Biblio

Graph analysis can capture relationships between network entities and can be used to identify and rank anomalous hosts, users, or applications from various types of cyber logs. It is often the case that the data in the logs can be represented as a bipartite graph (e.g. internal IP-external IP, user-application, or client-server). State-of-the-art graph based anomaly detection often generalizes across all types of graphs — namely bipartite and non-bipartite. This confounds the interpretation and use of specific graph features such as degree, page rank, and eigencentrality that can provide a security analyst with situational awareness and even insights to potential attacks on enterprise scale networks. Furthermore, graph algorithms applied to data collected from large, distributed enterprise scale networks require accompanying methods that allow them to scale to the data collected. In this paper, we provide a novel, scalable, directional graph projection framework that operates on cyber logs that can be represented as bipartite graphs. We also present methodologies to further narrow returned results to anomalous/outlier cases that may be indicative of a cyber security event. This framework computes directional graph projections and identifies a set of interpretable graph features that describe anomalies within each partite.

The Internet of Things (IoT) devices perform security-critical operations and deal with sensitive information in the IoT-based systems. Therefore, the increased deployment of smart devices will make them targets for cyber attacks. Adversaries can perform malicious actions, leak private information, and track devices' and their owners' location by gaining unauthorized access to IoT devices and networks. However, conventional security protocols are not primarily designed for resource constrained devices and therefore cannot be applied directly to IoT systems. In this paper, we propose Boot-IoT - a privacy-preserving, lightweight, and scalable security scheme for limited resource devices. Boot-IoT prevents a malicious device from joining an IoT network. Boot-IoT enables a device to compute a unique identity for authentication each time the device enters a network. Moreover, during device to device communication, Boot-IoT provides a lightweight mutual authentication scheme that ensures privacy-preserving identity usages. We present a detailed analysis of the security strength of BootIoT. We implemented a prototype of Boot-IoT on IoT devices powered by Contiki OS and provided an extensive comparative analysis of Boot-IoT with contemporary authentication methods. Our results show that Boot-IoT is resource efficient and provides better scalability compared to current solutions.

A growing need for scalable solutions for both machine learning and interactive analytics exists in the area of cyber-security. Machine learning aims at segmentation and classification of log events, which leads towards optimization of the threat monitoring processes. The tools for interactive analytics are required to resolve the uncertain cases, whereby machine learning algorithms are not able to provide a convincing outcome and human expertise is necessary. In this paper we focus on a case study of a security operations platform, whereby typical layers of information processing are integrated with a new database engine dedicated to approximate analytics. The engine makes it possible for the security experts to query massive log event data sets in a standard relational style. The query outputs are received orders of magnitude faster than any of the existing database solutions running with comparable resources and, in addition, they are sufficiently accurate to make the right decisions about suspicious corner cases. The engine internals are driven by the principles of information granulation and summary-based processing. They also refer to the ideas of data quantization, approximate computing, rough sets and probability propagation. In the paper we study how the engine's parameters can influence its performance within the considered environment. In addition to the results of experiments conducted on large data sets, we also discuss some of our high level design decisions including the choice of an approximate query result accuracy measure that should reflect the specifics of the considered threat monitoring operations.

SDSM is a novel approach for providing secure directory-based distributed shared memory to CPUs that are connected via an untrusted medium. SDSM scales efficiently to thousands of CPUs with less than 0.8% performance reduction, compared to a system with no security.

Cloud computing is revolutionizing many IT ecosystems through offering scalable computing resources that are easy to configure, use and inter-connect. However, this model has always been viewed with some suspicion as it raises a wide range of security and privacy issues that need to be negotiated. This research focuses on the construction of a trust layer in cloud computing to build a trust relationship between cloud service providers and cloud users. In particular, we address the rise of container-based virtualisation has a weak isolation compared to traditional VMs because of the shared use of the OS kernel and system components. Therefore, we will build a trust layer to solve the issues of weaker isolation whilst maintaining the performance and scalability of the approach. This paper has two objectives. Firstly, we propose a security system to protect containers from other guests through the addition of a Role-based Access Control (RBAC) model and the provision of strict data protection and security. Secondly, we provide a stress test using isolation benchmarking tools to evaluate the isolation in containers in term of performance.

Most of the existing authentication protocols are based on either asymmetric cryptography like public-key infrastructure (PKI) or symmetric cryptography. The PKI-based authentication protocols are strongly recommended for solving security issues in VANETs. However, they have following shortcomings: (1) lengthy certificates lead to transmission and computation overheads, and (2) lack of privacy-preservation due to revealing of vehicle identity, communicated in broadcasting safety-message. Symmetric cryptography based protocols are faster because of a single secret key and simplicity; however, it does not ensure non-repudiation. In this paper, we present an Efficient, Scalable and Privacy-preserving Authentication (ESPA) protocol for secure vehicular ad hoc networks (VANETs). The protocol employs hybrid cryptography. In ESPA, the asymmetric PKI based pre-authentication and the symmetric hash message authentication code (HMAC) based authentication are adopted during vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communications, respectively. Extensive simulations are conducted to validate proposed ESPA protocol and compared with the existing work based on PKI and HMAC. The performance analysis showed that ESPA is more efficient, scalable and privacy-preserving secured protocol than the existing work.

This paper proposes a highly scalable framework that can be applied to detect network anomaly at per flow level by constructing a meta-model for a family of machine learning algorithms or statistical data models. The approach is scalable and attainable because raw data needs to be accessed only one time and it will be processed, computed and transformed into a meta-model matrix in a much smaller size that can be resident in the system RAM. The calculation of meta-model matrix can be achieved through disposable updating operations at per row level: once a per-flow information is proceeded, it is no longer needed in calculating the meta-model matrix. While the proposed framework covers both Gaussian and non-Gaussian data, the focus of this work is on the linear regression models. Specifically, a new concept called meta-model sufficient statistics is proposed to analyze a group of models, where exact, not the approximate, results are derived. In addition, the proposed framework can quickly discover an optimal statistical or computer model from a family of candidate models without the need of rescanning the raw dataset. This suggest an extremely efficient and effectively theory and method is possible for big data security analysis.

This paper focuses on the issues of secure key management for smart grid. With the present key management schemes, it will not yield security for deployment in smart grid. A novel key management scheme is proposed in this paper which merges elliptic curve public key technique and symmetric key technique. Based on the Needham-Schroeder authentication protocol, symmetric key scheme works. Well known threats like replay attack and man-in-the-middle attack can be successfully abolished using Smart Grid. The benefits of the proposed system are fault-tolerance, accessibility, Strong security, scalability and Efficiency.

Data sharing is a significant functionality in cloud storage. These cloud storage provider are answerable for keeping the data obtainable and available in addition to the physical environment protected and running. Here we can securely, efficiently, and flexibly share data with others in cloud storage. A new public-key cryptosystems is planned which create constant-size cipher texts such that efficient allocation of decryption rights for any set of cipher texts are achievable. The uniqueness means that one can aggregate any set of secret keys and make them as packed in as a single key, but encircling the power of all the keys being aggregated. This packed in aggregate key can be easily sent to others or be stored in a smart card with very restricted secure storage. In KAC, users encrypt a file with single key, that means every file have each file, also there will be aggregate keys for two or more files, which formed by using the tree structure. Through this, the user can share more files with a single key at a time.

Secure computation is increasingly required, most notably when using public clouds. Many secure CPU architectures have been proposed, mostly focusing on single-threaded applications running on a single node. However, security for parallel and distributed computation is also needed, requiring the sharing of secret data among mutually trusting threads running in different compute nodes in an untrusted environment. We propose SDSM, a novel hardware approach for providing a security layer for directory-based distributed shared memory systems. Unlike previously proposed schemes that cannot maintain reasonable performance beyond 32 cores, our approach allows secure parallel applications to scale efficiently to thousands of cores.

The blockchain emerges as an innovative tool which proves to be useful in a number of application scenarios. A number of large industrial players, such as IBM, Microsoft, Intel, and NEC, are currently investing in exploiting the blockchain in order to enrich their portfolio of products. A number of researchers and practitioners speculate that the blockchain technology can change the way we see a number of online applications today. Although it is still early to tell for sure, it is expected that the blockchain will stimulate considerable changes to a large number of products and will positively impact the digital experience of many individuals around the globe. In this tutorial, we overview, detail, and analyze the security provisions of Bitcoin and its underlying blockchain-effectively capturing recently reported attacks and threats in the system. Our contributions go beyond the mere analysis of reported vulnerabilities of Bitcoin; namely, we describe and evaluate a number of countermeasures to deter threats on the system-some of which have already been incorporated in the system. Recall that Bitcoin has been forked multiple times in order to fine-tune the consensus (i.e., the block generation time and the hash function), and the network parameters (e.g., the size of blocks). As such, the results reported in this tutorial are not only restricted to Bitcoin, but equally apply to a number of "altcoins" which are basically clones/forks of the Bitcoin source code. Given the increasing number of alternative blockchain proposals, this tutorial extracts the basic security lessons learnt from the Bitcoin system with the aim to foster better designs and analysis of next-generation secure blockchain currencies and technologies.

Because of rampant security breaches in IoT devices, searching vulnerabilities in massive IoT ecosystems is more crucial than ever. Recent studies have demonstrated that control-flow graph (CFG) based bug search techniques can be effective and accurate in IoT devices across different architectures. However, these CFG-based bug search approaches are far from being scalable to handle an enormous amount of IoT devices in the wild, due to their expensive graph matching overhead. Inspired by rich experience in image and video search, we propose a new bug search scheme which addresses the scalability challenge in existing cross-platform bug search techniques and further improves search accuracy. Unlike existing techniques that directly conduct searches based upon raw features (CFGs) from the binary code, we convert the CFGs into high-level numeric feature vectors. Compared with the CFG feature, high-level numeric feature vectors are more robust to code variation across different architectures, and can easily achieve realtime search by using state-of-the-art hashing techniques. We have implemented a bug search engine, Genius, and compared it with state-of-art bug search approaches. Experimental results show that Genius outperforms baseline approaches for various query loads in terms of speed and accuracy. We also evaluated Genius on a real-world dataset of 33,045 devices which was collected from public sources and our system. The experiment showed that Genius can finish a search within 1 second on average when performed over 8,126 firmware images of 420,558,702 functions. By only looking at the top 50 candidates in the search result, we found 38 potentially vulnerable firmware images across 5 vendors, and confirmed 23 of them by our manual analysis. We also found that it took only 0.1 seconds on average to finish searching for all 154 vulnerabilities in two latest commercial firmware images from D-LINK. 103 of them are potentially vulnerable in these images, and 16 of them were confirmed.

MPI includes all processes in MPI\_COMM\_WORLD; this is untenable for reasons of scale, resiliency, and overhead. This paper offers a new approach, extending MPI with a new concept called Sessions, which makes two key contributions: a tighter integration with the underlying runtime system; and a scalable route to communication groups. This is a fundamental change in how we organise and address MPI processes that removes well-known scalability barriers by no longer requiring the global communicator MPI\_COMM\_WORLD.