Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model. Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well. We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure

Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

Signal and Information Processing Methods for Embedded Robotic Tactile Sensing Systems

The human skin has several sensors with different properties and responses that are able to detect stimuli resulting from mechanical stimulations. Pressure sensors are the most important type of receptors for the exploration and manipulation of objects. In the last decades, smart tactile sensing based on different sensing techniques have been developed as their application in robotics and prosthetics is considered of huge interest, mainly driven by the prospect of autonomous and intelligent robots that can interact with the environment. However, regarding object properties estimation on robots, hardness detection is still a major limitation due to the lack of techniques to estimate it. Furthermore, finding processing methods that can interpret the measured information from multiple sensors and extract relevant information is a Challenging task. Moreover, embedding processing methods and machine learning algorithms in robotic applications to extract meaningful information such as object properties from tactile data is an ongoing challenge, which is controlled by the device constraints (power constraint, memory constraints, etc.), the computational complexity of the processing and machine learning algorithms, the application requirements (real-time operations, high prediction performance). In this dissertation, we focus on the design and implementation of pre-processing methods and machine learning algorithms to handle the aforementioned challenges for a tactile sensing system in robotic application. First, we propose a tactile sensing system for robotic application. Then we present efficient preprocessing and feature extraction methods for our tactile sensors. Then we propose a learning strategy to reduce the computational cost of our processing unit in object classification using sensorized Baxter robot. Finally, we present a real-time robotic tactile sensing system for hardness classification on a resource-constrained devices. The first study represents a further assessment of the sensing system that is based on the PVDF sensors and the interface electronics developed in our lab. In particular, first, it presents the development of a skin patch (multilayer structure) that allows us to use the sensors in several applications such as robotic hand/grippers. Second, it shows the characterization of the developed skin patch. Third, it validates the sensing system. Moreover, we designed a filter to remove noise and detect touch. The experimental assessment demonstrated that the developed skin patch and the interface electronics indeed can detect different touch patterns and stimulus waveforms. Moreover, the results of the experiments defined the frequency range of interest and the response of the system to realistic interactions with the sensing system to grasp and release events. In the next study, we presented an easy integration of our tactile sensing system into Baxter gripper. Computationally efficient pre-processing techniques were designed to filter the signal and extract relevant information from multiple sensor signals, in addition to feature extraction methods. These processing methods aim in turn to reduce also the computational complexity of machine learning algorithms utilized for object classification. The proposed system and processing strategy were evaluated on object classification application by integrating our system into the gripper and we collected data by grasping multiple objects. We further proposed a learning strategy to accomplish a trade-off between the generalization accuracy and the computational cost of the whole processing unit. The proposed pre-processing and feature extraction techniques together with the learning strategy have led to models with extremely low complexity and very high generalization accuracy. Moreover, the support vector machine achieved the best trade-off between accuracy and computational cost on tactile data from our sensors. Finally, we presented the development and implementation on the edge of a real–time tactile sensing system for hardness classification on Baxter robot based on machine and deep learning algorithms. We developed and implemented in plain C a set of functions that provide the fundamental layer functionalities of the Machine learning and Deep Learning models (ML and DL), along with the pre–processing methods to extract the features and normalize the data. The models can be deployed to any device that supports C code since it does not rely on any of the existing libraries. Shallow ML/DL algorithms for the deployment on resource–constrained devices are designed. To evaluate our work, we collected data by grasping objects of different hardness and shape. Two classification problems were addressed: 5 levels of hardness classified on the same objects’ shape, and 5 levels of hardness classified on two different objects’ shape. Furthermore, optimization techniques were employed. The models and pre–processing were implemented on a resource constrained device, where we assessed the performance of the system in terms of accuracy, memory footprint, time latency, and energy consumption. We achieved for both classification problems a real-time inference (< 0.08 ms), low power consumption (i.e., 3.35 μJ), extremely small models (i.e., 1576 Byte), and high accuracy (above 98%)

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Belief-Space Planning for Resourceful Manipulation and Mobility

Robots are increasingly expected to work in partially observable and unstructured environments. They need to select actions that exploit perceptual and motor resourcefulness to manage uncertainty based on the demands of the task and environment. The research in this dissertation makes two primary contributions. First, it develops a new concept in resourceful robot platforms called the UMass uBot and introduces the sixth and seventh in the uBot series. uBot-6 introduces multiple postural configurations that enable different modes of mobility and manipulation to meet the needs of a wide variety of tasks and environmental constraints. uBot-7 extends this with the use of series elastic actuators (SEAs) to improve manipulation capabilities and support safer operation around humans. The resourcefulness of these robots is complemented with a belief-space planning framework that enables task-driven action selection in the context of the partially observable environment. The framework uses a compact but expressive state representation based on object models. We extend an existing affordance-based object model, called an aspect transition graph (ATG), with geometric information. This enables object-centric modeling of features and actions, making the model much more expressive without increasing the complexity. A novel task representation enables the belief-space planner to perform general object-centric tasks ranging from recognition to manipulation of objects. The approach supports the efficient handling of multi-object scenes. The combination of the physical platform and the planning framework are evaluated in two novel, challenging, partially observable planning domains. The ARcube domain provides a large population of objects that are highly ambiguous. Objects can only be differentiated using multi-modal sensor information and manual interactions. In the dexterous mobility domain, a robot can employ multiple mobility modes to complete navigation tasks under a variety of possible environment constraints. The performance of the proposed approach is evaluated using experiments in simulation and on a real robot

Físchlár-DiamondTouch: collaborative video searching on a table

In this paper we present the system we have developed for our participation in the annual TRECVid benchmarking activity, specically the system we have developed, Físchlár-DT, for participation in the interactive search task of TRECVid 2005. Our back-end search engine uses a combination of a text search which operates over the automatic speech recognised text, and an image search which uses low-level image features matched against video keyframes. The two novel aspects of our work are the fact that we are evaluating collaborative, team-based search among groups of users working together, and that we are using a novel touch-sensitive tabletop interface and interaction device known as the DiamondTouch to support this collaborative search. The paper summarises the backend search systems as well as presenting the interface we have developed, in detail

Slaves to Technology: Worker control in the surveillance economy

Technology is enabling new forms of coercion and control over workers. While digital platforms for labour markets have been seen as benign or neutral technology, in reality they may enable new forms of worker exploitation. Workers in precarious conditions who seek employment via digital platforms are highly vulnerable to coercion and control via forms of algorithmic manipulation. This manipulation is enabled by information asymmetries, lack of labour protection, and predatory business models. When put together, these deficits create a perfect storm for labour exploitation. This article describes how digital platforms alter traditional labour relations, summarises case data from several existing studies, and details emerging forms of worker control and barriers to worker agency. It explores current definitions of forced labour and whether digital spaces require us to consider a new conceptualisation of what constitutes force, fraud, and coercion. It concludes with a summary of possible responses to these new forms of abuse in the global economy, including alternative models for business and for worker organising