An Analysis of Security System for Intrusion in Smartphone Environment by Maya Louk, Hyotaek Lim, HoonJae Lee

“…There are many malware applications in Smartphone. Smartphone’s users may become unaware if their data has been recorded and stolen by intruders via malware. …”
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Identifikasi Malicious Host dalam Local Area Network Menggunakan Teknik Graph Clustering dan Filtering by Khafidzun Fadli, Achmad Basuki, Eko Setiawan

“…Penyebab LAN menjadi tidak aman dikarenakan teknologi firewall tidak mampu melindungi host (komputer) dalam LAN dari penyebaran malware. Penyebaran malware yang terdapat dalam LAN dilakukan oleh host di dalam LAN yang disebut sebagai malicious host. …”
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Pengukuran Kesadaran Keamanan Informasi Pegawai: Studi Kasus PT Meshindo Jayatama by Abdul Gofur, Rizal Fathoni Aji, Heri Kurniawan

“…Hasil wawancara dengan Presiden Direktur dan Manager Teknologi Informasi (TI) PT Meshindo Jayatama menyatakan bahwa telah terjadi serangan seperti Phishing dan Malware sebanyak 26 kali ditahun 2023. Dengan adanya serangan malware yang mengakibatkan terinfeksinya dokumen laporan perusahaan dan kerugian finansial yang disebabkan oleh faktor kelalaian sumber daya manusia, menjadi pertimbangan perlu untuk dilakukan pengukuran kesadaran keamanan informasi dan mengetahui area yang perlu ditingkatkan. …”
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Real-time Defense Against Cyber Threats: Analyzing Wazuh's Effectiveness in Server Monitoring by Alde Alanda, H.A Mooduto, Ronal Hadi

“…Wazuh's capabilities such as log data collection, malware detection, active response automation, and Docker container monitoring, are examined. …”
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A New Sensors-Based Covert Channel on Android by Ahmed Al-Haiqi, Mahamod Ismail, Rosdiadee Nordin

“…Accelerometers generate signals that reflect user motions, and malware applications can apparently only read their data. …”
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Behaviour Analysis of Mobile Malicious Software in Network by Xuetao Du, Ling Chang, Jia Liu, Chen Zhang

“…A behavior analysis method of mobile malicious softwares and a corresponding solution on the network-side were proposed.The method combined network behaviors and virus-inner behaviors of a mobile malicious software to improve the accuracy and the timeliness.The method was a useful guidance for communication operators to block mobile malware in real time.…”
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Research on Clue Mining in Criminal Cases of Smart Phone Trojan Horse under the Background of Information Security by Li Gang, Yong Wen

“…With the massive popularity and wide application of Android smartphones, there are more and more malware targeting Android smartphones. Research and analysis Android smart phone Trojan horses can provide corresponding technical support for malware detection on Android smart phones, which has good scientific research significance and broad market value. …”
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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System by Safdar Hussain Javed, Maaz Bin Ahmad, Muhammad Asif, Waseem Akram, Khalid Mahmood, Ashok Kumar Das, Sachin Shetty

“…Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. …”
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Approaches to cyber weapons classification problem by V. V. Kabernik

“…Working criteria for differentiation between information warfare and practical cyber warfare is suggested for proper classification of malware, espionage toolkits, hacking tools and combat software.…”
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Software Authority Transition through Multiple Distributors by Kyusunk Han, Taeshik Shon

“…Such openness, however, can promote serious threats, including malware and illegal usage. In order to prevent such threats, several stores use online authentication techniques. …”
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Research and practice of SASE cloud security by Chaoyang YE, Xin WANG, Shicong ZHANG, Zhiyong ZHAN, Yisha LIU

“…With the development of the Internet today, earth shaking changes have taken place compared with the traditional network.In today's network reform, ＆quot;cloud＆quot; has become the mainstream direction of the evolution of enterprise IT architecture, so the requirements for cloud security are becoming higher and higher.The practical deployment scheme of SASE cloud security architecture was introduced to solve the problem of cloud security for customers.A unique and complete SASE solution was proposed, which combined security functions with SD-WAN functions, and realized real-time monitoring, intelligent analysis and automatic interception of malware or malicious activities by building a unified management and control platform and centralized deployment of core security resource pool.Practice has proved that the scheme achieves the expected results in unified management and control, intrusion detection and defense, fine access control and so on, and can fully ensure the cloud security of enterprises.…”
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Analisis Kinerja Intrusion Detection System Berbasis Algoritma Random Forest Menggunakan Dataset Unbalanced Honeynet BSSN by Kuni Inayah, Kalamullah Ramli

“…Model mengklasifikasikan 10 (sepuluh) klasifikasi yaitu Benign, Information Leak, Malware, Trojan Activity, Information Gathering, APT, Exploit, Web Application Attack, Denial of Service (DoS), dan jenis serangan lainnya (other). …”
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Narrowing the semantic gap in virtual machine introspection by Chao-yuan CUI, Yun WU, Ping LI, Xiao-ming ZHANG

“…Virtual machine introspection(VMI)has been widely used in areas such as intrusion detection and malware analysis.However,due to the existence of semantic gap,the generality and the efficiency of VMI were partly influenced while getting internal information of a virtual machine.By analyzing the deficiencies of existing technology of semantic gap restoration,a method called ModSG was proposed to bridge the semantic gap.ModSG was a modularity system,it divided semantic restoration into two parts.One was online phase that interact directly with user to construct semantic views,the other was offline phase that only interact with operating system to parse high-level semantic knowledge.Both were implemented via independent module,and the latter provided the former with necessary kernel information during semantic view construction.Experiments on different virtual machine states and different kernel versions show that the ModSG is accurate and efficient in narrowing semantic gap.The modular design and deployment also make ModSG easily to be extended to other operating systems and virtualization platforms.…”
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Leaking Network Devices with Rubber Ducky Attack by Zeynep Rana Donmez, Şeyma Atmaca, Yıldıray YALMAN

“…Unlike traditional electronic attacks, it relies on manipulating individuals to run malware-infected devices or share sensitive information willingly. …”
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Survey on key technologies for protective DNS service by MA Yongzheng, LI Hongtao, MA Zhongsheng, HU Weihong, ZHANG Zhongxian

“…Common DNS abuses and related security threats on the client side include phishing, malware, and other attacks based on DNS. The protective DNS (PDNS) service leverages the existing DNS protocol and architecture to detect and mitigate threats by analyzing DNS queries, thereby protecting users against potential security threads at the source. …”
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A Review about Internet of Things (IoT) integration with Cloud Computing with a Limelight on Security by Umm e Kulsoom, Syeda Faiza Nasim, Asma Qaiser, Sidra Aziz, Syeda Alishba Fatima

“…Through an in-depth analysis of recent research, we examine the security challenges associated with IoT-based cloud computing, including account hacking, phishing, malware, middleman attacks, and service denial. We also discuss the potential benefits, architectural integration options, and the impact of IoT on cloud computing. …”
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Fusion of Focal Loss’s cyber threat intelligence entity extraction by Yuanbo GUO, Yongfei LI, Qingli CHEN, Chen FANG, Yangyang HU

“…Cyber threat intelligence contains a wealth of knowledge of threat behavior.Timely analysis and process of threat intelligence can promote the transformation of defense from passive to active.Nowadays, most threat intelligence that exists in the form of natural language texts contains a large amount of unstructured data, which needs to be converted into structured data for subsequent processing using entity extraction methods.However, since threat intelligence contains numerous terminology such as vulnerability names, malware and APT organizations, and the distribution of entities are extremely unbalanced, the performance of extraction methods in general field are severely limited when applied to threat intelligence.Therefore, an entity extraction model integrated with Focal Loss was proposed, which improved the cross-entropy loss function and balanced sample distribution by introducing balance factor and modulation coefficient.In addition, for the problem that threat intelligence had a complex structure and a wide range of sources, and contained a large number of professional words, token and character features were added to the model, which effectively improved OOV (out of vocabulary) problem in threat intelligence.Experiment results show that compared with existing mainstream model BiLSTM and BiLSTM-CRF, the F1 scores of the proposed model is increased by 7.07% and 4.79% respectively, which verifies the effectiveness of introducing Focal Loss and character features.…”
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Analisis Perilaku Entitas untuk Pendeteksian Serangan Internal Menggunakan Kombinasi Model Prediksi Memori dan Metode PCA by Rahmat - Budiarto, Yanif Dwi Kuntjoro

“…Serangan dari luar dapat dicegah menggunakan firewall, anti virus dan perangkat lunak khusus untuk pendeteksi penyusupan/malware. Penelitian ini bertujuan untuk membangun suatu model analisis perilaku entitas berazaskan Model Prediksi Memori (MPM) yang dikombinasikan dengan metode seleksi fitur principal component analysis (PCA) yang diimplementasikan untuk mendeteksi serangan/anomali siber yang melibatkan internal. …”
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Evaluating the Performance of a Static Patching Strategy against Computer Viruses by Da-Wen Huang, Lu-Xing Yang, Xiaofan Yang, Xiang Zhong, Yuan Yan Tang

“…Our findings provide a significant guidance for restraining malware propagation.…”
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Cyber Security Intrusion Detection Using a Deep Learning Method by Basheer Ullah, Shafiq-ur-Rehman Massan, M. Abdul Rehman, Rabia Ali Khan

“…Present methods to detect, predict and prevent malware still fall short of the desired level. The new techniques of deep learning are poised to succeed for detecting intrusion by employing different algorithms of detection and prevention. …”
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