Biblio

We classify .NET files as either benign or malicious by examining directed graphs derived from the set of functions comprising the given file. Each graph is viewed probabilistically as a Markov chain where each node represents a code block of the corresponding function, and by computing the PageRank vector (Perron vector with transport), a probability measure can be defined over the nodes of the given graph. Each graph is vectorized by computing Lebesgue antiderivatives of hand-engineered functions defined on the vertex set of the given graph against the PageRank measure. Files are subsequently vectorized by aggregating the set of vectors corresponding to the set of graphs resulting from decompiling the given file. The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space. We refer to this vectorization technique as PageRank Measure Integration Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla random forest on 2.5 million samples of decompiled. NET, evenly split between benign and malicious, from our in-house corpus and compare this model to a baseline model which leverages a text-only feature space. The median time needed for decompilation and scoring was 24ms. 11Code available at https://github.com/gtownrocks/grafuple.

Continuous monitoring and risk assessment of privacy violations on cloud systems are needed by anyone who has business needs subject to privacy regulations. Compliance to such regulations in dynamic systems demands appropriate techniques, tools and instruments. As a Service concepts can be a good option to support this task. Previous work presented PRIVAaaS, a software toolkit that allows controlling and reducing data leakages, thus preserving privacy, by providing anonymization capabilities to query-based systems. This short paper discusses the implementation details and deployment environment of an evolution of PRIVAaaS as a MAPE-K control loop within the ATMOSPHERE Platform. ATMOSPHERE is both a framework and a platform enabling the implementation of trustworthy cloud services. By enabling PRIVAaaS within ATMOSPHERE, privacy is made one of several trustworthiness properties continuously monitored and assessed by the platform with a software-based, feedback control loop known as MAPE-K.

Major commercial Field Programmable Gate Arrays (FPGAs) vendors provide encryption and authentication for programmable logic fabric (PL) bitstream using AES and RSA respectively. They are limited in scope of security that they provide and have proven to be vulnerable to different attacks. As-such, in-field deployed devices are susceptible to attacks where either a configuration bitstream, application software or dynamically reconfigurable bitstreams can be maliciously replaced. This hardware demo presents a framework for secure boot and runtime authentication for FPGAs. The presented system employs on-board cryptographic mechanisms and third-party established architectures such as Trusted Platform Module (TPM). The scope of this hardware demo is of systems level.

Direct Anonymous Attestation (DAA) is a cryptographic scheme that provides Trusted Platform Module TPM-backed anonymous credentials. We develop Tamarin modelling of the ECC-based version of the protocol as it is standardised and provide the first mechanised analysis of this standard. Our analysis confirms that the scheme is secure when all TPMs are assumed honest, but reveals a break in the protocol's expected authentication and secrecy properties for all TPMs even if only one is compromised. We propose and formally verify a minimal fix to the standard. In addition to developing the first formal analysis of ECC-DAA, the paper contributes to the growing body of work demonstrating the use of formal tools in supporting standardisation processes for cryptographic protocols.

Internet of Things is the new paradigm towards which the world is moving today. As these devices proliferate, security issues at these scales become more and more intimidating. Traditional approach like an antivirus does not work well with these devices and there is a need to look for a more trusted solution. For a device with reasonable computational power, we use a software trusted platform module for the cryptographic operations. In this paper, we have developed a model to remotely attest to the integrity of the processes running in the device. We have also explored the various features of the TPM (Trusted Platform Module) to gain insight into its working and also to ascertain those which can make this process better. This model depends on the server and the TPM to behave as roots of trust for this model. The client computes the HMAC (Hashed Message Authentication Code) values and appends a nonce and sends these values periodically to the server via asymmetric encryption. The HMAC values are verified by the server by comparing with its known good values (KGV) and the trustworthiness of the process is determined and accordingly an authorization response is sent.

The Internet of Things (IoT) is rapidly emerging as one of the dominant computing paradigms of this decade. Applications range from in-home entertainment to large-scale industrial deployments such as controlling assembly lines and monitoring traffic. While IoT devices are in many respects similar to traditional computers, user expectations and deployment scenarios as well as cost and hardware constraints are sufficiently different to create new security challenges as well as new opportunities. This is especially true for large-scale IoT deployments in which a central entity deploys and controls a large number of IoT devices with minimal human interaction. Like traditional computers, IoT devices are subject to attack and compromise. Large IoT deployments consisting of many nearly identical devices are especially attractive targets. At the same time, recovery from root compromise by conventional means becomes costly and slow, even more so if the devices are dispersed over a large geographical area. In the worst case, technicians have to travel to all devices and manually recover them. Data center solutions such as the Intelligent Platform Management Interface (IPMI) which rely on separate service processors and network connections are not only not supported by existing IoT hardware, but are unlikely to be in the foreseeable future due to the cost constraints of mainstream IoT devices. This paper presents CIDER, a system that can recover IoT devices within a short amount of time, even if attackers have taken root control of every device in a large deployment. The recovery requires minimal manual intervention. After the administrator has identified the compromise and produced an updated firmware image, he/she can instruct CIDER to force the devices to reset and to install the patched firmware on the devices. We demonstrate the universality and practicality of CIDER by implementing it on three popular IoT platforms (HummingBoard Edge, Raspberry Pi Compute Module 3 and Nucleo-L476RG) spanning the range from high to low end. Our evaluation shows that the performance overhead of CIDER is generally negligible.

In this paper, we present an improved approach to transfer style for videos based on semantic segmentation. We segment foreground objects and background, and then apply different styles respectively. A fully convolutional neural network is used to perform semantic segmentation. We increase the reliability of the segmentation, and use the information of segmentation and the relationship between foreground objects and background to improve segmentation iteratively. We also use segmentation to improve optical flow, and apply different motion estimation methods between foreground objects and background. This improves the motion boundaries of optical flow, and solves the problems of incorrect and discontinuous segmentation caused by occlusion and shape deformation.

Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. This work enhances state-of-the-art neural style transfer techniques by a generalized user interface with interactive tools to facilitate a creative and localized editing process. Thereby, we first propose a problem characterization representing trade-offs between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, first user tests indicate different levels of satisfaction for the implemented techniques and interaction design.

Trust is known to be a key component in human social relationships. It is trust that defines human behavior with others to a large extent. Generative models have been extensively used in social networks study to simulate different characteristics and phenomena in social graphs. In this work, an attempt is made to understand how trust in social graphs can be combined with generative modeling techniques to generate trust-based social graphs. These generated social graphs are then compared with the original social graphs to evaluate how trust helps in generative modeling. Two well-known social network data sets i.e. the soc-Bitcoin and the wiki administrator network data sets are used in this work. Social graphs are generated from these data sets and then compared with the original graphs along with other standard generative modeling techniques to see how trust is a good component in this. Other Generative modeling techniques have been available for a while but this investigation with the real social graph data sets validate that trust can be an important factor in generative modeling.

With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.

With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. There are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud; (ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. The capabilities of our system have been quantified. The experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.

Software Defined Networking (SDN) provides opportunities for flexible and dynamic traffic engineering. However, in current SDN systems, routing strategies are based on traditional mechanisms which lack in real-time modification and less efficient resource utilization. To overcome these limitations, deep learning is used in this paper to improve the routing computation in SDN. This paper proposes Convolutional Deep Reinforcement Learning (CoDRL) model which is based on deep reinforcement learning agent for routing optimization in SDN to minimize the mean network delay and packet loss rate. The CoDRL model consists of Deep Deterministic Policy Gradients (DDPG) deep agent coupled with Convolution layer. The proposed model tends to automatically adapts the dynamic packet routing using network data obtained through the SDN controller, and provides the routing configuration that attempts to reduce network congestion and minimize the mean network delay. Hence, the proposed deep agent exhibits good convergence towards providing routing configurations that improves the network performance.

In this paper, we propose a robust Nash strategy for a class of uncertain Markov jump delay stochastic systems (UMJDSSs) via static output feedback (SOF). After establishing the extended bounded real lemma for UMJDSS, the conditions for the existence of a robust Nash strategy set are determined by means of cross coupled stochastic matrix inequalities (CCSMIs). In order to solve the SOF problem, an heuristic algorithm is developed based on the algebraic equations and the linear matrix inequalities (LMIs). In particular, it is shown that robust convergence is guaranteed under a new convergence condition. Finally, a practical numerical example based on the congestion control for active queue management is provided to demonstrate the reliability and usefulness of the proposed design scheme.

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete re-routing of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.

Congestion diffusion resulting from the coupling by resource competing is a kind of typical failure propagation in network systems. The existing models of failure propagation mainly focused on the coupling by direct physical connection between nodes, the most efficiency path, or dependence group, while the coupling by resource competing is ignored. In this paper, a model of network congestion diffusion with resource competing is proposed. With the analysis of the similarities to resource competing in biomolecular network, the model describing the dynamic changing process of biomolecule concentration based on titration mechanism provides reference for our model. Then the innovation on titration mechanism is proposed to describe the dynamic changing process of link load in networks, and a novel congestion model is proposed. By this model, the global congestion can be evaluated. Simulations show that network congestion with resource competing can be obtained from our model.

Existing systems allow manufacturers to acquire factory floor data and perform analysis with cloud applications for machine health monitoring, product quality prediction, fault diagnosis and prognosis etc. However, they do not provide capabilities to perform testing of machine tools and associated components remotely, which is often crucial to identify causes of failure. This paper presents a fault diagnosis system in a cyber-physical manufacturing cloud (CPMC) that allows manufacturers to perform diagnosis and maintenance of manufacturing machine tools through remote monitoring and online testing using Machine Tool Communication (MTComm). MTComm is an Internet scale communication method that enables both monitoring and operation of heterogeneous machine tools through RESTful web services over the Internet. It allows manufacturers to perform testing operations from cloud applications at both machine and component level for regular maintenance and fault diagnosis. This paper describes different components of the system and their functionalities in CPMC and techniques used for anomaly detection and remote online testing using MTComm. It also presents the development of a prototype of the proposed system in a CPMC testbed. Experiments were conducted to evaluate its performance to diagnose faults and test machine tools remotely during various manufacturing scenarios. The results demonstrated excellent feasibility to detect anomaly during manufacturing operations and perform testing operations remotely from cloud applications using MTComm.

Digital-Twins simulate physical world objects by creating 'as-is' virtual images in a cyberspace. In order to create a well synchronized digital-twin simulator in manufacturing, information and activities of a physical machine need to be virtualized. Many existing digital-twins stream read-only data of machine sensors and do not incorporate operations of manufacturing machines through Internet. In this paper, a new method of virtualization is proposed to integrate machining data and operations into the digital-twins using Internet scale machine tool communication method. A fully functional digital-twin is implemented in CPMC testbed using MTComm and several manufacturing application scenarios are developed to evaluate the proposed method and system. Performance analysis shows that it is capable of providing data-driven visual monitoring of a manufacturing process and performing manufacturing operations through digital twins over the Internet. Results of the experiments also shows that the MTComm based digital twins have an excellent efficiency.

Advancements in computing, communication, and sensing technologies are making it possible to embed, control, and gather vital information from tiny devices that are being deployed and utilized in practically every aspect of our modernized society. From smart home appliances to municipal water and electric industrial facilities to our everyday work environments, the next Internet frontier, dubbed IoT, is promising to revolutionize our lives and tackle some of our nations' most pressing challenges. While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements in many aspects and diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. Further, such compromised devices will undeniably be leveraged as the next generation of botnets, given their increased processing capabilities and abundant bandwidth. While several demonstrations exist in the literature describing the exploitation procedures of a number of IoT devices, the up-to-date inference, characterization, and analysis of unsolicited IoT devices that are currently deployed "in the wild" is still in its infancy. In this article, we address this imperative task by leveraging active and passive measurements to report on unsolicited Internet-scale IoT devices. This work describes a first step toward exploring the utilization of passive measurements in combination with the results of active measurements to shed light on the Internet-scale insecurities of the IoT paradigm. By correlating results of Internet-wide scanning with Internet background radiation traffic, we disclose close to 14,000 compromised IoT devices in diverse sectors, including critical infrastructure and smart home appliances. To this end, we also analyze their generated traffic to create effective mitigation signatures that could be deployed in local IoT realms. To support largescale empirical data analytics in the context of IoT, we make available the inferred and extracted IoT malicious raw data through an authenticated front-end service. The outcomes of this work confirm the existence of such compromised devices on an Internet scale, while the generated inferences and insights are postulated to be employed for inferring other similarly compromised IoT devices, in addition to contributing to IoT cyber security situational awareness.

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of "blockchain", the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash as well as symmetric/asymmetric encryption and decryption algorithms ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning.

This paper discusses the possible effort to mitigate insider threats risk and aim to inspire organizations to consider identifying insider threats as one of the risks in the company's enterprise risk management activities. The paper suggests Trusted Human Framework (THF) as the on-going and cyclic process to detect and deter potential employees who bound to become the fraudster or perpetrator violating the access and trust given. The mitigation's control statements were derived from the recommended practices in the “Common Sense Guide to Mitigating Insider Threats” produced by the Software Engineering Institute, Carnegie Mellon University (SEI-CMU). The statements validated via a survey which was responded by fifty respondents who work in Malaysia.

In recent years, almost all the real-world operations are transferred to cyber world and these market computers connect with each other via Internet. As a result of this, there is an increasing number of security breaches of the networks, whose admins cannot protect their networks from the all types of attacks. Although most of these attacks can be prevented with the use of firewalls, encryption mechanisms, access controls and some password protections mechanisms; due to the emergence of new type of attacks, a dynamic intrusion detection mechanism is always needed in the information security market. To enable the dynamicity of the Intrusion Detection System (IDS), it should be updated by using a modern learning mechanism. Neural Network approach is one of the mostly preferred algorithms for training the system. However, with the increasing power of parallel computing and use of big data for training, as a new concept, deep learning has been used in many of the modern real-world problems. Therefore, in this paper, we have proposed an IDS system which uses GPU powered Deep Learning Algorithms. The experimental results are collected on mostly preferred dataset KDD99 and it showed that use of GPU speed up training time up to 6.48 times depending on the number of the hidden layers and nodes in them. Additionally, we compare the different optimizers to enlighten the researcher to select the best one for their ongoing or future research.

Mobile systems are always growing, automatically they need enough resources to secure them. Indeed, traditional techniques for protecting the mobile environment are no longer effective. We need to look for new mechanisms to protect the mobile environment from malicious behavior. In this paper, we examine one of the most popular systems, Android OS. Next, we will propose a distributed architecture based on IDS-AM to detect intrusions by mobile agents (IDS-AM).

the more (IoT) scales up with promises, the more security issues raise to the surface and must be tackled down. IoT is very vulnerable against DoS attacks. In this paper, we propose a hybrid design of signature-based IDS and anomaly-based IDS. The proposed hybrid design intends to enhance the intrusion detection and prevention systems (IDPS) to detect any DoS attack at early stages by classifying the network packets based on user behavior. Simulation results prove successful detection of DoS attack at earlier stages.

Intrusion Detection system (IDS) was an application which was aimed to monitor network activity or system and it could find if there was a dangerous operation. Implementation of IDS on Software Define Network architecture (SDN) has drawbacks. IDS on SDN architecture might decreasing network Quality of Service (QoS). So the network could not provide services to the existing network traffic. Throughput, delay and packet loss were important parameters of QoS measurement. Snort IDS and bro IDS were tools in the application of IDS on the network. Both had differences, one of which was found in the detection method. Snort IDS used a signature based detection method while bro IDS used an anomaly based detection method. The difference between them had effects in handling the network traffic through it. In this research, we compared both tools. This comparison are done with testing parameters such as throughput, delay, packet loss, CPU usage, and memory usage. From this test, it was found that bro outperform snort IDS for throughput, delay , and packet loss parameters. However, CPU usage and memory usage on bro requires higher resource than snort.

Most traditional recommendation algorithms only consider the binary relationship between users and projects, these can basically be converted into score prediction problems. But most of these algorithms ignore the users's interests, potential work factors or the other social factors of the recommending products. In this paper, based on the existing trustworthyness model and similarity measure, we puts forward the concept of trust similarity and design a joint interest-content recommendation framework to suggest users which videos to watch in the online video site. In this framework, we first analyze the user's viewing history records, tags and establish the user's interest characteristic vector. Then, based on the updated vector, users should be clustered by sparse subspace clust algorithm, which can improve the efficiency of the algorithm. We certainly improve the calculation of similarity to help users find better neighbors. Finally we conduct experiments using real traces from Tencent Weibo and Youku to verify our method and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.