Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares a...Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.展开更多
Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.da...Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.data confidentiality,integrity,and availability.Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats,which can be broadly classified into Signature-based Intrusion Detection Systems(SIDS)and Anomaly-based Intrusion Detection Systems(AIDS).This survey paper presents a taxonomy of contemporary IDS,a comprehensive review of notable recent works,and an overview of the datasets commonly used for evaluation purposes.It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.展开更多
基金Supported by the National High-Tech Research and Development Plan of China under Grant Nos.2006AA01Z445, 2006AA01Z410(国家高技术研究发展计划(863))the National Information Security Research Plan of China under Grant No.2006A30 (国家242信息安 全计划)+1 种基金the Electronic Development Fund of the Ministry of Information Industry of China under Grant No.[2006]634 (信息产业部电子发展基金)the IBM Ph.D. Fellowship Plan (IBM全球博士生英才计划)
文摘Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.
文摘Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.data confidentiality,integrity,and availability.Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats,which can be broadly classified into Signature-based Intrusion Detection Systems(SIDS)and Anomaly-based Intrusion Detection Systems(AIDS).This survey paper presents a taxonomy of contemporary IDS,a comprehensive review of notable recent works,and an overview of the datasets commonly used for evaluation purposes.It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.
