针对全IP融合网络环境下的垃圾语音信息(spam over Internet telephony,SPIT)隐患,提出了一种基于反馈评判的检测与防范方法.该方法引入了终端用户的参与,结合信任与信誉机制,能够简单、高效、无损地利用直接和间接的反馈信息.改进的信...针对全IP融合网络环境下的垃圾语音信息(spam over Internet telephony,SPIT)隐患,提出了一种基于反馈评判的检测与防范方法.该方法引入了终端用户的参与,结合信任与信誉机制,能够简单、高效、无损地利用直接和间接的反馈信息.改进的信任度与信誉度推理算法充分体现了SPIT行为的分布特性并反映了影响评判结果的各因素的权重关系.增量学习算法保证了信任度和信誉度的实时性,融合算法则动态调整了信任度和信誉度在评判中的角色.实验及分析表明上述方法具有较好的准确性和敏感性,能够对SPIT进行有效的检测及防范.展开更多
针对基于SIP协议的SPIT攻击(Spam over Internet Telephony,SPIT),提出一种基于统计的SPIT检测方法。该方法提取用户多个行为属性和采用D-S理论将多个属性进行融合,实现对多种攻击方式的检测。同时,对域进行分类有区别地考虑域发动攻击...针对基于SIP协议的SPIT攻击(Spam over Internet Telephony,SPIT),提出一种基于统计的SPIT检测方法。该方法提取用户多个行为属性和采用D-S理论将多个属性进行融合,实现对多种攻击方式的检测。同时,对域进行分类有区别地考虑域发动攻击的可能性和用户的合法性,提高了检测的准确性。实验及分析表明上述方法具有较好的准确性,能够针对SPIT进行有效的检测。展开更多
Robo or unsolicited calls have become a persistent issue in telecommunication networks,posing significant challenges to individuals,businesses,and regulatory authorities.These calls not only trick users into disclosin...Robo or unsolicited calls have become a persistent issue in telecommunication networks,posing significant challenges to individuals,businesses,and regulatory authorities.These calls not only trick users into disclosing their private and financial information,but also affect their productivity through unwanted phone ringing.A proactive approach to identify and block such unsolicited calls is essential to protect users and service providers from potential harm.Therein,this paper proposes a solution to identify robo-callers in the telephony network utilising a set of novel features to evaluate the trustworthiness of callers in a network.The trust score of the callers is then used along with machine learning models to classify them as legitimate or robo-caller.We use a large anonymized dataset(call detailed records)from a large telecommunication provider containing more than 1 billion records collected over 10 days.We have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error rate.Specifically,the proposed features when used collectively achieve a true-positive rate of around 97%with a false-positive rate of less than 0.01%.展开更多
文摘针对全IP融合网络环境下的垃圾语音信息(spam over Internet telephony,SPIT)隐患,提出了一种基于反馈评判的检测与防范方法.该方法引入了终端用户的参与,结合信任与信誉机制,能够简单、高效、无损地利用直接和间接的反馈信息.改进的信任度与信誉度推理算法充分体现了SPIT行为的分布特性并反映了影响评判结果的各因素的权重关系.增量学习算法保证了信任度和信誉度的实时性,融合算法则动态调整了信任度和信誉度在评判中的角色.实验及分析表明上述方法具有较好的准确性和敏感性,能够对SPIT进行有效的检测及防范.
文摘针对基于SIP协议的SPIT攻击(Spam over Internet Telephony,SPIT),提出一种基于统计的SPIT检测方法。该方法提取用户多个行为属性和采用D-S理论将多个属性进行融合,实现对多种攻击方式的检测。同时,对域进行分类有区别地考虑域发动攻击的可能性和用户的合法性,提高了检测的准确性。实验及分析表明上述方法具有较好的准确性,能够针对SPIT进行有效的检测。
文摘Robo or unsolicited calls have become a persistent issue in telecommunication networks,posing significant challenges to individuals,businesses,and regulatory authorities.These calls not only trick users into disclosing their private and financial information,but also affect their productivity through unwanted phone ringing.A proactive approach to identify and block such unsolicited calls is essential to protect users and service providers from potential harm.Therein,this paper proposes a solution to identify robo-callers in the telephony network utilising a set of novel features to evaluate the trustworthiness of callers in a network.The trust score of the callers is then used along with machine learning models to classify them as legitimate or robo-caller.We use a large anonymized dataset(call detailed records)from a large telecommunication provider containing more than 1 billion records collected over 10 days.We have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error rate.Specifically,the proposed features when used collectively achieve a true-positive rate of around 97%with a false-positive rate of less than 0.01%.
