Browsing by Author "Seneviratne, S"

Now showing 1 - 5 of 5

item: Article-Full-text
A multi-modal neural embeddings approach for detecting mobile counterfeit apps: A case study on google play store
(IEEE, 2022) Karunanayake, N; Rajasegaran, J; Gunathillake, A; Seneviratne, S; Jourjon, G
Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information, spreading malware, or simply to increase their advertisement revenue. Many counterfeits can be identified once installed, however even a techsavvy user may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted to be published in app markets. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings (given by the Gram matrix of CNN feature maps) outperforms the baseline methods for image similarity such as SIFT, SURF, LATCH, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that, content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. We also show that adding text embeddings further increases the performance by 5% and 6% in terms of precision@k and recall@k, respectively when k is five. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a conservative assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
item: Article-Full-text
Power control for body area networks: accurate channel prediction by lightweight deep learning
(IEEE, 2021) Yang, Y; Smith, D; Rajasegaran, J; Seneviratne, S
Recent advances in the Internet of Things (IoT) are reforming the health care industry by providing higher communication efficiency, lower costs, and higher mobility. Among the many IoT applications, wireless body area networks (BANs) are a remarkable solution caring for a rapidly growing aged population. Predictive transmit power control schemes improve BAN communications' reliability and energy efficiency through long-term optimal radio resources allocation that supports consistent pervasive healthcare services. Here, we propose LSTM-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2 s over nonstationary BAN on-body channels. An incremental learning scheme, which enables the LSTM predictor to operate online, is also developed for dynamic scenarios. Our main contribution is a lightweight NN predictor, “LiteLSTM,” that has a compact structure and higher computational efficiency than other variants. We show that LiteLSTM remains functional under an incremental learning scheme, with only marginal performance degradation when implemented on hand-held devices. For optimal power allocation, we develop an interquartile range (IQR)-based power control for our channel prediction. When extensively tested using empirical channel measurements at different sampling rates, our proposed methods outperform the existing state-of-the-art methods in terms of prediction accuracy, power consumption, level crossing rate (LCR), and outage probability and duration.
item: Conference-Abstract
Quantitative and qualitative evaluation of performance and robustness of image stitching algorithms
Dissanayake, V; Herath, S; Rasnayaka, S; Seneviratne, S; Vidanaarachchi, R; Gamage, CD
Many different image stitching algorithms, and mechanisms to assess their quality have been proposed by different research groups in the past decade. However, a comparison across different stitching algorithms and evaluation mechanisms has not been performed before. Our objective is to recognize the best algorithm for panoramic image stitching. We measure the robustness of different algorithms by means of assessing image quality of a set of panoramas. For the evaluation itself, a varied set of assessment criteria are used, and the evaluation is performed over a large range of images captured using differing cameras. In an ideal stitching algorithm, the resulting stitched image should be without visible seams and other noticeable anomalies. An objective evaluation for image quality should give results corresponding to a similar evaluation by the Human Visual System. Our conclusion is that the choice of stitching algorithm is scenario dependent, with run-time and accuracy being the primary considerations.
item: Article-Full-text
Robust open-set classification for encrypted traffic fingerprinting
(Elsevier, 2023-11-01) Dahanayaka, T; Ginige, Y; Huang, Y; Jourjon, G; Seneviratne, S
Encrypted network traffic has been known to leak information about their underlying content through side-channel information leaks. Traffic fingerprinting attacks exploit this by using machine learning techniques to threaten user privacy by identifying user activities such as website visits, videos streamed, and messenger app activities. Although state-of-the-art traffic fingerprinting attacks have high performances, even undermining the latest defenses, most of them are developed under the closed-set assumption. To deploy them in practical situations, it is important to adapt them to the open-set scenario, which allows the attacker to identify its target content while rejecting other background traffic. At the same time, in practice, these models need to be deployed on in-networking devices such as programmable switches, which have limited memory and computation power. Model weight quantization can reduce the memory footprint of deep learning models while at the same time, allowing inference to be done as integer operations as opposed to floating point operations. Open-set classification in the domain of traffic fingerprinting has not been explored well in prior work and none of them explored the effect of quantization on the open-set performance of such models. In this work, we propose a framework for robust open-set classification of encrypted traffic based on three key ideas. First, we show that a well-regularized deep learning model improves the open-set classification and then we propose a novel open-set classification method with three variants that perform consistently over multiple datasets. Next, we show that traffic fingerprinting models can be quantized without a significant drop in both closed-set and open-set accuracy and therefore, they can be readily deployed on in-network computing devices. Finally, we show that when the above three components are combined, the resulting open-set classifier outperforms all other open-set classification methods evaluated across five datasets with a minimum and maximum increase in F1_Score of 8.9% and 77.3% respectively.
item: Article-Full-text
Self-supervised vision rransformers for malware setection
(IEEE, 2022) Seneviratne, S; Shariffdeen, R; Rasnayaka, S; Kasthuriarachchi, N
Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is becoming inevitable to find a solution that can self-learn from unlabeled sample data. This paper presents SHERLOCK, a self-supervision based deep learning model to detect malware based on the Vision Transformer (ViT) architecture. SHERLOCK is a novel malware detection method which learns unique features to differentiate malware from benign programs with the use of image-based binary representation. Experimental results using 1.2 million Android applications across a hierarchy of 47 types and 696 families, shows that self-supervised learning can achieve an accuracy of 97% for the binary classification of malware which is higher than existing state-of-the-art techniques. Our proposed model is also able to outperform state-of-the-art techniques for multi-class malware classification of types and family with macro-F1 score of .497 and .491 respectively.