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"Seiring dengan perkembangan teknologi komunikasi data yang tinggi, permasalahan yang dihadapi pun semakin bervariasi. Salah satu permasalahan yang sering dialami adalah cyber attack, permasalahan ini dapat mengakibatkan kerugian tidak hanya informasi yang bocor, namun juga kerugian secara finansial yang diakibatkan dari transaksi illegal yang memanfaatkan informasi pribadi pengguna yang bocor, serta kerugian lainnya. Dengan adanya permasalahan ini, penulis berinisiatif dalam mengusung model deteksi anomali berdasarkan dataset NSL-KDD menggunakan machine learning model XGBoost dengan Optuna Tuning. XGBoost merupakan machine learning model yang mampu mengatasi overfitting pada simulasi yang diusulkan, dengan kombinasi Optuna Tuning, model machine learning yang diusung mampu bekerja efisien akibat dari adanya optimasi hyperparameter secara otomatis. Kinerja model yang diusulkan penulis berhasil mendapatkan akurasi 99,56%, dengan nilai precision 98,16%, nilai recall 99,82%, dan untuk nilai f-1 score 99,61%. Berdasarkan hasil simulasi tersebut menunjukkan bahwa model yang diusulkan penulis berhasil mendeteksi adanya anomali pada trafik serta memiliki sensitivitas yang tinggi.

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Along with the development of high data communication technology, the problems faced are increasingly varied. One of the problems that is often experienced is in form of cyber attacks, this problem affected to losses, not only leaked information which occred, but also financial losses caused by illegal transactions that utilized by using user personal information, as well as other losses. In concern of facing this problem, the author takes an initiative in carrying out an anomaly detection model based on the NSL-KDD dataset using XGBoost, machine learning model with Optuna Tuning. XGBoost is a machine learning model that is able to overcome overfitting in the proposed simulation, with a combination of Optuna Tuning, this machine learning model is able to work efficiently due to automatic hyperparameter optimization. That statement is proven by the performance of the model succesfully manage to get an accuracy of 99.56%, with a precision value of 98.16%, a recall value of 99.82%, and for an f-1 value of 99.61%. Based on the results of the simulation, it shows that the model proposed by the author has successfully detected anomalies in traffic and has a high sensitivity"

"Di era digital saat ini, serangan siber terus meningkat dengan berbagai modus, salah satunya adalah rekayasa sosial yang memanfaatkan psikologi manusia untuk mendapatkan akses tidak sah ke sistem dan informasi. Penelitian ini bertujuan untuk merancang kerangka kerja pencegahan rekayasa sosial dengan menggunakan pendekatan Kerangka Kerja Kepribadian Rekayasa Sosial dan NIST SP 800-53 Rev. 5 di Perusahaan XYZ. Penelitian ini mengidentifikasi elemen-elemen kunci dari kepribadian yang rentan terhadap rekayasa sosial melalui pengukuran menggunakan BFI-10 (Big Five Inventory Ten Item Scale) dan simulasi serangan phishing. BFI-10 adalah instrumen yang digunakan untuk mengukur lima dimensi utama ciri kepribadian, yaitu Ekstraversi, Keterbukaan, Mudah Setuju, Neurotisisme, dan Ketelitian. Hasil penelitian menunjukkan bahwa ditemukan korelasi antara prinsip persuasi dengan ciri-ciri kepribadian saat respon compromised. Walaupun demkian semua klasifikasi generasi rentan menjadi korban dari serangan rekayasa sosial. Kesadaran dan pelatihan keamanan yang dilakukan efektif dalam mengurangi ancaman rekayasa sosial dan berhasil menurunkan jumlah karyawan yang mengklik situs phishing sebesar 37% dan jumlah karyawan yang memasukkan data pribadi ke situs phishing sebesar 43%. Kemudian penelitian ini menghasilkan Kerangka Kerja Pencegahan Rekayaasa Sosial yang menggabungkan Kerangka Kerja Kepribadian Sosial dan kontrol keamanan yang terkait dengan aspek manusia dari NIST SP 800-53 Rev. 5. Tahapan kerangka kerja meliputi tahap asesmen risiko, tahap penetapan kebijakan dan prosedur, tahap operasional, serta tahap perbaikan berkelanjutan. Dengan demikian, penelitian ini memberikan kontribusi signifikan dalam membangun pertahanan yang lebih kuat dan responsif terhadap serangan rekayasa sosial, serta meningkatkan ketahanan siber perusahaan secara keseluruhan.

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In the current digital age, the frequency of cyber attacks is on the rise, employing several methods, including social engineering, which exploits human psychology to illicitly obtain access to networks and information. This research aims to create a framework for preventing social engineering utilizing the Social Engineering Personality Framework methodology and NIST SP 800-53 Rev. 5 at XYZ Company. This study analyzes the specific aspects of personality that are susceptible to manipulation through social engineering. It uses the BFI-10 (Big Five Inventory Ten Item Scale) and simulated phishing assaults to gather data. The BFI-10 is a tool utilized for assessing the big five personality traits, specifically Extraversion, Openness, Agreeableness, Neuroticism, and Conscientiousness. The results showed that there was a correlation between the principles of persuasion and personality traits when the response was compromised. However, all generation classifications are vulnerable to social engineering attacks. The security awareness and training initiatives reduced the number of employees clicking on phishing sites by 37% and the number of employees filling in personal data to phishing sites by 43%. This research then resulted in a Social Engineering Prevention Framework that incorporates the Social Personality Framework and the human-related security controls of NIST SP 800-53 Rev. 5. The framework consists of four stages: risk assessment, policy and process establishment, operational, and continual improvement. This research significantly contributes to enhancing the development of more robust and adaptive defenses against social engineering assaults, while also enhancing the overall cyber resilience of enterprises."

"This book outlines the complexity in understanding different forms of cyber attacks, the actors involved, and their motivations. It explores the key challenges in investigating and prosecuting politically motivated cyber attacks, the lack of consistency within regulatory frameworks, and the grey zone that this creates, for cybercriminals to operate within.Connecting diverse literatures on cyberwarfare, cyberterrorism, and cyberprotests, and categorising the different actors involved – state-sponsored/supported groups, hacktivists, online protestors – this book compares the means and methods used in attacks, the various attackers, and the current strategies employed by cybersecurity agencies. It examines the current legislative framework and proposes ways in which it could be reconstructed, moving beyond the traditional and fragmented definitions used to manage offline violence.This book is an important contribution to the study of cyber attacks within the areas of criminology, criminal justice, law, and policy. It is a compelling reading for all those engaged in cybercrime, cybersecurity, and digital forensics."