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Researchers from cybersecurity firm ESET said they discovered a brand of Android spyware called Kamran that is allegedly being distributed through a so-called watering hole attack involving a compromised news website called Hunza News.

The researchers said they contacted Hunza News about the issue with their apps but never got a response. The news outlet has existed since 2013, deriving its name either from Hunza District or Hunza Valley. The news site began offering its Android app in 2015 through the Google Play Store.

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Jonathan Greig is a Breaking News Reporter at Recorded Future News. Jonathan has worked across the globe as a journalist since 2014. Before moving back to New York City, he worked for news outlets in South Africa, Jordan and Cambodia. He previously covered cybersecurity at ZDNet and TechRepublic.

Users are urged to download apps only from trusted and official sources to protect themselves from such threats. A list of Indicators of Compromise (IoCs) related to the Kamran spyware attack is included in the ESET advisory.

ESET researchers have identified what appears to be a watering-hole attack on a regional news website that delivers news about Gilgit-Baltistan, a disputed region administered by Pakistan. When opened on a mobile device, the Urdu version of the Hunza News website offers readers the possibility to download the Hunza News Android app directly from the website, but the app has malicious espionage capabilities. We named this previously unknown spyware Kamran because of its package name com.kamran.hunzanews. Kamran is a common given name in Pakistan and other Urdu-speaking regions; in Farsi, which is spoken by some minorities in Gilgit-Baltistan, it means fortunate or lucky.

Upon launching, the malicious app prompts the user to grant it permissions to access various data. If accepted, it gathers data about contacts, calendar events, call logs, location information, device files, SMS messages, images, etc. As this malicious app has never been offered through the Google Play store and is downloaded from an unidentified source referred to as Unknown by Google, to install this app, the user is requested to enable the option to install apps from unknown sources.

The Hunza News website provides content in two languages: English and Urdu. Alongside English, Urdu holds national language status in Pakistan, and in Gilgit-Baltistan, it serves as the common or bridge language for interethnic communications. The official domain of Hunza News is hunzanews.net, registered on May 22nd, 2017, and has been consistently publishing online articles since then, as evidenced by Internet Archive data for hunzanews.net.

In 2015, hunzanews.com started to provide a legitimate Android application, as shown in Figure 3, which was available on the Google Play store. Based on available data we believe two versions of this app were released, with neither containing any malicious functionality. The purpose of these apps was to present the website content to readers in a user-friendly way.

In the second half of 2022, the new website hunzanews.net underwent visual updates, including the removal of the option to download the Android app from Google Play. Additionally, the official app was taken down from the Google Play store, likely due to its incompatibility with the latest Android operating systems.

Clicking on the DOWNLOAD APP button triggers a download from [.]net/wp-content/uploads/apk/app-release.apk. As this malicious app has never been offered through the Google Play store and is downloaded from a third-party site to install this app, the user is requested to enable the non-default, Android option to install apps from unknown sources.

In contrast, legitimate applications from Hunza News that were formerly available on Google Play were signed with a different developer certificate (SHA-1 fingerprint: BC2B7C4DF3B895BE4C7378D056792664FCEEC591). These clean and legitimate apps exhibit no code similarities with the identified malicious app.

Kamran is previously unknown Android spyware targeting Urdu-speaking people in the Gilgit-Baltistan region. Our research indicates that the malicious app containing Kamran has been distributed since at least 2023 via what probably is a watering-hole attack on a local, online newspaper named Hunza News.

Urdu-speaking readers of a regional news website that caters to the Gilgit-Baltistan region have likely emerged as a target of a watering hole attack designed to deliver a previously undocumented Android spyware dubbed Kamran.

The campaign, ESET has discovered, leverages Hunza News (urdu.hunzanews[.]net), which, when opened on a mobile device, prompts visitors of the Urdu version to install its Android app directly hosted on the website.

This includes contacts, call logs, calendar events, location information, files, SMS messages, photos, list of installed apps, and device metadata. The collected data is subsequently uploaded to a command-and-control (C2) server hosted on Firebase.

"As this malicious app has never been offered through the Google Play store and is downloaded from an unidentified source referred to as unknown by Google, to install this app, the user is requested to enable the option to install apps from unknown sources," security researcher Luk tefanko said.

Live News: Hindi, English & Urdu app brings you the latest English news, Hindi News & Urdu News breaking news central & state political headlines, Bollywood News & Photos, Cricket, Football, Business, Travel, Gadgets, Education & City news updates on the go from India and around the World.

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Geo News Urdu is a free application for Android that offers daily news. You can watch live news for sports, politics, entertainment, etc. in Pakistan and from around the world. You can also watch your favorite Pakistan TV shows such as Aaj Shahzeb with Khanzada, Capital Talk, Report Card, Lakan, Aaps Ki, and more.

With the rise of social media, the dissemination of forged content and news has been on the rise. Consequently, fake news detection has emerged as an important research problem. Several approaches have been presented to discriminate fake news from real news, however, such approaches lack robustness for multi-domain datasets, especially within the context of Urdu news. In addition, some studies use machine-translated datasets using English to Urdu Google translator and manual verification is not carried out. This limits the wide use of such approaches for real-world applications. This study investigates these issues and proposes fake news classier for Urdu news. The dataset has been collected covering nine different domains and constitutes 4097 news. Experiments are performed using the term frequency-inverse document frequency (TF-IDF) and a bag of words (BoW) with the combination of n-grams. The major contribution of this study is the use of feature stacking, where feature vectors of preprocessed text and verbs extracted from the preprocessed text are combined. Support vector machine, k-nearest neighbor, and ensemble models like random forest (RF) and extra tree (ET) were used for bagging while stacking was applied with ET and RF as base learners with logistic regression as the meta learner. To check the robustness of models, fivefold and independent set testing were employed. Experimental results indicate that stacking achieves 93.39%, 88.96%, 96.33%, 86.2%, and 93.17% scores for accuracy, specificity, sensitivity, MCC, ROC, and F1 score, respectively.

Fake news is engineered to deceive people and weaken public trust (Pierri & Ceri, 2019; Bozarth & Budak, 2020). To overcome this problem, an automated tool should be developed to check the authenticity or fakeness of news. Natural language processing (NLP) is an approach for handling text data that enables computers to interpret and interact with natural language (Wilks & Brewster, 2009). NLP facilitates the development of several applications, including text classification, question answering, machine translation, and much more (Church & Rau, 1995). Text classification has the most commonly utilized area in NLP problems being able to determine the semantic meaning of a sentence, word, or document (Manek et al., 2017).

Fake news detection is not easy and this task has several challenges. For example, the collection of the benchmark dataset and annotating it manually is a challenging task. This problem becomes more complicated with low-resource languages like Urdu for which a few online resources are available. Although there exist several approaches for Urdu fake news detection, they lack in several aspects. Such approaches do not use multi-domain data. Acquiring data from more domains allows for a more thorough evaluation of the performance of predictors. The performance of the models is better to be tested using data from a higher number of domains. Amjad et al. (2020) conducted a study to detect fake news with five domains. In addition, for dataset collection, some studies use Google Translate for English-to-Urdu translation but no manual verification is performed which reduces the scope of such models for real-world applications (Amjad et al., 2020; Akhter et al., 2021). Also, the number of samples in the dataset used in Amjad et al. (2020) is comparatively low due to which the models might not be trained and tested well. In previous works, fake news from five domains is considered with 900 samples only (Amjad et al., 2020).

Akhter et al. (2021) utilized an ensemble approach to detect fake news in the Urdu language. This study has contributed a new dataset along with experimentation on the benchmark dataset created by Amjad, Sidorov & Zhila (2020). However, this study lacks the ability to produce a highly accurate model for the identification of fake news in the Urdu language. In addition, this study has also used an English-translated dataset in Urdu language using Google Translate without manual verification. This study uses the data from a total of five domains only and its scope is small. ff782bc1db