Analysis of Tizen Security Model and Ways of Bypassing It on Smart TV Platform

The smart TV market is growing at an ever faster pace every year. Smart TVs are equipped with many advanced functions, allow users to search, chat, browse, share, update, and download different content. That is one of the reason why smart TVs became a target for the hacker community. In this article, we decided to test security of Tizen operating system, which is one of the most popular smart TV operating systems. Tizen is used on many different devices including smartphones, notebooks, wearables, infotainment systems, and smart TVs. By now, there are articles which present security mechanisms of Tizen OS, and sometimes with a way to bypass them; however, none of them are applicable to the smart TVs. In the article, we focused on developing an algorithm that will allow us to gain root access to the smart TV. The proposed attack scenario uses CVE-2014-1303 and CVE-2015-1805 bugs to bypass or disable security mechanisms in Tizen OS and finally gain root access.

Intelligent Traffic Classification for Detecting DDoS Attacks using SDN/OpenFlow

<p>Distributed denial of service (DDoS) attacks utilise many attacking entities to prevent legitimate use of a resource via consumption. Detecting these attacks is often difficult when using a traditional networking paradigm as network information and control are not centralised. Software-Defined Networking is a recent paradigm that centralises network control, thus improving the ability to gather network information. Traffic classification techniques can leverage the gathered data to detect DDoS attacks.This thesis utilises nmeta2, a SDN-based traffic classification architecture, to study the effectiveness of machine learning methods to detect DDoS attacks. These methods are evaluated on a physical network testbed to demonstrate their application during a DDoS attack scenario.</p>

Intelligent Traffic Classification for Detecting DDoS Attacks using SDN/OpenFlow

<p>Distributed denial of service (DDoS) attacks utilise many attacking entities to prevent legitimate use of a resource via consumption. Detecting these attacks is often difficult when using a traditional networking paradigm as network information and control are not centralised. Software-Defined Networking is a recent paradigm that centralises network control, thus improving the ability to gather network information. Traffic classification techniques can leverage the gathered data to detect DDoS attacks.This thesis utilises nmeta2, a SDN-based traffic classification architecture, to study the effectiveness of machine learning methods to detect DDoS attacks. These methods are evaluated on a physical network testbed to demonstrate their application during a DDoS attack scenario.</p>

Optimizing network microsegmentation policy for cyber resilience

This paper describes an approach for improving cyber resilience through the synthesis of optimal microsegmentation policy for a network. By leveraging microsegmentation security architecture, we can reason about fine-grained policy rules that enforce access for given combinations of source address, destination address, destination port, and protocol. Our approach determines microsegmentation policy rules that limit adversarial movement within a network according to assumed attack scenarios and mission availability needs. For this problem, we formulate a novel optimization objective function that balances cyberattack risks against accessibility to critical network resources. Given the application of a particular set of policy rules as a candidate optimal solution, this objective function estimates the adversary effort for carrying out a particular attack scenario, which it balances against the extent to which the solution restricts access to mission-critical services. We then apply artificial intelligence techniques (evolutionary programming) to learn microsegmentation policy rules that optimize this objective function.

An analytical Survey of Attack Scenario Parameters on the Techniques of Attack Mitigation in WSN

Wireless sensor networks (WSN) were cataloged as one of the most important emerging technologies of the last century and are considered the basis of the Internet of Things paradigm. However, an undeniable disadvantage of WSN is that the resources available for these types of networks, such as processing capacity, memory, and battery, are usually in short supply. This limitation in resources implements security mechanisms a difficult task. This work reviews 93 recent proposals in which different solutions were formulated for the different attacks in WSN in the network layer; in total, 139 references were considered. According to the literature, these attacks are mainly Sybil, wormhole, sinkhole, and selective forwarding. The main goal of this contribution is to present the evaluation metrics used in the state of the art to mitigate the Sybil, wormhole, sinkhole, and selective forwarding attacks and show the network topologies used in each of these proposals.

Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario.

Social engineering in cybersecurity: a domain ontology and knowledge graph application examples

Social engineering has posed a serious threat to cyberspace security. To protect against social engineering attacks, a fundamental work is to know what constitutes social engineering. This paper first develops a domain ontology of social engineering in cybersecurity and conducts ontology evaluation by its knowledge graph application. The domain ontology defines 11 concepts of core entities that significantly constitute or affect social engineering domain, together with 22 kinds of relations describing how these entities related to each other. It provides a formal and explicit knowledge schema to understand, analyze, reuse and share domain knowledge of social engineering. Furthermore, this paper builds a knowledge graph based on 15 social engineering attack incidents and scenarios. 7 knowledge graph application examples (in 6 analysis patterns) demonstrate that the ontology together with knowledge graph is useful to 1) understand and analyze social engineering attack scenario and incident, 2) find the top ranked social engineering threat elements (e.g. the most exploited human vulnerabilities and most used attack mediums), 3) find potential social engineering threats to victims, 4) find potential targets for social engineering attackers, 5) find potential attack paths from specific attacker to specific target, and 6) analyze the same origin attacks.

A reevaluation of the Late Jurassic dinosaur tracksite Barkhausen (Wiehengebirge, Northern Germany)

A restudy of the Barkhausen dinosaur tracksite shows that the track-bearing surface reveals considerably more detail than previously indicated, and a new map is presented, showing the trackways of nine sauropods, traveling north, possibly as a group. These are among the smallest sauropod tracks recorded in Europe. There is also evidence of two large theropods crossing the area, one moving to the south and the other to the west. Evidence of at least three other sauropods is registered in the form of isolated manus traces that represent larger individuals. Previous interpretations inferred that sauropod trackways trended south, and therefore suggested a predator chasing its prey as in the purported but controversial attack scenario claimed for the famous Paluxy River site in Texas. Based on the present study, this scenario is no longer tenable for the Barkhausen tracksite. The description of Elephantopoides barkhausensis (Kaever and Lapparent, 1974) shows that it represents a moderately wide gauge, but small manus sauropod and can be assigned under the ichnofamily label Parabrontopodidae. E. barkhausensis as originally defined was a nomen dubium, but it has since been re-described semi-formally, without renaming, we emend the description and assigned them to the ichnotaxon Parabrontopodus barkhausensis comb. nov. These tracks could have been produced by the small sauropod dinosaur taxon Europasaurus. The problematic ichnotaxon Megalosauropus teutonicus (Kaever and Lapparent, 1974), which represents a large three-toed theropod, is assigned to the recently described ichnogenus Jurabrontes from the Late Kimmeridgian of the Swiss Jura mountains as Jurabrontes teutonicus comb. nov. Furthermore, we attribute the theropod tracks from the time equivalent Langenberg quarry to the same ichnotaxon.