Tag Archive for: deep

Ensemble averaging deep neural network for botnet detection in heterogeneous Internet of Things devices


In this section, the results of the simulation modeling and benchmarking study are presented and discussed. The findings of this research are discussed in the context of their impact on ensemble averaging for NIDS in heterogeneous IoT devices. Additionally, potential areas for future research in this field are highlighted.

Experiment environment

This research used a server with the following specifications: Processor 2.3 GHz 16-Core Intel(R) Xeon(R) CPU E5-2650 v3 and 128 GB memory. The operating system used was Ubuntu 22.04.2 LTS. Python version 3.10.6 and Keras version 2.12 were employed as the machine learning library for conducting the DNN experiments. Jupyter notebook version 6.5.3 was used for presenting the experiment and simulation results.

Preliminaries analysis

In this section, the explanation of results from both Scenario 1 and Scenario 2 is provided. The main objective of Scenario 1 was to assess the performance of individual DNN models constructed using device-specific traffic for the purpose of detecting botnet attacks occurring within the traffic of each respective device.

Table 7 Scenario 1 result.

The results of Scenario 1 are presented in Table 7. The findings indicate that the DNN models within each device exhibited robust performance when analyzing the traffic generated by that specific device. Notably, accuracy for each device reached 100%, signifying accurate identification of both true positive and true negative instances of botnet attacks within the corresponding device’s traffic. Precision and recall metrics also demonstrated performance exceeding 99%, implying the models’ ability to minimize misclassifications of normal traffic while accurately recognizing positive instances. Moreover, the DNN models achieved a high F1-score in detecting botnet attacks, highlighting their proficiency in both precision and recall aspects. Both training and prediction times for each model were influenced by dataset volume, with larger datasets leading to longer training and prediction durations. Remarkably, the model size remained consistent at around 70 Kb for each DNN model, indicating a stable size unaffected by variations in training data volume.

Figure 6
figure 6

Average accuracy,…

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A Deep Dive into the Cybersecurity Crisis


In the digital veins of our nation’s healthcare system, a formidable threat lurks, one that has recently intensified its focus on hospitals and healthcare providers. The BlackCat hacker gang, known also as ALPHV, has been unleashing a series of ransomware attacks, significantly disrupting operations and compromising sensitive patient data. This escalation, particularly evident since December, follows the group’s administrator’s call to arms for targeting the healthcare sector, a move that came hot on the heels of the FBI’s infiltration of its operations.

The Surge in Healthcare Attacks

Since mid-December, the healthcare industry has witnessed a notable uptick in ransomware attacks, with approximately 70 entities falling victim to BlackCat’s malicious endeavors. Among these, the cyberattack on Change Healthcare, an Optum subsidiary, stands out for its significant impact. On February 21, BlackCat claimed responsibility for the attack that rattled the U.S. healthcare payment and pharmacy processing systems nationwide. This incident not only highlighted the gang’s strategic shift towards critical infrastructure but also underscored the vulnerabilities existing within these essential services.

Moreover, the attack on the Lehigh Valley Health Network based in Allentown, Pennsylvania, brought a chilling new dimension to BlackCat’s operations. Sensitive photos of nude breast cancer patients were maliciously leaked online, exhibiting a gross violation of privacy and underscoring the potential for profound psychological impacts on victims.

Government and Industry Response

In response to the escalating threat, the FBI, along with the Department of Health and Human Services (HHS) and the Cybersecurity and Infrastructure Security Agency (CISA), issued a warning to healthcare providers about the resurgence of BlackCat ransomware attacks. This advisory highlighted the sophisticated tactics and ransomware variants used in these attacks, including the creation of victim-specific emails to facilitate their operations. The U.S. government has also put forth a reward of up to $15 million for information leading to the capture of key…

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A Deep Dive into Wi-Fi Analytics


Exploring the Future of Telecommunications: A Comprehensive Analysis of Wi-Fi Analytics

The future of telecommunications is a fascinating topic that is constantly evolving, with Wi-Fi analytics playing a pivotal role in shaping this landscape. As we delve deeper into the realm of Wi-Fi analytics, we uncover a world of possibilities that could revolutionize the way we communicate and interact with technology.

Wi-Fi analytics, at its core, is the process of collecting, analyzing, and interpreting data from Wi-Fi networks. This data can provide valuable insights into user behavior, network performance, and other critical aspects of a Wi-Fi network. With the advent of advanced technologies such as artificial intelligence and machine learning, the potential of Wi-Fi analytics has expanded exponentially.

One of the most significant developments in Wi-Fi analytics is the ability to track user behavior. By analyzing data from Wi-Fi networks, businesses can gain a deeper understanding of their customers’ habits and preferences. This information can be used to tailor services and products to meet customer needs more effectively, thereby enhancing customer satisfaction and loyalty.

Moreover, Wi-Fi analytics can also be used to optimize network performance. By analyzing data on network usage, businesses can identify bottlenecks and other issues that may be affecting the performance of their Wi-Fi networks. This can lead to more efficient network management and improved user experience.

In addition, Wi-Fi analytics can play a crucial role in enhancing security. By monitoring network activity, businesses can detect unusual patterns that may indicate a security breach. This can enable them to take proactive measures to protect their networks and data, thereby reducing the risk of cyber-attacks.

However, the potential of Wi-Fi analytics extends beyond these applications. With the advent of the Internet of Things (IoT), Wi-Fi analytics can play a crucial role in managing and optimizing the performance of IoT devices. By analyzing data from these devices, businesses can gain insights into their operation and usage, which can be used to enhance their functionality and efficiency.

Furthermore,…

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A Deep Dive into Modern Ransomware Attacks – CryptoMode


In a world where data is valuable, ransomware attacks have become a formidable threat to organizations worldwide. This concern continues to escalate with time, making it imperative for businesses to understand and address it effectively. The recent investigation by Microsoft’s Incident Response team into the relentless BlackByte 2.0 ransomware attacks has underscored these cyber onslaughts’ alarming rapidity and destructive capacity.

The Threatening Velocity of Ransomware Attacks

These cyber adversaries operate with astounding speed. The entire process can be alarmingly wrapped up in five days, from infiltrating systems to inflicting considerable damage. With such agility, these hackers can penetrate systems, encrypt valuable data, and demand a ransom for its release, leaving organizations scrambling to keep up.

In these attacks, the BlackByte ransomware surfaces in the final stage, employing an 8-digit number key to encrypt the data. The dynamics of these attacks underscore the use of a potent mix of tools and techniques, contributing to the high success rates of these malicious endeavors.

The investigation uncovered the troubling practice of exploiting unpatched Microsoft Exchange Servers. This tactic facilitates initial access to the target networks, setting the stage for further malevolent actions.

Blackbyte 2.0: Deceptive Strategies and Sophisticated Tools

Apart from using process hollowing and antivirus evasion techniques to ensure successful encryption, hackers also employ web shells. These allow remote access and control, enabling them to persist within the compromised systems, undetected. Additionally, the deployment of Cobalt Strike beacons furthers their command and control operations, arming them with various skills and making defense efforts more challenging for organizations.

To further avoid detection, cybercriminals cleverly use ‘living-off-the-land’ tools to camouflage their activities as legitimate processes. The BlackByte ransomware also manipulates volume shadow copies on infected machines to obstruct data recovery through system restore points. Specially crafted backdoors are deployed, allowing attackers to maintain access even…

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