Biblio
Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.
A term systems of systems (SoS) refers to a setup in which a number of independent systems collaborate to create a value that each of them is unable to achieve independently. Complexity of a SoS structure is higher compared to its constitute systems that brings challenges in analyzing its critical properties such as security. An SoS can be seen as a set of connected systems or services that needs to be adequately protected. Communication between such systems or services can be considered as a service itself, and it is the paramount for establishment of a SoS as it enables connections, dependencies, and a cooperation. Given that reliable and predictable communication contributes directly to a correct functioning of an SoS, communication as a service is one of the main assets to consider. Protecting it from malicious adversaries should be one of the highest priorities within SoS design and operation. This study aims to investigate the attack propagation problem in terms of service-guarantees through the decomposition into sub-services enriched with preconditions and postconditions at the service levels. Such analysis is required as a prerequisite for an efficient SoS risk assessment at the design stage of the SoS development life cycle to protect it from possibly high impact attacks capable of affecting safety of systems and humans using the system.
With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.
The smart grid is a complex cyber-physical system (CPS) that poses challenges related to scale, integration, interoperability, processes, governance, and human elements. The US National Institute of Standards and Technology (NIST) and its government, university and industry collaborators, developed an approach, called CPS Framework, to reasoning about CPS across multiple levels of concern and competency, including trustworthiness, privacy, reliability, and regulatory. The approach uses ontology and reasoning techniques to achieve a greater understanding of the interdependencies among the elements of the CPS Framework model applied to use cases. This paper demonstrates that the approach extends naturally to automated and manual decision-making for smart grids: we apply it to smart grid use cases, and illustrate how it can be used to analyze grid topologies and address concerns about the smart grid. Smart grid stakeholders, whose decision making may be assisted by this approach, include planners, designers and operators.
Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.
The battlefield environment differs from the natural environment in terms of irregular communications and the possibility of destroying communication and medical units by enemy forces. Information that can be collected in a war environment by soldiers is important information and must reach top-level commanders in time for timely decisions making. Also, ambulance staff in the battlefield need to enter the data of injured soldiers after the first aid, so that the information is available for the field hospital staff to prepare the needs for incoming injured soldiers.In this research, we propose two transaction techniques to handle these issues and use different concurrency control protocols, depending on the nature of the transaction and not a one concurrency control protocol for all types of transactions. Message transaction technique is used to collect valuable data from the battlefield by soldiers and allows top-level commanders to view it according to their permissions by logging into the system, to help them make timely decisions. In addition, use the capabilities of DBMS tools to organize data and generate reports, as well as for future analysis. Medical service unit transactional workflow technique is used to provides medical information to the medical authorities about the injured soldiers and their status, which helps them to prepare the required needs before the wounded soldiers arrive at the hospitals. Both techniques handle the disconnection problem during transaction processing.In our approach, the transaction consists of four phases, reading, editing, validation, and writing phases, and its processing is based on the optimistic concurrency control protocol, and the rules of actionability that describe how a transaction behaves if a value-change is occurred on one or more of its attributes during its processing time by other transactions.
Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.
Wireless networks are currently proliferated by multiple tiers and heterogeneous networking equipment that aims to support multifarious services ranging from distant monitoring and control of wireless sensors to immersive virtual reality services. The vast collection of heterogeneous network equipment with divergent radio capabilities (e.g. multi-GHz operation) is vulnerable to wireless network attacks, raising questions on the service availability and coverage performance of future multi-tier wireless networks. In this paper, we study the impact of black hole attacks on service coverage of multi-tier heterogeneous wireless networks and derive closed form expressions when network nodes are unable to identify and avoid black hole nodes. Assuming access to multiple bands, the derived expressions can be readily used to assess the performance gains following from the employment of different association policies and the impact of black hole attacks in multi-tier wireless networks.
Data can be stored securely in various storage servers. But in the case of a server failure, or data theft from a certain number of servers, the remaining data becomes inadequate for use. Data is stored securely using secret sharing schemes, so that data can be reconstructed even if some of the servers fail. But not much work has been carried out in the direction of updation of this data. This leads to the problem of updation when two or more concurrent requests arrive and thus, it results in inconsistency. Our work proposes a novel method to store data securely with concurrent update requests using Petri Nets, under the assumption that the number of nodes is very large and the requests for updates are very frequent.
Network attack is a significant security issue for modern society. From small mobile devices to large cloud platforms, almost all computing products, used in our daily life, are networked and potentially under the threat of network intrusion. With the fast-growing network users, network intrusions become more and more frequent, volatile and advanced. Being able to capture intrusions in time for such a large scale network is critical and very challenging. To this end, the machine learning (or AI) based network intrusion detection (NID), due to its intelligent capability, has drawn increasing attention in recent years. Compared to the traditional signature-based approaches, the AI-based solutions are more capable of detecting variants of advanced network attacks. However, the high detection rate achieved by the existing designs is usually accompanied by a high rate of false alarms, which may significantly discount the overall effectiveness of the intrusion detection system. In this paper, we consider the existence of spatial and temporal features in the network traffic data and propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the convolutional neural network (CNN) and the recurrent neural network (RNN) learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features of the data can be effectively extracted. Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.
Cybersecurity education is a pressing need, when computer systems and mobile devices are ubiquitous and so are the associated threats. However, in the teaching and learning process of cybersecurity, it is challenging when the students are from diverse disciplines with various academic backgrounds. In this project, a number of virtual laboratories are developed to facilitate the teaching and learning process in a cybersecurity course. The aim of the laboratories is to strengthen students’ understanding of cybersecurity topics, and to provide students hands-on experience of encountering various security threats. The results of this project indicate that virtual laboratories do facilitate the teaching and learning process in cybersecurity for diverse discipline students. Also, we observed that there is an underestimation of the difficulty of studying cybersecurity by the students due to the general image of cybersecurity in public, which had a negative impact on the student’s interest in studying cybersecurity.
Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ways to obscure themselves in the network and making the conventional techniques inefficient. In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph Neural Network, which is applied on social connection graphs to detect anomalies. The combinations of various social network statistical measures are taken into account to study the graph structure and functioning of the anomalous nodes by employing deep neural networks on it. The hidden layer of the neural network plays an important role in finding the impact of statistical measure combination in anomaly detection.