A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed

Aβ40 Oligomers Identified as a Potential Biomarker for the Diagnosis of Alzheimer's Disease

Alzheimer's Disease (AD) is the most prevalent form of dementia worldwide, yet the development of therapeutics has been hampered by the absence of suitable biomarkers to diagnose the disease in its early stages prior to the formation of amyloid plaques and the occurrence of irreversible neuronal damage. Since oligomeric Aβ species have been implicated in the pathophysiology of AD, we reasoned that they may correlate with the onset of disease. As such, we have developed a novel misfolded protein assay for the detection of soluble oligomers composed of Aβ x-40 and x-42 peptide (hereafter Aβ40 and Aβ42) from cerebrospinal fluid (CSF). Preliminary validation of this assay with 36 clinical samples demonstrated the presence of aggregated Aβ40 in the CSF of AD patients. Together with measurements of total Aβ42, diagnostic sensitivity and specificity greater than 95% and 90%, respectively, were achieved. Although larger sample populations will be needed to confirm this diagnostic sensitivity, our studies demonstrate a sensitive method of detecting circulating Aβ40 oligomers from AD CSF and suggest that these oligomers could be a powerful new biomarker for the early detection of AD

Person-of-Interest Detection on Mobile Forensics Data—AI-Driven Roadmap

The research problem addressed in the paper centers around the difficulty of identifying Persons of Interest (POIs) in law enforcement activity due to the vast amount of data stored on mobile devices. Given the complexity and volume of mobile forensic data, traditional analysis methods are often insufficient. The paper proposes leveraging Artificial Intelligence (AI) techniques, including machine learning and natural language processing, to improve the efficiency and effectiveness of data analysis in mobile forensics. This approach aims to overcome the limitations of manual data examination and enhance the identification process of POIs in a forensically sound manner. The main objective of the study is to explore and demonstrate the effectiveness of Artificial Intelligence techniques in improving the identification of POIs from mobile forensic data. The study proposes AI-driven approaches, particularly machine learning, and natural language processing, which can significantly enhance the efficiency, accuracy, and depth of analysis in mobile forensics, thereby addressing the challenges of handling vast amounts of data and the complexity of modern digital evidence. The study employs a quantitative research design, utilizing AI algorithms to process mobile forensic data from simulated environments. The study particularly demonstrates how deep learning can be utilized for searching POIs in WhatsApp messenger data. The result of the experiment shows that using AI for face recognition may throw false positive results, which means humans can’t be replaced in the stage of AI evolution. Also, results emphasize that using AI is helpful in mobile forensics data analysis and followed 88% of successful face recognition. The findings underscore the transformative potential of AI in mobile forensics, highlighting its capacity to enhance investigative accuracy and efficiency. This advancement could lead to more effective law enforcement and judicial processes by enabling quicker identification of POIs with higher precision. Moreover, the research underscores the importance of addressing ethical and privacy concerns in the application of AI technologies in forensic investigations, suggesting a balanced approach to leverage AI benefits while safeguarding individual rights

Resistance to Replay Attacks of Remote Control Protocols using the 433 MHz Radio Channel

This study focuses on the analysis of replay attacks, which pose a significant risk to remote control systems using the 433 MHz radio frequency band. A replay attack occurs when an attacker intercepts communications between two legitimate parties and resends the intercepted data to activate a remotely controlled system or commit identity theft. Special attention is paid to the study of the EV1527 protocol and its structure, as well as potential vulnerabilities that can be exploited by attackers. The study includes a detailed analysis of the design documentation on modules using the EV1527 protocol, as well as an assessment of the characteristics of the corresponding antennas and the features of working with hardware and software. The work also includes a comparative analysis of the technical means that can be used to carry out the attack and a demonstration of a practical attack using the HackRF One software-controlled transceiver in a laboratory setting. The main goal of the work is to demonstrate the mechanisms for implementing a replay attack on remote control systems with static code and to develop recommendations for improving the security of these systems. The results of the study are aimed at increasing the understanding of potential risks and vulnerabilities, as well as at determining the feasibility of using such protocols in modern physical security and access control systems

Mobile Application as a Critical Infrastructure Cyberattack Surface

Mobile applications are becoming increasingly crucial for critical infrastructure, ensuring effective management and reliable communication in today’s world. Postal services play a key role in logistics and serving citizens, providing a connection between people, the transfer of goods, and even delivering payments to the socially vulnerable segments of the population in remote regions. Mobile apps are increasingly becoming an integral part of postal services, offering convenience, speed, and ease of use for users, as well as access to additional features, such as scanning package barcodes and receiving notifications about shipment statuses. This article is dedicated to the security assessment of a mobile application of one of Ukraine’s postal operators, which undeniably constitutes an element of the state’s critical infrastructure. The research aims to evaluate the security of this app, considering potential threats and vulnerabilities that might arise during its operation. The study includes an analysis of the recommendations from popular security standards—ISO/IEC 27001:2022 and NIST Special Publication 800-163, and the application of static and dynamic analysis techniques to verify the security requirements established by OWASP Mobile Application Security Verification Standard (MASVS). The primary tool selected for this research is MobSF (Mobile Security Framework)—an automated, all-in-one framework for penetration testing, malware analysis, and security assessment of mobile apps (Android/iOS). The attack and the exploitation scenario of the identified vulnerabilities were verified in real time in an emulated environment. This article presents the vulnerabilities discovered in the mobile application. Our findings indicate the absence of usage confirmation and improper authorization for critically important functions, allowing malicious actors to remotely access the user’s personal information, including name, contacts, and address, by only knowing the user’s system identifier. Further, we propose countermeasures to protect the infrastructure and prevent adversaries from conducting reconnaissance and launching remote attacks using compromised accounts. The authors urge considering the possibility of applying the DevSecOps methodology when developing critical infrastructure information system applications