I. Obiri, Qi Xia, Hu Xia, Eric Affum, Abla Smahi, Jianbin Gao
The distribution of personal health records (PHRs) via a cloud server is a promising platform as it reduces the cost of data maintenance. Nevertheless, the cloud server is semi-trusted and can expose the patients’ PHRs to unauthorized third parties for financial gains or compromise the query result. Therefore, ensuring the integrity of the query results and privacy of PHRs as well as realizing fine-grained access control are critical key issues when PHRs are shared via cloud computing. Hence, we propose new personal health records sharing scheme with verifiable data integrity based on B+ tree data structure and attribute-based signcryption scheme to achieve data privacy, query result integrity, unforgeability, blind keyword search, and fine-grained access control.
{"title":"Personal health records sharing scheme based on attribute based signcryption with data integrity verifiable","authors":"I. Obiri, Qi Xia, Hu Xia, Eric Affum, Abla Smahi, Jianbin Gao","doi":"10.3233/jcs-210045","DOIUrl":"https://doi.org/10.3233/jcs-210045","url":null,"abstract":"The distribution of personal health records (PHRs) via a cloud server is a promising platform as it reduces the cost of data maintenance. Nevertheless, the cloud server is semi-trusted and can expose the patients’ PHRs to unauthorized third parties for financial gains or compromise the query result. Therefore, ensuring the integrity of the query results and privacy of PHRs as well as realizing fine-grained access control are critical key issues when PHRs are shared via cloud computing. Hence, we propose new personal health records sharing scheme with verifiable data integrity based on B+ tree data structure and attribute-based signcryption scheme to achieve data privacy, query result integrity, unforgeability, blind keyword search, and fine-grained access control.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117104297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent years have seen an increasing popularity of online collaborative systems like social networks and web-based collaboration platforms. Collaborative systems typically offer their users a digital environment in which they can work together and share resources and information. These resources and information might be sensitive and, thus, they should be protected from unauthorized accesses. Multi-party access control is emerging as a new paradigm for the protection of co-owned and co-managed resources, where the policies of all users involved in the management of a resource should be accounted for collaborative decision making. Existing approaches, however, only focus on the jointly protection of resources and do not address the protection of the individual user policies themselves, whose disclosure might leak sensitive information. In this work, we propose a privacy-preserving mechanism for the evaluation of multi-party access control policies, which preserves the confidentiality of user policies while remaining capable of making collaborative decisions. To this end, we design secure computation protocols for the evaluation of policies in protected form against an access query and realize such protocols using two privacy-preserving techniques, namely Homomorphic Encryption and Secure Functional Evaluation. We show the practical feasibility of our mechanism in terms of computation and communication costs through an experimental evaluation.
{"title":"Privacy-preserving policy evaluation in multi-party access control","authors":"M. Alishahi, Ischa Stork, Nicola Zannone","doi":"10.3233/jcs-200007","DOIUrl":"https://doi.org/10.3233/jcs-200007","url":null,"abstract":"Recent years have seen an increasing popularity of online collaborative systems like social networks and web-based collaboration platforms. Collaborative systems typically offer their users a digital environment in which they can work together and share resources and information. These resources and information might be sensitive and, thus, they should be protected from unauthorized accesses. Multi-party access control is emerging as a new paradigm for the protection of co-owned and co-managed resources, where the policies of all users involved in the management of a resource should be accounted for collaborative decision making. Existing approaches, however, only focus on the jointly protection of resources and do not address the protection of the individual user policies themselves, whose disclosure might leak sensitive information. In this work, we propose a privacy-preserving mechanism for the evaluation of multi-party access control policies, which preserves the confidentiality of user policies while remaining capable of making collaborative decisions. To this end, we design secure computation protocols for the evaluation of policies in protected form against an access query and realize such protocols using two privacy-preserving techniques, namely Homomorphic Encryption and Secure Functional Evaluation. We show the practical feasibility of our mechanism in terms of computation and communication costs through an experimental evaluation.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134361219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Network attacks have become a major security concern for organizations worldwide. A category of network attacks that exploit the logic (security) flaws of a few widely-deployed authentication protocols has been commonly observed in recent years. Such logic-flaw-exploiting network attacks often do not have distinguishing signatures, and can thus easily evade the typical signature-based network intrusion detection systems. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach based on protocol fuzzing to automatically generate high-quality network data, on which deep learning models can be trained for network attack detection. Our findings show that protocol fuzzing can generate data samples that cover real-world data, and deep learning models trained with fuzzed data can successfully detect the logic-flaw-exploiting network attacks.
{"title":"Deep learning for detecting logic-flaw-exploiting network attacks: An end-to-end approach","authors":"Qingtian Zou, A. Singhal, Xiaoyan Sun, Peng Liu","doi":"10.3233/jcs-210101","DOIUrl":"https://doi.org/10.3233/jcs-210101","url":null,"abstract":"Network attacks have become a major security concern for organizations worldwide. A category of network attacks that exploit the logic (security) flaws of a few widely-deployed authentication protocols has been commonly observed in recent years. Such logic-flaw-exploiting network attacks often do not have distinguishing signatures, and can thus easily evade the typical signature-based network intrusion detection systems. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach based on protocol fuzzing to automatically generate high-quality network data, on which deep learning models can be trained for network attack detection. Our findings show that protocol fuzzing can generate data samples that cover real-world data, and deep learning models trained with fuzzed data can successfully detect the logic-flaw-exploiting network attacks.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126606447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a definition of ballot secrecy as an indistinguishability game in the computational model of cryptography. Our definition improves upon earlier definitions to ensure ballot secrecy is preserved in the presence of an adversary that controls ballot collection. We also propose a definition of ballot independence as an adaptation of an indistinguishability game for asymmetric encryption. We prove relations between our definitions. In particular, we prove ballot independence is sufficient for ballot secrecy in voting systems with zero-knowledge tallying proofs. Moreover, we prove that building systems from non-malleable asymmetric encryption schemes suffices for ballot secrecy, thereby eliminating the expense of ballot-secrecy proofs for a class of encryption-based voting systems. We demonstrate applicability of our results by analysing the Helios voting system and its mixnet variant. Our analysis reveals that Helios does not satisfy ballot secrecy in the presence of an adversary that controls ballot collection. The vulnerability cannot be detected by earlier definitions of ballot secrecy, because they do not consider such adversaries. We adopt non-malleable ballots as a fix and prove that the fixed system satisfies ballot secrecy.
{"title":"Ballot secrecy: Security definition, sufficient conditions, and analysis of Helios","authors":"B. Smyth","doi":"10.3233/jcs-191415","DOIUrl":"https://doi.org/10.3233/jcs-191415","url":null,"abstract":"We propose a definition of ballot secrecy as an indistinguishability game in the computational model of cryptography. Our definition improves upon earlier definitions to ensure ballot secrecy is preserved in the presence of an adversary that controls ballot collection. We also propose a definition of ballot independence as an adaptation of an indistinguishability game for asymmetric encryption. We prove relations between our definitions. In particular, we prove ballot independence is sufficient for ballot secrecy in voting systems with zero-knowledge tallying proofs. Moreover, we prove that building systems from non-malleable asymmetric encryption schemes suffices for ballot secrecy, thereby eliminating the expense of ballot-secrecy proofs for a class of encryption-based voting systems. We demonstrate applicability of our results by analysing the Helios voting system and its mixnet variant. Our analysis reveals that Helios does not satisfy ballot secrecy in the presence of an adversary that controls ballot collection. The vulnerability cannot be detected by earlier definitions of ballot secrecy, because they do not consider such adversaries. We adopt non-malleable ballots as a fix and prove that the fixed system satisfies ballot secrecy.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Helen-Maria Dounavi, Anna Mpanti, Stavros D. Nikolopoulos, Iosif Polenakis
In this paper we present a graph-based framework that, utilizing relations between groups of System-calls, detects whether an unknown software sample is malicious or benign, and classifies a malicious software to one of a set of known malware families. In our approach we propose a novel graph representation of dependency graphs by capturing their structural evolution over time constructing sequential graph instances, the so-called Temporal Graphs. The partitions of the temporal evolution of a graph defined by specific time-slots, results to different types of graphs representations based upon the information we capture across the capturing of its evolution. The proposed graph-based framework utilizes the proposed types of temporal graphs computing similarity metrics over various graph characteristics in order to conduct the malware detection and classification procedures. Finally, we evaluate the detection rates and the classification ability of our proposed graph-based framework conducting a series of experiments over a set of known malware samples pre-classified into malware families.
{"title":"A graph-based framework for malicious software detection and classification utilizing temporal-graphs","authors":"Helen-Maria Dounavi, Anna Mpanti, Stavros D. Nikolopoulos, Iosif Polenakis","doi":"10.3233/jcs-210057","DOIUrl":"https://doi.org/10.3233/jcs-210057","url":null,"abstract":"In this paper we present a graph-based framework that, utilizing relations between groups of System-calls, detects whether an unknown software sample is malicious or benign, and classifies a malicious software to one of a set of known malware families. In our approach we propose a novel graph representation of dependency graphs by capturing their structural evolution over time constructing sequential graph instances, the so-called Temporal Graphs. The partitions of the temporal evolution of a graph defined by specific time-slots, results to different types of graphs representations based upon the information we capture across the capturing of its evolution. The proposed graph-based framework utilizes the proposed types of temporal graphs computing similarity metrics over various graph characteristics in order to conduct the malware detection and classification procedures. Finally, we evaluate the detection rates and the classification ability of our proposed graph-based framework conducting a series of experiments over a set of known malware samples pre-classified into malware families.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121180828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the great advances in spam detection, spam remains a major problem that has affected the global economy enormously. Spam attacks are popularly perpetrated through different digital platforms with a large electronic audience, such as emails, microblogging websites (e.g. Twitter), social networks (e.g. Facebook), and review sites (e.g. Amazon). Different spam detection solutions have been proposed in the literature, however, Machine Learning (ML) based solutions are one of the most effective. Nevertheless, most ML algorithms have computational complexity problem, thus some studies introduced Nature Inspired (NI) algorithms to further improve the speed and generalization performance of ML algorithms. This study presents a survey of recent ML-based and NI-based spam detection techniques to empower the research community with information that is suitable for designing effective spam filtering systems for emails, social networks, microblogging, and review websites. The recent success and prevalence of deep learning show that it can be used to solve spam detection problems. Moreover, the availability of large-scale spam datasets makes deep learning and big data solutions (such as Mahout) very suitable for spam detection. Few studies explored deep learning algorithms and big data solutions for spam detection. Besides, most of the datasets used in the literature are either small or synthetically created. Therefore, future studies can consider exploring big data solutions, big datasets, and deep learning algorithms for building efficient spam detection techniques.
{"title":"Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques","authors":"A. A. Akinyelu","doi":"10.3233/JCS-210022","DOIUrl":"https://doi.org/10.3233/JCS-210022","url":null,"abstract":"Despite the great advances in spam detection, spam remains a major problem that has affected the global economy enormously. Spam attacks are popularly perpetrated through different digital platforms with a large electronic audience, such as emails, microblogging websites (e.g. Twitter), social networks (e.g. Facebook), and review sites (e.g. Amazon). Different spam detection solutions have been proposed in the literature, however, Machine Learning (ML) based solutions are one of the most effective. Nevertheless, most ML algorithms have computational complexity problem, thus some studies introduced Nature Inspired (NI) algorithms to further improve the speed and generalization performance of ML algorithms. This study presents a survey of recent ML-based and NI-based spam detection techniques to empower the research community with information that is suitable for designing effective spam filtering systems for emails, social networks, microblogging, and review websites. The recent success and prevalence of deep learning show that it can be used to solve spam detection problems. Moreover, the availability of large-scale spam datasets makes deep learning and big data solutions (such as Mahout) very suitable for spam detection. Few studies explored deep learning algorithms and big data solutions for spam detection. Besides, most of the datasets used in the literature are either small or synthetically created. Therefore, future studies can consider exploring big data solutions, big datasets, and deep learning algorithms for building efficient spam detection techniques.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129439752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things (IoT) is evolving our society; however, the growing adoption of IoT devices in many scenarios brings security and privacy implications. Current security solutions are either unsuitable for every IoT scenario or provide only partial security. This paper presents AntibIoTic 2.0, a distributed security system that relies on Fog computing to secure IoT devices, including legacy ones. The system is composed of a backbone, made of core Fog nodes and Cloud server, a Fog node acting at the edge as the gateway of the IoT network, and a lightweight agent running on each IoT device. The proposed system offers fine-grained, host-level security coupled with network-level protection, while its distributed nature makes it scalable, versatile, lightweight, and easy to deploy, also for legacy IoT deployments. AntibIoTic 2.0 can also publish anonymized and aggregated data and statistics on the deployments it secures, to increase awareness and push cooperations in the area of IoT security. This manuscript recaps and largely expands previous works on AntibIoTic, providing an enhanced design of the system, an extended proof-of-concept that proves its feasibility and shows its operation, and an experimental evaluation that reports the low computational overhead it causes.
{"title":"AntibIoTic: The Fog-enhanced distributed security system to protect the (legacy) Internet of Things","authors":"Michele De Donno, Xenofon Fafoutis, N. Dragoni","doi":"10.3233/jcs-210027","DOIUrl":"https://doi.org/10.3233/jcs-210027","url":null,"abstract":"The Internet of Things (IoT) is evolving our society; however, the growing adoption of IoT devices in many scenarios brings security and privacy implications. Current security solutions are either unsuitable for every IoT scenario or provide only partial security. This paper presents AntibIoTic 2.0, a distributed security system that relies on Fog computing to secure IoT devices, including legacy ones. The system is composed of a backbone, made of core Fog nodes and Cloud server, a Fog node acting at the edge as the gateway of the IoT network, and a lightweight agent running on each IoT device. The proposed system offers fine-grained, host-level security coupled with network-level protection, while its distributed nature makes it scalable, versatile, lightweight, and easy to deploy, also for legacy IoT deployments. AntibIoTic 2.0 can also publish anonymized and aggregated data and statistics on the deployments it secures, to increase awareness and push cooperations in the area of IoT security. This manuscript recaps and largely expands previous works on AntibIoTic, providing an enhanced design of the system, an extended proof-of-concept that proves its feasibility and shows its operation, and an experimental evaluation that reports the low computational overhead it causes.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128742217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.
{"title":"Enhancement of email spam detection using improved deep learning algorithms for cyber security","authors":"Kadam Vikas Samarthrao, Vandana Milind Rohokale","doi":"10.3233/jcs-200111","DOIUrl":"https://doi.org/10.3233/jcs-200111","url":null,"abstract":"Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117237344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianguo Jiang, Boquan Li, Baole Wei, Gang Li, Chao Liu, Wei-qing Huang, Meimei Li, Min Yu
Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. More alarmingly, fake images or videos created by deep learning technologies, also known as Deepfakes, are more realistic, high-quality, and reveal few tampering traces, which attracts great attention in digital multimedia forensics research. To address those threats imposed by Deepfakes, previous work attempted to classify real and fake faces by discriminative visual features, which is subjected to various objective conditions such as the angle or posture of a face. Differently, some research devises deep neural networks to discriminate Deepfakes at the microscopic-level semantics of images, which achieves promising results. Nevertheless, such methods show limited success as encountering unseen Deepfakes created with different methods from the training sets. Therefore, we propose a novel Deepfake detection system, named FakeFilter, in which we formulate the challenge of unseen Deepfake detection into a problem of cross-distribution data classification, and address the issue with a strategy of domain adaptation. By mapping different distributions of Deepfakes into similar features in a certain space, the detection system achieves comparable performance on both seen and unseen Deepfakes. Further evaluation and comparison results indicate that the challenge has been successfully addressed by FakeFilter.
{"title":"FakeFilter: A cross-distribution Deepfake detection system with domain adaptation","authors":"Jianguo Jiang, Boquan Li, Baole Wei, Gang Li, Chao Liu, Wei-qing Huang, Meimei Li, Min Yu","doi":"10.3233/JCS-200124","DOIUrl":"https://doi.org/10.3233/JCS-200124","url":null,"abstract":"Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. More alarmingly, fake images or videos created by deep learning technologies, also known as Deepfakes, are more realistic, high-quality, and reveal few tampering traces, which attracts great attention in digital multimedia forensics research. To address those threats imposed by Deepfakes, previous work attempted to classify real and fake faces by discriminative visual features, which is subjected to various objective conditions such as the angle or posture of a face. Differently, some research devises deep neural networks to discriminate Deepfakes at the microscopic-level semantics of images, which achieves promising results. Nevertheless, such methods show limited success as encountering unseen Deepfakes created with different methods from the training sets. Therefore, we propose a novel Deepfake detection system, named FakeFilter, in which we formulate the challenge of unseen Deepfake detection into a problem of cross-distribution data classification, and address the issue with a strategy of domain adaptation. By mapping different distributions of Deepfakes into similar features in a certain space, the detection system achieves comparable performance on both seen and unseen Deepfakes. Further evaluation and comparison results indicate that the challenge has been successfully addressed by FakeFilter.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123844621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The scale of Software Defined Networks (SDN) is expanding rapidly and the demands for security reinforcement are increasing. SDN creates new targets for potential security threats such as the SDN controller and networking devices in the data plane. Violation of data plane integrity might lead to abnormal behaviors of the overall network. In this paper, we propose a new security approach for OpenFlow-based SDN in order to detect violation of switches flow tables integrity and successfully locate the compromised switches online. We cover all aspects of integrity violation including flow rule adding, modifying and removing by an unauthorized entity. We achieve this by using the cookie field in the OpenFlow protocol to put in a suitable digest (hash) value for each flow entry. Moreover, we optimize our method performance by calculating a global digest value for the entire switch’s flow table that decides whether a switch is suspected of being compromised. Our method is also able to determine and handle false alarms that affect the coherence of a corresponding table digest. The implementation is a reactive java module integrated with the Floodlight controller. In addition, we introduce a performance evaluation for three different SDN topologies.
{"title":"A new approach for detecting violation of data plane integrity in Software Defined Networks","authors":"Ghandi Hessam, Ghassan Saba, M. Alkhayat","doi":"10.3233/JCS-200094","DOIUrl":"https://doi.org/10.3233/JCS-200094","url":null,"abstract":"The scale of Software Defined Networks (SDN) is expanding rapidly and the demands for security reinforcement are increasing. SDN creates new targets for potential security threats such as the SDN controller and networking devices in the data plane. Violation of data plane integrity might lead to abnormal behaviors of the overall network. In this paper, we propose a new security approach for OpenFlow-based SDN in order to detect violation of switches flow tables integrity and successfully locate the compromised switches online. We cover all aspects of integrity violation including flow rule adding, modifying and removing by an unauthorized entity. We achieve this by using the cookie field in the OpenFlow protocol to put in a suitable digest (hash) value for each flow entry. Moreover, we optimize our method performance by calculating a global digest value for the entire switch’s flow table that decides whether a switch is suspected of being compromised. Our method is also able to determine and handle false alarms that affect the coherence of a corresponding table digest. The implementation is a reactive java module integrated with the Floodlight controller. In addition, we introduce a performance evaluation for three different SDN topologies.","PeriodicalId":142580,"journal":{"name":"J. Comput. Secur.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126848509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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