Increased crime and cyber-attacks make network security an essential prerequisite for organizations. However, organizations cannot guarantee this because the COVID-19 pandemic has forced organizations to suspend activ...
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The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more t...
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ISBN:
(数字)9798331506995
ISBN:
(纸本)9798331507008
The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats.
Increased crime and cyber-attacks make network security an essential prerequisite for organizations. However, organizations cannot guarantee this because the COVID-19 pandemic has forced organizations to suspend activ...
详细信息
ISBN:
(纸本)9781665496971
Increased crime and cyber-attacks make network security an essential prerequisite for organizations. However, organizations cannot guarantee this because the COVID-19 pandemic has forced organizations to suspend activities in the office and give employees the option to work from home. As a result, employees must always be connected to the home network to work. It can attract hackers to take advantage of the situation by launching various attacks. Therefore home network security must be updated, minimize vulnerabilities, and apply additional security. The number of IoT devices that can connect to the home network is also considered to increase security because the main entry point for hacking IoT is through the network. Raspberry Pi 4 can be used as a low-cost, power-efficient, and practical solution for home network security, including IDS Suricata, multiple honeypots (Cowrie & Dionaea), and Tshark packet analyzer. There are six types of attack simulations: port scanning, brute force, TCP flood attacks, smurf attacks, UDP flood attacks, and exploits on services/ports. Measurement of device performance is also carried out when running the system. Log data from the four sensors will be visualized with the ELK stack, making it easier to analyze attacks.
Hoax news is false information disseminated to deceive or mislead audiences, often with the aim of swaying opinions or creating confusion. The rise of social media has amplified the spread of hoax news, particularly i...
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ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
Hoax news is false information disseminated to deceive or mislead audiences, often with the aim of swaying opinions or creating confusion. The rise of social media has amplified the spread of hoax news, particularly in sensitive areas such as politics and health. In response to this growing issue, this paper proposes a Natural Language Processing (NLP) approach of detecting hoax news using the Smith-Waterman similarity algorithm. By comparing news content with a curated dataset of verified hoaxes, the system calculates a similarity score to assess the likelihood of the news being false. The results of this study show that news articles analyzed using the Smith-Waterman algorithm achieve a high accuracy, with a similarity score exceeding $93 \%$ for news inputs over 100 words. Furthermore, the proposed system demonstrates an efficient processing time, completing the analysis in approximately 6.57 seconds. These findings underscore the algorithm’s potential for real-time application in detecting fake news on social media and other digital platforms. This research aims not only to enhance the technical capabilities of hoax detection systems but also to foster greater media literacy and a more informed public.
The ransomware can encrypt the files on the victim's device and then offer a keyword to decrypt them with a ransom of money. Information about the basic structure of ransomware is needed so that an antivirus can d...
The ransomware can encrypt the files on the victim's device and then offer a keyword to decrypt them with a ransom of money. Information about the basic structure of ransomware is needed so that an antivirus can detect its presence. To find out the structure of ransomware, static and/or dynamic analysis can be done. In this study, ransomware analysis was performed using static techniques. The choice of static techniques was based on the ease of doing the analysis and also it does not need to run the malware sample being analyzed. The result of the research shows that of the six ransomware samples analyzed, it is known that all of the samples used almost the same structure in the form of imphash, ssdeep, and library and there are even samples come from the same family of ransomware.
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