attractive and vulnerable to malicious attacks, which to date have been largely overlooked. In this paper, we demonstrate Snoopy, a password extraction and inference system which is able to accurately infer passwords entered on Android/Apple watches within 20 attempts, just by eavesdropping on motion sensors. Snoopy uses a uniform framework to extract the segments of motion data when passwords are entered, and uses novel deep neural networks to infer the actual passwords. We evaluate the proposed Snoopy system in the real-world with data from 362 participants and show that our system offers a ~ 3-fold improvement in the accuracy of inferring passwords compared to the state-of-the-art, without consuming excessive energy or computational resources. We also show that Snoopy is very resilient to user and device heterogeneity: it can be trained on crowd-sourced motion data (e.g. via Amazon Mechanical Turk), and then used to attack passwords from a new user, even if they are wearing a different model. This paper shows that, in the wrong hands, Snoopy can potentially cause serious leaks of sensitive information. By raising awareness, we invite the community and manufacturers to revisit the risks of continuous motion sensing on smart wearable devices

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

Frictionless Authentication Systems: Emerging Trends, Research Challenges and Opportunities

Authentication and authorization are critical security layers to protect a wide range of online systems, services and content. However, the increased prevalence of wearable and mobile devices, the expectations of a frictionless experience and the diverse user environments will challenge the way users are authenticated. Consumers demand secure and privacy-aware access from any device, whenever and wherever they are, without any obstacles. This paper reviews emerging trends and challenges with frictionless authentication systems and identifies opportunities for further research related to the enrollment of users, the usability of authentication schemes, as well as security and privacy trade-offs of mobile and wearable continuous authentication systems.Comment: published at the 11th International Conference on Emerging Security Information, Systems and Technologies (SECURWARE 2017

Machine Learning Based Detection of False Data Injection Attacks in Wide Area Monitoring Systems

The Smart Grid (SG) is an upgraded, intelligent, and a more reliable version of the traditional Power Grid due to the integration of information and communication technologies. The operation of the SG requires a dense communication network to link all its components. But such a network renders it prone to cyber attacks jeopardizing the integrity and security of the communicated data between the physical electric grid and the control centers. One of the most prominent components of the SG are Wide Area Monitoring Systems (WAMS). WAMS are a modern platform for grid-wide information, communication, and coordination that play a major role in maintaining the stability of the grid against major disturbances. In this thesis, an anomaly detection framework is proposed to identify False Data Injection (FDI) attacks in WAMS using different Machine Learning (ML) and Deep Learning (DL) techniques, i.e., Deep Autoencoders (DAE), Long-Short Term Memory (LSTM), and One-Class Support Vector Machine (OC-SVM). These algorithms leverage diverse, complex, and high-volume power measurements coming from communications between different components of the grid to detect intelligent FDI attacks. The injected false data is assumed to target several major WAMS monitoring applications, such as Voltage Stability Monitoring (VSM), and Phase Angle Monitoring (PAM). The attack vector is considered to be smartly crafted based on the power system data, so that it can pass the conventional bad data detection schemes and remain stealthy. Due to the lack of realistic attack data, machine learning-based anomaly detection techniques are used to detect FDI attacks. To demonstrate the impact of attacks on the realistic WAMS traffic and to show the effectiveness of the proposed detection framework, a Hardware-In-the-Loop (HIL) co-simulation testbed is developed. The performance of the implemented techniques is compared on the testbed data using different metrics: Accuracy, F1 score, and False Positive Rate (FPR) and False Negative Rate (FNR). The IEEE 9-bus and IEEE 39-bus systems are used as benchmarks to investigate the framework scalability. The experimental results prove the effectiveness of the proposed models in detecting FDI attacks in WAMS