Biblio

During the Cold War era, countries with asymmetrical relationships often demonstrated how lower-tier nation states required the alliance and support from top-tier nation states. This statement no longer stands true as country such as North Korea has exploited global financial institutions through various malware such as WANNACRY V0, V1, V2, evtsys.exe, and BRAMBUL WORM. Top tier nation states such as the U.S. are unable to use diplomatic clout or to retaliate against the deferrer. Our study examined the affidavit filed against the North Korean hacker, Park Jin Hyok, which was provided by the FBI. Our paper focuses on the operations and campaigns that were carried out by the Lazarus Group by focusing on the key factors of the infrastructure and artifacts. Due to the nature of the cyber deterrence, deterrence in the cyber realm is far complex than the nuclear deterrence. We focused on the Sony Picture Entertainment’s incident for our study. In this study, we discuss how cyber deterrence can be employed when different nation states share an asymmetrical relationship. Furthermore, we focus on contestability and attribution that is a key factor that makes cyber deterrence difficult.

Due to the steady growth of various sophisticated types of malware, different malware analysis systems are becoming more and more demanded. While there are various automatic approaches available to identify and detect malware, the malware analysis is still time-consuming process. The visualization-driven techniques may significantly increase the efficiency of the malware analysis process by involving human visual system which is a powerful pattern seeker. In this paper the authors reviewed different visualization methods, examined their features and tasks solved with their help. The paper presents the most commonly used approaches and discusses open challenges in malware visual analytics.

Cross-Site Scripting, also called as XSS, is a type of injection where malicious scripts are injected into trusted websites. When malicious code, usually in the form of browser side script, is injected using a web application to a different end user, an XSS attack is said to have taken place. Flaws which allows success to this attack is remarkably widespread and occurs anywhere a web application handles the user input without validating or encoding it. A study carried out by Symantic states that more than 50% of the websites are vulnerable to the XSS attack. Security engineers of Microsoft coined the term "Cross-Site Scripting" in January of the year 2000. But even if was coined in the year 2000, XSS vulnerabilities have been reported and exploited since the beginning of 1990's, whose prey have been all the (then) tech-giants such as Twitter, Myspace, Orkut, Facebook and YouTube. Hence the name "Cross-Site" Scripting. This attack could be combined with other attacks such as phishing attack to make it more lethal but it usually isn't necessary, since it is already extremely difficult to deal with from a user perspective because in many cases it looks very legitimate as it's leveraging attacks against our banks, our shopping websites and not some fake malicious website.

Cloud federation is the evolution of modern cloud computing. It provides better resource-sharing, perfect resource-utilization, and load-balancing. However, the heterogeneity of security policies and configurations between cloud service providers makes it hard for users to totally trust them. Further, the severe impact of modern cloud attacks such as cross-side channels on federated environments is a major roadblock against such evolution. Securing users' capsules (Virtual Machines and containers) against cross-side channel attacks is considered as a big challenge to cloud service providers. Moving-target Defense (MtD) by live capsule migration was introduced as an effective mechanism to overcome such challenge. However, researchers noted that even with MtD, migrated capsules can still be tracked via routing information. In this paper, we propose a novel Blockchain-based routing mechanism to enable trace-resistant Moving-target Defence (BMtD) to enable anonymous live cross-cloud migrations of running capsules in federated cloud environments. Exploiting the Vulnerable, Exposed, Attacked, Recovered (VEAR) model, simulation results demonstrated the effectiveness of BMtD in minimizing viral attack dispersion.

Lateral movement attack performs malicious activities by infecting part of a network system first and then moving laterally to the left system in order to compromise more computers. It is widely used in various sophisticated attacks and plays a critical role. This paper aims to quantitatively analyze the transient security and dependability of a critical network system under lateral movement attacks, whose intruding capability increases with the increasing number of attacked computers. We propose a survivability model for capturing the system and adversary behaviors from the time instant of the first intrusion launched from any attacked computer to the other vulnerable computers until defense solution is developed and deployed. Stochastic Reward Nets (SRN) is applied to automatically build and solve the model. The formulas are also derived for calculating the metrics of interest. Simulation is carried out to validate the approximate accuracy of our model and formulas. The quantitative analysis can help network administrators make a trade-off between damage loss and defense cost.

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host's network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network's vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts' network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.

In malware behavior analysis, there are limitations in the analysis method of control flow and data flow. Researchers analyzed data flow by dynamic taint analysis tools, however, it cost a lot. In this paper, we proposed a method of generating malware summary based on semantic behavior graphs (SBGs, Semantic Behavior Graphs) to address this issue. In this paper, we considered various situation where behaviors be capable of being associated, thus an algorithm of generating semantic behavior graphs was given firstly. Semantic behavior graphs are composed of behavior nodes and associated data edges. Then, we extracted behaviors and logical relationships between behaviors from semantic behavior graphs, and finally generated a summary of malware behaviors with true intension. Experimental results showed that our approach can effectively identify and describe malicious behaviors and generate accurate behavior summary.

Recently, the rapid development of Internet of things (IoT) has resulted in the generation of a considerable amount of data, which should be stored. Therefore, it is necessary to develop methods that can easily capture, save, and modify these data. The data generated using IoT contain private information; therefore sufficient security features should be incorporated to ensure that potential attackers cannot access the data. Researchers from various fields are attempting to achieve data security. One of the major challenges is that IoT is a paradigm of how each device in the Internet infrastructure is interconnected to a globally dynamic network. When searching in dynamic cloud-stored data, sensitive data can be easily leaked. IoT data storage and retrieval from untrusted cloud servers should be secure. Searchable symmetric encryption (SSE) is a vital technology in the field of cloud storage. SSE allows users to use keywords to search for data in an untrusted cloud server but the keywords and the data content are concealed from the server. However, an SSE database is seldom used by cloud operators because the data stored on the cloud server is often modified. The server cannot update the data without decryption because the data are encrypted by the user. Therefore, dynamic SSE (DSSE) has been developed in recent years to support the aforementioned requirements. Instead of decrypting the data stored by customers, DSSE adds or deletes encrypted data on the server. A number of DSSE systems based on linked list structures or blind storage (a new primitive) have been proposed. From the perspective of functionality, extensibility, and efficiency, these DSSE systems each have their own advantages and drawbacks. The most crucial aspect of a system that is used in the cloud industry is the trade-off between performance and security. Therefore, we compared the efficiency and security of multiple DSSE systems and identified their shortcomings to develop an improved system.

Software-defined networks (SDN), through their programmability, significantly increase network resilience by enabling dynamic reconfiguration of network topologies in response to faults and potentially malicious attacks detected in real-time. Another key trend in network softwarization is cloud-native software, which, together with SDN, will be an integral part of the core of future 5G networks. In SDN, the control plane forms the "brain" of the software-defined network and is typically implemented as a set of distributed controller replicas to avoid a single point of failure. Distributed consensus algorithms are used to ensure agreement among the replicas on key data even in the presence of faults. Security is also a critical concern in ensuring that attackers cannot compromise the SDN control plane; byzantine fault tolerance algorithms can provide protection against compromised controller replicas. However, while reliability/availability and security form key attributes of resilience, they are typically modeled separately in SDN, without consideration of the potential impacts of their interaction. In this paper we present an initial framework for a model that unifies reliability, availability, and security considerations in distributed consensus. We examine – via simulation of our model – some impacts of the interaction between accidental faults and malicious attacks on SDN and suggest potential mitigations unique to cloud-native software.

Binary similarity has been widely used in function recognition and vulnerability detection. How to define a proper similarity is the key element in implementing a fast detection method. We proposed a scalable method to detect binary vulnerabilities based on similarity. Procedures lifted from binaries are divided into several comparable strands by data dependency, and those strands are transformed into a normalized form by our tool named VulneraBin, so that similarity can be determined between two procedures through a hash value comparison. The low computational complexity allows semantically equivalent code to be identified in binaries compiled from million lines of source code in a fast and accurate way.

This paper describes an experimental result of modeling stomp boxes of the distortion effect based on a machine learning approach. Our proposed technique models a distortion stomp box as a neural network consisting of LSTM layers. In this approach, the neural network is employed for learning the nonlinear behavior of the distortion stomp boxes. All the parameters for replicating the distortion sound are estimated through its training process using the input and output signals obtained from some commercial stomp boxes. The experimental result indicates that the proposed technique may have a certain appropriateness to replicate the distortion sound by using the well-trained neural networks.

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.

In this paper we report on the results of selected horizontal SCA attacks against two open-source designs that implement hardware accelerators for elliptic curve cryptography. Both designs use the complete addition formula to make the point addition and point doubling operations indistinguishable. One of the designs uses in addition means to randomize the operation sequence as a countermeasure. We used the comparison to the mean and an automated SPA to attack both designs. Despite all these countermeasures, we were able to extract the keys processed with a correctness of 100%.

Advertising channels have evolved from conventional print media, billboards and radio-advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a causal attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict per-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real-world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modeling on the predicted channel attribution.

Database forensic investigation (DBFI) is an important area of research within digital forensics. It's importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.

Video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing. Similarly, surveillance video recording is a powerful method of deterring unlawful activities. A Solution-by-Design can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered. This paper presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos using steganography and hashing. The method conforms to the data protection regulations which are increasingly adopted by governments, and is applicable to network edge storage. Security credentials are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored within video frames themselves, independently of the content. The proposed method has turned out to not only preserve the integrity of the stored video data but also results in very limited degradation of the video data due to steganography. Despite the presence of steganographic information, video frames are still available for common image processing tasks such as tracking and classification.

There are multiple steps involved in transitioning a server from the factory to being fully provisioned for an intended workload. These steps include finding the optimal slot for the hardware and to compose the required resources on the hardware for the intended workload. There are many different factors that influence the placement of server hardware in the datacenter, such as physical limitations to connect to a network be it Ethernet or storage networks, power requirements, temperature/cooling considerations, and physical space, etc. In addition to this, there may be custom requirements driven by workload policies (such as security, data privacy, power redundancy, etc.). Once the server has been placed in the right slot it needs to be configured with the appropriate resources for the intended workload. CIED will provide a ranked list of locations for server placement based on the intended workload, connectivity and physical requirements of the server. Once the server is placed in the suggested slot, the solution automatically discovers the server and composes the required resources (compute, storage and networks) for running the appropriate workload. CIED reduces the overall time taken to move hardware from factory to production and also maximizes the server hardware utilization while minimizing downtime by physically placing the resources optimally. From the case study that was undertaken, the time taken to transition a server from factory to being fully provisioned was proportional to the number of devices in the datacenter. With CIED this time is constant irrespective of the complexity or the number of devices in a datacenter.

Hyperspectral image is acquired with a special sensor in which the information is collected continuously. This sensor will provide abundant data from the scene captured. The high voluminous data in this image give rise to the extraction of materials and other valuable items in it. This paper proposes a methodology to extract rich information from the hyperspectral images. As the information collected in a contiguous manner, there is a need to extract spectral bands that are uncorrelated. A factor analysis based dimensionality reduction technique is employed to extract the spectral bands and a weight least square filter is used to get the spatial information from the data. Due to the preservation of edge property in the spatial filter, much information is extracted during the feature extraction phase. Finally, a nonlinear SVM is applied to assign a class label to the pixels in the image. The research work is tested on the standard dataset Indian Pines. The performance of the proposed method on this dataset is assessed through various accuracy measures. These accuracies are 96%, 92.6%, and 95.4%. over the other methods. This methodology can be applied to forestry applications to extract the various metrics in the real world.

This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.

We propose Cleann, the first end-to-end framework that enables online mitigation of Trojans for embedded Deep Neural Network (DNN) applications. A Trojan attack works by injecting a backdoor in the DNN while training; during inference, the Trojan can be activated by the specific backdoor trigger. What differentiates Cleann from the prior work is its lightweight methodology which recovers the ground-truth class of Trojan samples without the need for labeled data, model retraining, or prior assumptions on the trigger or the attack. We leverage dictionary learning and sparse approximation to characterize the statistical behavior of benign data and identify Trojan triggers. Cleann is devised based on algorithm/hardware co-design and is equipped with specialized hardware to enable efficient real-time execution on resource-constrained embedded platforms. Proof of concept evaluations on Cleann for the state-of-the-art Neural Trojan attacks on visual benchmarks demonstrate its competitive advantage in terms of attack resiliency and execution overhead.

Fast Growth of (ICT) Information and Communication Technology results to Innovation of Cloud Computing and is considered as a key driver for technological innovations, as an IT innovations, cloud computing had added a new dimension to that importance by increasing usage to technology that motivates economic development at the national and global levels. Continues need of higher storage space (applications, files, videos, music and others) are some of the reasons for adoption and implementation, Users and Enterprises are gradually changing the way and manner in which Data and Information are been stored. Storing/Retrieving Data and Information traditionally using Standalone Computers are no longer sustainable due to high cost of Peripheral Devices, This further recommends organizational innovative adoption with regards to approaches on how to effectively reduced cost in businesses. Cloud Computing provides a lot of prospects to users/organizations; it also exposes security concerns which leads to low adoption, implementation and usage. Therefore, the study will examine standard ways of improving cloud computing adoption, implementation and usage by proposing and developing a security model using a design methodology that will ensure a secured Cloud Computing and also identify areas where future regularization could be operational.

Incident Handling for Cloud Infrastructures focuses on how the clustering based and non-clustering based algorithms can be implemented. Our research focuses in identifying anomalies and suspicious activities that might happen inside a Cloud Infrastructure over available datasets. A brief study has been conducted, where a network statistics dataset the NSL-KDD, has been chosen as the model to be worked upon, such that it can mirror the Cloud Infrastructure and its components. An important aspect of cloud security is to implement anomaly detection mechanisms, in order to monitor the incidents that inhibit the development and the efficiency of the cloud. Several methods have been discovered which help in achieving our present goal, some of these are highlighted as the following; by applying algorithm such as the Local Outlier Factor to cancel the noise created by irrelevant data points, by applying the DBSCAN algorithm which can detect less denser areas in order to identify their cause of clustering, the K-Means algorithm to generate positive and negative clusters to identify the anomalous clusters and by applying the Isolation Forest algorithm in order to implement decision based approach to detect anomalies. The best algorithm would help in finding and fixing the anomalies efficiently and would help us in developing an Incident Handling model for the Cloud.

The Internet of Things (IoT) has enabled the rapid pace of the use of communication technology and infiltration of technical systems in a digital world. In terms of power systems generation and operation, a reliable solution for substation automation and smart grid communication is the IEC 61850 standard. It has a robust modelling structure for monitoring, protection, and control and management systems in substations and across the grid. Modern communication technologies are destined for internet use for remote monitoring, settings, and data recovery. However, the communication network is exposed to cyber threats and evident risks in security defense of automated power systems. To tackle these vulnerabilities, the IEC 62351 standard aims to improve security in handling the communication and data transfers in power system automation. This paper discusses the different security measures in communication, information and cyber security solutions in power systems. To further illustrate the novel communication and security schemes of digital substations, a case study using the Victoria University Zone Substation (VUZS) simulator for cybersecurity assessment has been instigated.

Lightweight virtualization represented by container technology provides a virtual environment for cloud services with more flexibility and efficiency due to the kernel-sharing property. However, the shared kernel also means that the system isolation mechanisms are incomplete. Attackers can scan the shared system configuration files to explore vulnerabilities for launching attacks. Previous works mainly eliminate the problem by fixing operating systems or using access control policies, but these methods require significant modifications and cannot meet the security needs of individual containers accurately. In this paper, we present ConfigRand, a moving target defense framework to prevent the information leakages due to the shared kernel in the container-based cloud. The ConfigRand deploys deceptive system configurations for each container, bounding the scan of attackers aimed at the shared kernel. In design of ConfigRand, we (1) propose a framework applying the moving target defense philosophy to periodically generate, distribute, and deploy the deceptive system configurations in the container-based cloud; (2) establish a model to formalize these configurations and quantify their heterogeneity; (3) present a configuration movement strategy to evaluate and optimize the variation of configurations. The results show that ConfigRand can effectively prevent the information leakages due to the shared kernel and apply to typical container applications with minimal system modification and performance degradation.

Vendors who wish to provide software or services to large corporations and governments must often obtain numerous certificates of compliance. Each certificate asserts that the software satisfies a compliance regime, like SOC or the PCI DSS, to protect the privacy and security of sensitive data. The industry standard for obtaining a compliance certificate is an auditor manually auditing source code. This approach is expensive, error-prone, partial, and prone to regressions. We propose continuous compliance to guarantee that the codebase stays compliant on each code change using lightweight verification tools. Continuous compliance increases assurance and reduces costs. Continuous compliance is applicable to any source-code compliance requirement. To illustrate our approach, we built verification tools for five common audit controls related to data security: cryptographically unsafe algorithms must not be used, keys must be at least 256 bits long, credentials must not be hard-coded into program text, HTTPS must always be used instead of HTTP, and cloud data stores must not be world-readable. We evaluated our approach in three ways. (1) We applied our tools to over 5 million lines of open-source software. (2) We compared our tools to other publicly-available tools for detecting misuses of encryption on a previously-published benchmark, finding that only ours are suitable for continuous compliance. (3) We deployed a continuous compliance process at AWS, a large cloud-services company: we integrated verification tools into the compliance process (including auditors accepting their output as evidence) and ran them on over 68 million lines of code. Our tools and the data for the former two evaluations are publicly available.