The Experts below are selected from a list of 1020 Experts worldwide ranked by ideXlab platform

Yi Zhang - One of the best experts on this subject based on the ideXlab platform.

  • malware collusion attack against machine learning based methods issues and countermeasures
    International Conference on Cloud Computing, 2018
    Co-Authors: Hongyi Chen, Jinshu Su, Linbo Qiao, Yi Zhang
    Abstract:

    Android has become the most popular platform for mobile devices, and also it has become a popular target for malware developers. At the same time, researchers have proposed a large number of methods, both static and dynamic analysis methods, to fight against malwares. Among these, Machine learning based methods are quite effective in Android malware detection, the accuracy of which can be up to 98%. Thus, malware developers have the incentives to develop more advanced malwares to evade detection. This paper presents an adversary attack pattern that will compromise current machine learning based malware detection methods. The malware developers can perform this attack easily by splitting Malicious Payload into two or more apps. The split apps will all be classified as benign by current methods. Thus, we proposed a method to deal with this issue. This approach, realized in a tool, called ColluDroid, can identify the collusion apps by analyzing the communication between apps. The evaluation results show that ColluDroid is effective in finding out the collusion apps. Also, we showed that it’s easy to split an app to evade detection. According to our split simulation, the evasion rate is 78%, when split into two apps; while the evasion rate comes to 94.8%, when split into three apps.

Wei Ruan - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based Malicious Payload identification in software defined networking
    Journal of Network and Computer Applications, 2021
    Co-Authors: Qiumei Cheng, Haifeng Zhou, Dezhang Kong, Dong Zhang, Junchi Xing, Wei Ruan
    Abstract:

    Abstract Deep packet inspection (DPI) has been extensively investigated in software-defined networking (SDN) as complicated attacks may intractably inject Malicious Payloads in the packets. Existing proprietary pattern-based or port-based third-party DPI tools can suffer from limitations in efficiently processing a large volume of data traffic. In this paper, a novel OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on the SDN paradigm to provide adaptive and efficient packet inspection. First, OFDPI prescribes an early detection at the flow-level granularity by checking the IP addresses of each new flow via OpenFlow protocols. Then, OFDPI allows for deep packet inspection at the packet-level granularity: (i) for unencrypted packets, OFDPI extracts the features of accessible Payloads, including tri-gram frequency based on Term Frequency and Inverted Document Frequency (TF-IDF) and linguistic features. These features are concatenated into a sparse matrix representation and are then applied to train a binary classifier with logistic regression rather than matching with specific pattern combinations. In order to balance the detection accuracy and performance bottleneck of the SDN controller, OFDPI introduces an adaptive packet sampling window based on the linear prediction; and (ii) for encrypted packets, OFDPI extracts notable features of packets and then trains a binary classifier with a decision tree, instead of decrypting the encrypted traffic to weaken user privacy. A prototype of OFDPI is implemented on the Ryu SDN controller and the Mininet platform. The performance and the overhead of the proposed sulotion are assessed using the real-world datasets through experiments. The numerical results indicate that OFDPI can provide a significant improvement in detection accuracy with acceptable overheads.

Hongyi Chen - One of the best experts on this subject based on the ideXlab platform.

  • Malware Collusion Attack against SVM: Issues and Countermeasures
    Applied Sciences, 2018
    Co-Authors: Hongyi Chen, Linbo Qiao, Qin Xin
    Abstract:

    Android has become the most popular mobile platform, and a hot target for malware developers. At the same time, researchers have come up with numerous ways to deal with malware. Among them, machine learning based methods are quite effective in Android malware detection, the accuracy of which can be as high as 98%. Thus, malware developers have the incentives to develop more advanced malware to evade detection. This paper presents an adversary attack scenario (Collusion Attack) that will compromise current machine learning based malware detection methods, especially Support Vector Machines (SVM). The malware developers can perform this attack easily by splitting Malicious Payload into two or more apps. Meanwhile, attackers may hide their Malicious behavior by using advanced techniques (Evasion Attack), such as obfuscation, etc. According to our simulation, 87.4% of apps can evade Linear SVM by Collusion Attack. When performing Collusion and Evasion Attack simultaneously, the evasion rate can reach 100% at a low cost. Thus, we proposed a method to deal with this issue. This approach, realized in a tool, called ColluDroid, can identify the collusion apps by analyzing the communication between apps. In addition, it can integrate secure learning methods (e.g., Sec-SVM) to fight against Evasion Attack. The evaluation results show that ColluDroid is effective in finding out the collusion apps and ColluDroid-Sec-SVM has the best performance in the presence of both Collusion and Evasion Attack.

  • malware collusion attack against machine learning based methods issues and countermeasures
    International Conference on Cloud Computing, 2018
    Co-Authors: Hongyi Chen, Jinshu Su, Linbo Qiao, Yi Zhang
    Abstract:

    Android has become the most popular platform for mobile devices, and also it has become a popular target for malware developers. At the same time, researchers have proposed a large number of methods, both static and dynamic analysis methods, to fight against malwares. Among these, Machine learning based methods are quite effective in Android malware detection, the accuracy of which can be up to 98%. Thus, malware developers have the incentives to develop more advanced malwares to evade detection. This paper presents an adversary attack pattern that will compromise current machine learning based malware detection methods. The malware developers can perform this attack easily by splitting Malicious Payload into two or more apps. The split apps will all be classified as benign by current methods. Thus, we proposed a method to deal with this issue. This approach, realized in a tool, called ColluDroid, can identify the collusion apps by analyzing the communication between apps. The evaluation results show that ColluDroid is effective in finding out the collusion apps. Also, we showed that it’s easy to split an app to evade detection. According to our split simulation, the evasion rate is 78%, when split into two apps; while the evasion rate comes to 94.8%, when split into three apps.

Qiumei Cheng - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based Malicious Payload identification in software defined networking
    Journal of Network and Computer Applications, 2021
    Co-Authors: Qiumei Cheng, Haifeng Zhou, Dezhang Kong, Dong Zhang, Junchi Xing, Wei Ruan
    Abstract:

    Abstract Deep packet inspection (DPI) has been extensively investigated in software-defined networking (SDN) as complicated attacks may intractably inject Malicious Payloads in the packets. Existing proprietary pattern-based or port-based third-party DPI tools can suffer from limitations in efficiently processing a large volume of data traffic. In this paper, a novel OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on the SDN paradigm to provide adaptive and efficient packet inspection. First, OFDPI prescribes an early detection at the flow-level granularity by checking the IP addresses of each new flow via OpenFlow protocols. Then, OFDPI allows for deep packet inspection at the packet-level granularity: (i) for unencrypted packets, OFDPI extracts the features of accessible Payloads, including tri-gram frequency based on Term Frequency and Inverted Document Frequency (TF-IDF) and linguistic features. These features are concatenated into a sparse matrix representation and are then applied to train a binary classifier with logistic regression rather than matching with specific pattern combinations. In order to balance the detection accuracy and performance bottleneck of the SDN controller, OFDPI introduces an adaptive packet sampling window based on the linear prediction; and (ii) for encrypted packets, OFDPI extracts notable features of packets and then trains a binary classifier with a decision tree, instead of decrypting the encrypted traffic to weaken user privacy. A prototype of OFDPI is implemented on the Ryu SDN controller and the Mininet platform. The performance and the overhead of the proposed sulotion are assessed using the real-world datasets through experiments. The numerical results indicate that OFDPI can provide a significant improvement in detection accuracy with acceptable overheads.

Stringhini Gianluca - One of the best experts on this subject based on the ideXlab platform.

  • Eight Years of Rider Measurement in the Android Malware Ecosystem: Evolution and Lessons Learned
    2020
    Co-Authors: Suarez-tangil Guillermo, Stringhini Gianluca
    Abstract:

    Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malware has evolved over time, focusing on repackaging malware. In this type of threats different innocuous apps are piggybacked with a Malicious Payload (rider), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the Malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of Malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of Malicious activity performed by the Malicious samples and in the level of obfuscation used by malware to avoid detection. We then show that our framework can aid analysts who attempt to study unknown malware families. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community.Comment: A shorter version of this paper appears in IEEE Transactions on Dependable and Secure Computing. This is a more detailed version, extended from the pre-prin

  • Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned
    2018
    Co-Authors: Suarez-tangil Guillermo, Stringhini Gianluca
    Abstract:

    Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malware has evolved over time, focusing on repackaging malware. In this type of threats different innocuous apps are piggybacked with a Malicious Payload (rider), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the Malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of Malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of Malicious activity performed by the Malicious samples and in the level of obfuscation used by malware to avoid detection. We then show that our framework can aid analysts who attempt to study unknown malware families. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community.Accepted manuscrip