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Intelligent Defenses: Advancing Cybersecurity through Machine Learning-Driven Malware Detection

Intelligent Defenses: Advancing Cybersecurity through Machin...

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International conference on communication Systems and Network Technologies (CSNT)

作者： Sakshi Mittal Prateek Rajvanshi

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

the exponential rise in cyberthreats has rendered malware detection a crucial component of cybersecurity. Traditional signature-based detection technologies are insufficient for combating emerging threat vectors, as increasingly complex malware targets sensitive systems. Machine learning techniques have emerged as a viable alternative for the precise detection and classification of malware through data-driven insights. this study presents a comprehensive malware detection system that combines Convolutional Neural Networks (CNN) with sophisticated machine learning (ML) methodologies, including ensemble techniques like Random Forest and Gradient Boosted Decision Trees. To rectify class imbalance and enhance model performance, the framework utilizes extensive preprocessing methods, including SMOTE. the Random Forest achieved the highest accuracy of 97.4%, followed by Gradient Boosted Decision Trees at 96.7%; the CNN demonstrated competitive performance at 95.0%. Dynamic API call sequences were identified as essential elements for precise classification. the proposed technique significantly reduces false negatives, hence ensuring improved protection against malicious software and enhancing recall. this research provides a scalable framework for future malware detection systems and demonstrates how machine learning may enhance cybersecurity protocols.

关键词： Accuracy Transformers Malware Vectors Real-time systems Convolutional neural networks Decision trees computer security Random forests Streams

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Perceptual Learning Technology Based on computer Vision and Machine Learning Algorithms

Perceptual Learning Technology Based on Computer Vision and ...

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International conference on communication Systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. the mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. this may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. the mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. the perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models computer vision Image segmentation Machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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Advanced Fault Sleuth System Architectures for Prognostic Anomaly Detection and Autonomous Irregularity Mitigation in Intelligent Power Networks

Advanced Fault Sleuth System Architectures for Prognostic An...

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International conference on communication Systems and Network Technologies (CSNT)

作者： Biresh Kumar Arpit Anurag Sanyam Anand Ritesh Kumar Kamaldeep Gupta Binod Kumar Sharma Amity Inst. of Information Technology Amity University Ranchi Jharkhand India Cambridge Institute Of Technology Tatisilwai Ranchi Jharkhand India St. Xavier’s College Ranchi Jharkhand India Dept. of Computer Sci. & Engg. BIT Mesra Ranchi Jharkhand India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Revenue losses, inconsistent service, delayed problem detection, and electricity theft are just a few of the major issues that power distribution networks deal with. the suggested Fault Sleuth System makes use of Internet of things (IoT) technologies to facilitate remote system control, automatic anomaly detection, and real-time electrical parameter monitoring. the technology guarantees accurate fault localization and proactive theft prevention by combining sophisticated data analytics and feature extraction techniques. the technology greatly lowers maintenance costs and security threats while improving operating efficiency through thorough data collecting, preprocessing, and intelligent analysis. In addition to improving problem detection and theft mitigation, this strategy also raises customer happiness, power quality, and service reliability. the study's conclusions show how the Fault Sleuth System can revolutionize the way that important power distribution issues are handled. this technology opens the door for a more robust, safe, and effective energy infrastructure that can satisfy needs in the future by leveraging real-time analytics and state-of-the-art IoT technologies.

关键词： Accuracy Prevention and mitigation Fault detection Power distribution Real-time systems Smart grids Internet of things security Predictive analytics Anomaly detection

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Privacy-Preserving Model for Cyber threat Intelligence, Sharing Across Multi-Organizational Platforms

Privacy-Preserving Model for Cyber Threat Intelligence, Shar...

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International conference on communication Systems and Network Technologies (CSNT)

作者： Shridhar Pandey H. Azath Rizwan Ur Rahman Hemraj Lamkuche School of Computing Science Engineering and Artificial Intelligence (SCAI) VIT Bhopal University Bhopal India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

the research presents a privacy-preserving structure for secure Cyber threat Intelligence (CTI) exchanging, using cutting-edge AI techniques to increase cooperative IT delicacy while keeping secret information safe. We propose a system based on Federated Learning, Differential Privacy and Homomorphic Encryption to model training in multiple organizations without exchanging the raw data. Using these privacy-preserving techniques together, organizations can collaborate to enhance their overall threat detection and response capabilities while maintaining the confidentiality of sensitive data. the implemented results indicate significant advancements with a 50%-55% detection accuracy overall for role intention measurement, model accuracy increase to 82% (predicting the next role intention), 30% computational overhead improvement, and a 10% improvement in novel threat detection. It provides 85% compliance against regulatory standards including GDPR and NIST. the applicability of the proposed solution on real-world data in the financial and healthcare domains demonstrates the proposed solution achieves a balance between privacy protection and CTI sharing still providing a powerful method for distributed, scalable and efficient collaboration in secure cybersecurity .

关键词： Differential privacy Privacy Accuracy Federated learning Collaboration Organizations threat assessment Cyber threat intelligence Homomorphic encryption computer security

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Securing Your Web Applications: the Power of Bugbite Vulnerability Scanner 14th

Securing Your Web Applications: The Power of Bugbite Vulner...

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14th International conference on Applications and Techniques in Information security, ATIS 2024

作者： thirunavukkarasu, K. Raju, Leo Sathishbabu, S. Yogeshwaran, V.N. Shafeeq Ahmed, Z. Department of Computer Science and Engineering Thanthai Periyar Government Institute of Technology Tamil Nadu Vellore India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Thanthai Periyar Government Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789819797424

the BugBite vulnerability scanner is a comprehensive tool developed to discover security flaws inside online web applications. By applying a combination of dynamic and static analysis approaches the BugBite successfully discovers an array of significant vulnerabilities which includes SQL injection, cross-site scripting (XSS), HTML injection, command injection, clickjacking, path traversal and cross-site request forgery (CSRF). this powerful scanner rigorously checks both the code and behavior of the web application, methodically discovering exploitable flaws that might be abused by malicious organizations. BugBite’s technique entails crawling the whole website by doing in-depth analyses of individual files and disclosing the underlying website structure. Following this first investigation phase, an automated audit is launched which subjecting the web application to a series of targeted assaults to measure its security posture. Augmenting its capabilities, BugBite combines the sophisticated Nmap tool to extend vulnerability detection across network systems and applications. through port scans and probing, possible security weak points are found that includes features like OS foot printing and service identification. Upon discovery of vulnerabilities, the scanner delivers a complete report summarizing the discovered issues along with recommended techniques for repair. this confluence of cutting edge approaches and integrated tools makes BugBite a powerful option for ensuring online application security. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： HTML

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Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data

Enhancing IoT Cyber Attack Detection in the Presence of High...

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International conference on communication Systems and Network Technologies (CSNT)

作者： Md. Ehsanul Haque Md. Saymon Hosen Polash Md Al-Imran Sanjida Simla Md Alomgir Hossain Sarwar Jahan Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Due to the rapid growth in the number of Internet of things (IoT) networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. High rate of missed threats can be the result as traditional machine learning models tend to struggle in identifying attacks as normal data volume is so much higher than the volume of attacks. For example, the data set used in this study reveals a strong class imbalance with 94,659 instances of the majority class and only 28 instances of the minority class, in which determining rare attacks accurately is quite challenging. the challenges presented in this research are addressed by hybrid sampling techniques designed to drive data imbalance detection accuracy in IoT domains. After doing so, we then evaluate the performance of several machine learning models such as Random Forest, Soft Voting, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP) and Logistic Regression with respect to the classification of cyber-attacks accurately. the obtained results indicate that the Random Forest model achieved the best performance value of 0.9903 of Kappa score, 0.9961 of test accuracy and 0.9994 of AUC. It also shows strong performance in the Soft Voting model, with an accuracy of 0.9952 and AUC of 0.9997, while the latter is an indication of the benefits of combining models’ prediction. Overall, this work has shown the great benefit of hybrid sampling combined with robust model selection and feature selection to deliver a dramatic increase in of IoT security against cyber-attack, an important factor for implementing security in environments with strongly imbalanced data.

关键词： Adaptation models Accuracy Predictive models Nearest neighbor methods Feature extraction Data models Internet of things Cyberattack Random forests Testing

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Denial of Service or Denial of security?

Denial of Service or Denial of Security?

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14th acm conference on computer and communication security

作者： Borisov, Nikita Danezis, George Mittal, Prateek Tabriz, Parisa Univ Illinois 1308 W Main St Urbana IL 61801 USA

ISBN: (纸本)9781595937032

We consider the effect attackers who disrupt anonymous communications have on the security of traditional high- and low-latency anonymous communication systems;as well as on the Hydra-Onion and Cashmere systems that aim to offer reliable mixing, and Salsa, a peer-to-peer anonymous communication network. We show that denial of service (DoS) lowers anonymity as messages need to get retransmitted to be delivered;presenting more opportunities for attack. We uncover a fundamental limit on the security of mix networks, showing that they cannot tolerate a majority of nodes being malicious. Cashmere, Hydra-Onion, and Salsa security is also badly affected by DoS attackers. Our results are backed by probabilistic modeling and extensive simulations and are of direct applicability to deployed anonymity systems.

关键词： Anonymity reliability denial of service attacks

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An Information-theoretic Model for Adaptive Side-Channel Attacks

An Information-Theoretic Model for Adaptive Side-Channel Att...

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14th acm conference on computer and communication security

作者： Koepf, Boris Basin, David ETH Zurich Switzerland

ISBN: (纸本)9781595937032

We present a model of adaptive side-channel attacks which we combine with information-theoretic metrics to quantify the information revealed to an attacker. this allows us to express an attacker's remaining uncertainty about a secret as a function of the number of side-channel measurements made. We present algorithms and approximation techniques for computing this measure. We also give examples of how they can be used to analyze the resistance of hardware implementations of cryptographic functions to both timing and power attacks.

关键词： security

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Improvement of communication Stability by Geo-Drone-Based SOA Routing Algorithm in Emergency Area Ad-Hoc Network 15th

Improvement of Communication Stability by Geo-Drone-Based SO...

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15th International conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, M. Rudra Kalyani, B.J.D. Suneetha, K. Satyam, K. HemaChandra, S. Madhavi, K. Reddy Department of Information Technology MGIT Hyderabad India Department of CSE Institute of Aeronautical Engineering Telangana Hyderabad India Department of Computer Science and Information Technology Jain (Deemed-to-Be University) Karnataka Bangalore India Department of MCA Annamacharya Institute of Technology and Sciences Andhra Pradesh Tirupati India Department of EEE School of Engineering Mohan Babu University A.P Tirupati India Department of AI and ML School of Computing Mohan Babu University A.P Tirupati India

ISBN: (纸本)9783031810855

Research into the use of drones to gather data from on-the-ground networks, known as the Internet of Drone things (IoDT), is now on the rise. Drone-assisted Internet of Vehicles (IoV) refers to the integration of (UAVs) into the IoV to address problems with traffic congestion, data security, and power use. the latency, overhead of the network are all negatively impacted by the IoV's distinctive features, such as its dynamic mobility and ad hoc traffic patterns. In addition, there may be a number of attackers around to wreak havoc on the flow of traffic. Drone-assisted IoV was created to address this shortcoming. Establishing communication connection that is tailored for wireless mobile networks, and in particular the disaster area network, is a major focus of current efforts. the current system is Internet Protocol (IP) based and places an emphasis on location-based communications. To address these issues, it is recommended that nations minimise their proximity to one another (node density) and stay away from highly mobile notes (node speed). By combining the snake optimizer with the sine cosine algorithm (SO-SCA), the authors offer a system with much lower overhead and faster nodes, better able to pinpoint the epicentre of a catastrophe and therefore boosting overall performance. this drone periodically checks in with the anchor node and provides data to maintain the drone-based disaster network in the region. the use of geo-tracking to enhance MANET routing efficiency shows great promise. the use of geographic navigation to enhance MANET routing performance shows promise. Packet latency, and packet damage are some of the metrics used to gauge performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drone things Geo-tracking Internet of Vehicles Internet Protocol Snake optimizer

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Split-Ballot Voting: Everlasting Privacy With Distributed Trust

Split-Ballot Voting: Everlasting Privacy With Distributed Tr...

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14th acm conference on computer and communication security

作者： Moran, Tal Naor, Moni Weizmann Inst Sci Dept Comp Sci & Appl Math IL-76100 Rehovot Israel

ISBN: (纸本)9781595937032

In this paper we propose a new voting protocol with desirable security properties. the voting stage of the protocol can be performed by humans without computers;it provides every voter with the means to verify that all the votes were counted correctly (universal verifiability) while preserving ballot secrecy. the protocol has "everlasting privacy": even a computationally unbounded adversary gains no information about specific votes from observing the protocol's output. Unlike previous protocols with these properties, this protocol distributes trust between two authorities: a single corrupt authority will not cause voter privacy to be breached. Finally, the protocol is receipt-free: a voter cannot prove how she voted even she wants to do so. We formally prove the security of the protocol in the Universal Composability framework, based on number-theoretic assumptions.

关键词： security theory Human Factors

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