Search

The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solutions have been already used as a tool to supersede signature-based anti-malware systems. However, malware authors leverage features from malicious and legitimate samples to estimate statistical difference in-order to create adversarial examples. Hence, to evaluate the vulnerability of machine learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications (apps). By doing this, the classification algorithm inappropriately fits the discriminant function on the set of data points, eventually yielding a higher misclassification rate. Further, to distinguish the adversarial examples from benign samples, we propose two defense mechanisms to counter attacks. To validate our attacks and solutions, we test our model on three different benchmark datasets. We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix. Promising results show that generated adversarial samples can evade detection with a very high probability. Additionally, evasive variants generated by our attack models when used to harden the developed anti-malware system improves the detection rate up to 50% when using the generative adversarial network (GAN) method. [ABSTRACT FROM AUTHOR]

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature. [ABSTRACT FROM AUTHOR]

The idea of Internet of Things (IoT) is that many of the live objects (e.g., appliances) in the network are accessible, sensed, and interconnected. However, energy-constrained IoT nodes limit the performance of the IoT network. Hence, preserving energy in IoT network requires utmost attention. Unequal clustering is commonly considered as one of the efficient energy saving technique. Here, the traffic load is evenly distributed among the nodes using variable size clusters across the network. However, none of the existing solutions considered (1) realistic factors like fading model, routing protocol etc., or (2) optimization of cluster radius while devising clustering structure. The contribution of this paper is two-fold. First, we analyze the maximization of network lifetime by balancing the energy consumption among Cluster Heads (CHs). We found that cluster radius of each level has significant role in maximization of network lifetime. Second, to meet the requirement of maximization of network lifetime, this paper proposes a novel Lifetime Maximizing optimal Clustering Algorithm (LiMCA) for battery-powered IoT devices. Particularly, LiMCA includes a novel stochastic deployment scheme for Member Nodes (MNs) and CHs and a training protocol to train CHs and MNs about their coarse-grain location. Extensive simulation study shows that our algorithm improves the network lifetime by more than 30%, compared to other existing approaches. [ABSTRACT FROM AUTHOR]

The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large-scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table-driven and on-demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end-to-end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state-of-the-art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic-Based Clustering, Dynamic Link Duration Clustering, and mobility-based clustering algorithms. [ABSTRACT FROM AUTHOR]

Many dependable systems rely on the integrity of the position of their components. In such systems, two key problems are secure localization and secure location verification of the components. Researchers proposed several solutions, which generally require expensive infrastructures of several fixed stations (anchors) with trusted positions. In this paper, we explore the approach of replacing all the fixed anchors with a single drone that flies through a sequence of waypoints. At each waypoint, the drone acts as an anchor and securely determines the positions. This approach completely eliminates the need for many expensive anchors. The main challenge becomes how to find a convenient path for the drone to do this for all the devices. The problem presents novel aspects, which make existing path planning algorithms unsuitable. We propose LocalizerBee, VerifierBee, and PreciseVerifierBee: three path planning algorithms that allow a drone to respectively measure, verify, and verify with a guaranteed precision a set of positions in a secure manner. They are able to securely localize all the positions in a generic deployment area, even in the presence of drone control errors. Moreover, they produce short path lengths and they run in a reasonable processing time. [ABSTRACT FROM AUTHOR]