Tag Archive for: Detect

Bitdefender unveils App Anomaly Detection to detect malicious activity in Android apps


Bitdefender has unveiled App Anomaly Detection, the real-time, behavior-based protection layer available now in Bitdefender Mobile Security for Android, that continuously detects anomalous and malicious behavior in Android applications as it emerges.

The number of malicious and compromised Android applications available for download in popular app stores continues to grow as cybercriminal groups increasingly leverage the malware as a service (MaaS) model.

Bitdefender research identified dozens of Android applications totaling millions of downloads in the Google Play store in the last year alone that turned malicious after users installed them, with some acting as delivery mechanisms for mobile banking trojans that steal users’ login credentials.

Bitdefender App Anomaly Detection is a technology integrated into the Bitdefender Malware Scanner to provide an additional layer of protection by continuously monitoring and detecting any malicious behaviors and alerting the user if suspicious activities are identified.

Designed to help safeguard Android mobile users’ data, financial assets, and identities from fake or malicious applications, App Anomaly Detection protects users from known and unknown (zero-day) attacks that result in financial loss, account takeover, and identity fraud.

Other anti-malware solutions for Android, currently available on the market, use signature-based detection, that cybercriminals could evade by designing their mobile applications to only manifest malicious behaviors when certain conditions are met, or after a period of days or weeks after they are first downloaded.

Bitdefender App Anomaly Detection uses a combination of machine learning models, real-time behavior scanning, reputation systems, and other data points to continuously monitor and detect the moment an application turns from benign to malicious.

In this way, Bitdefender App Anomaly Detection protects users even when they have unknowingly installed a dangerous app that runs dormant for a period of time or a seemingly trusted app that breaks its functionality and turns rogue – all with minimal impact on battery life.

“Cybercriminals exploit users’ inherent trust of popular…

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How to detect most advance Russian FSB malware “Snake” in your network


How to detect most advance Russian FSB malware “Snake” in your network


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A new AI-based tool to detect DDoS attacks


IDS deployment on the ISP. Credit: Mustapha et al

Cybercriminals are coming up with increasingly savvy ways to disrupt online services, access sensitive data or crash internet user’s devices. A cyber-attack that has become very common over the past decades is the so-called Distributed Denial of Service (DDoS) attack.

This type of attack involves a series of devices connected to the internet, which are collectively referred to as a “botnet.” This “group” of connected devices is then used to flood a target server or website with “fake” traffic, disrupting its operation and making it inaccessible to legitimate users.

To protect their website or servers from DDoS attacks, businesses and other users commonly use firewalls, anti-malware software or conventional intrusion detection systems. Yet detecting these attacks can be very challenging today, as they are often carried out using generative adversarial networks (GANs), machine learning techniques that can learn to realistically mimic the activity of real users and legitimate user requests.

As a result, many existing anti-malware systems ultimately fail to secure users against them.

Researchers at Institut Polytechnique de Paris, Telecom Paris (INFRES) have recently developed a new computational method that could detect DDoS attacks more effectively and reliably. This method, introduced in a paper published in Computers & Security, is based on a long short-term memory (LSTM) model, a type of recurrent neural network (RNN) that can learn to detect long-term dependencies in event sequences.

“Our research paper was based on the problem of detecting DDoS attacks, a type of cyber-attacks that can cause significant damage to online services and network communication,” Ali Mustapha, one of the researchers who carried out the study, told Tech…

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People may ‘struggle to detect’ online scams with introduction of AI chatbots


Tech Expert Trevor Long says we may “struggle to detect” online scams in the future with the introduction of AI chatbots.

“Frankly, there may be a bigger challenge ahead for us in that sense,” Mr Long told Sky News Australia.

“That’s why we have internet security technology and things like that.”

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