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Guan, Chengli, Yang, Yue.  2019.  Research of Computer Network Security Evaluation Based on Backpropagation Neural Network. 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :181—184.
In recent years, due to the invasion of virus and loopholes, computer networks in colleges and universities have caused great adverse effects on schools, teachers and students. In order to improve the accuracy of computer network security evaluation, Back Propagation (BP) neural network was trained and built. The evaluation index and target expectations have been determined based on the expert system, with 15 secondary evaluation index values taken as input layer parameters, and the computer network security evaluation level values taken as output layer parameter. All data were divided into learning sample sets and forecasting sample sets. The results showed that the designed BP neural network exhibited a fast convergence speed and the system error was 0.000999654. Furthermore, the predictive values of the network were in good agreement with the experimental results, and the correlation coefficient was 0.98723. These results indicated that the network had an excellent training accuracy and generalization ability, which effectively reflected the performance of the system for the computer network security evaluation.
Matin, Iik Muhamad Malik, Rahardjo, Budi.  2019.  Malware Detection Using Honeypot and Machine Learning. 2019 7th International Conference on Cyber and IT Service Management (CITSM). 7:1–4.

Malware is one of the threats to information security that continues to increase. In 2014 nearly six million new malware was recorded. The highest number of malware is in Trojan Horse malware while in Adware malware is the most significantly increased malware. Security system devices such as antivirus, firewall, and IDS signature-based are considered to fail to detect malware. This happens because of the very fast spread of computer malware and the increasing number of signatures. Besides signature-based security systems it is difficult to identify new methods, viruses or worms used by attackers. One other alternative in detecting malware is to use honeypot with machine learning. Honeypot can be used as a trap for packages that are suspected while machine learning can detect malware by classifying classes. Decision Tree and Support Vector Machine (SVM) are used as classification algorithms. In this paper, we propose architectural design as a solution to detect malware. We presented the architectural proposal and explained the experimental method to be used.

Biswal, Satya Ranjan, Swain, Santosh Kumar.  2019.  Model for Study of Malware Propagation Dynamics in Wireless Sensor Network. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). :647–653.
Wireless Sensor Network (WSN) faces critical security challenges due to malware(worm, virus, malicious code etc.) attack. When a single node gets compromised by malware then start to spread in entire sensor network through neighboring sensor nodes. To understand the dynamics of malware propagation in WSN proposed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model. This model used the concept of epidemiology. The model focused on early detection of malicious signals presence in the network and accordingly application of security mechanism for its removal. The early detection method helps in controlling of malware spread and reduce battery consumption of sensor nodes. In this paper study the dynamics of malware propagation and stability analysis of the system. In epidemiology basic reproduction number is a crucial parameter which is used for the determination of malware status in the system. The expression of basic reproduction number has been obtained. Analyze the propagation dynamics and compared with previous model. The proposed model provides improved security mechanism in comparison to previous one. The extensive simulation results conform the analytical investigation and accuracy of proposed model.
Tenentes, Vasileios, Das, Shidhartha, Rossi, Daniele, Al-Hashimi, Bashir M..  2019.  Run-time Detection and Mitigation of Power-Noise Viruses. 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS). :275–280.
Power-noise viruses can be used as denial-of-service attacks by causing voltage emergencies in multi-core microprocessors that may lead to data corruptions and system crashes. In this paper, we present a run-time system for detecting and mitigating power-noise viruses. We present voltage noise data from a power-noise virus and benchmarks collected from an Arm multi-core processor, and we observe that the frequency of voltage emergencies is dramatically increasing during the execution of power-noise attacks. Based on this observation, we propose a regression model that allows for a run-time estimation of the severity of voltage emergencies by monitoring the frequency of voltage emergencies and the operating frequency of the microprocessor. For mitigating the problem, during the execution of critical tasks that require protection, we propose a system which periodically evaluates the severity of voltage emergencies and adapts its operating frequency in order to honour a predefined severity constraint. We demonstrate the efficacy of the proposed run-time system.
Janjua, K., Ali, W..  2018.  Enhanced Secure Mechanism for Virtual Machine Migration in Clouds. 2018 International Conference on Frontiers of Information Technology (FIT). :135–140.
Live VM migration is the most vulnerable process in cloud federations for DDOS attacks, loss of data integrity, confidentiality, unauthorized access and injection of malicious viruses on VM disk images. We have scrutinized following set of crucial security features which are; authorization, confidentiality, replay protection (accountability), integrity, mutual authentication and source non-repudiation (availability) to cater different threats and vulnerabilities during live VM migration. The investigated threats and vulnerabilities are catered and implemented in a proposed solution, presented in this paper. Six security features-authorization, confidentiality, replay protection, integrity, mutual authentication and source non-repudiation are focused and modular implementation has been done. Solution is validated in AVISPA tool in modules for threats for all the notorious security requirements and no outbreak were seen.
Headrick, W. J., Dlugosz, A., Rajcok, P..  2018.  Information Assurance in modern ATE. 2018 IEEE AUTOTESTCON. :1–4.

For modern Automatic Test Equipment (ATE) one of the most daunting tasks is now Information Assurance (IA). What was once at most a secondary item consisting mainly of installing an Anti-Virus suite is now becoming one of the most important aspects of ATE. Given the current climate of IA it has become important to ensure ATE is kept safe from any breaches of security or loss of information. Even though most ATE are not on the Internet (or even on a network for many) they are still vulnerable to some of the same attack vectors plaguing common computers and other electronic devices. This paper will discuss some of the processes and procedures which must be used to ensure that modern ATE can continue to be used to test and detect faults in the systems they are designed to test. The common items that must be considered for ATE are as follows: The ATE system must have some form of Anti-Virus (as should all computers). The ATE system should have a minimum software footprint only providing the software needed to perform the task. The ATE system should be verified to have all the Operating System (OS) settings configured pursuant to the task it is intended to perform. The ATE OS settings should include password and password expiration settings to prevent access by anyone not expected to be on the system. The ATE system software should be written and constructed such that it in itself is not readily open to attack. The ATE system should be designed in a manner such that none of the instruments in the system can easily be attacked. The ATE system should insure any paths to the outside world (such as Ethernet or USB devices) are limited to only those required to perform the task it was designed for. These and many other common configuration concerns will be discussed in the paper.

Amjad, N., Afzal, H., Amjad, M. F., Khan, F. A..  2018.  A Multi-Classifier Framework for Open Source Malware Forensics. 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :106-111.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

Qbeitah, M. A., Aldwairi, M..  2018.  Dynamic malware analysis of phishing emails. 2018 9th International Conference on Information and Communication Systems (ICICS). :18–24.

Malicious software or malware is one of the most significant dangers facing the Internet today. In the fight against malware, users depend on anti-malware and anti-virus products to proactively detect threats before damage is done. Those products rely on static signatures obtained through malware analysis. Unfortunately, malware authors are always one step ahead in avoiding detection. This research deals with dynamic malware analysis, which emphasizes on: how the malware will behave after execution, what changes to the operating system, registry and network communication take place. Dynamic analysis opens up the doors for automatic generation of anomaly and active signatures based on the new malware's behavior. The research includes a design of honeypot to capture new malware and a complete dynamic analysis laboratory setting. We propose a standard analysis methodology by preparing the analysis tools, then running the malicious samples in a controlled environment to investigate their behavior. We analyze 173 recent Phishing emails and 45 SPIM messages in search for potentially new malwares, we present two malware samples and their comprehensive dynamic analysis.

Alsulami, B., Mancoridis, S..  2018.  Behavioral Malware Classification Using Convolutional Recurrent Neural Networks. 2018 13th International Conference on Malicious and Unwanted Software (MALWARE). :103-111.

Behavioral malware detection aims to improve on the performance of static signature-based techniques used by anti-virus systems, which are less effective against modern polymorphic and metamorphic malware. Behavioral malware classification aims to go beyond the detection of malware by also identifying a malware's family according to a naming scheme such as the ones used by anti-virus vendors. Behavioral malware classification techniques use run-time features, such as file system or network activities, to capture the behavioral characteristic of running processes. The increasing volume of malware samples, diversity of malware families, and the variety of naming schemes given to malware samples by anti-virus vendors present challenges to behavioral malware classifiers. We describe a behavioral classifier that uses a Convolutional Recurrent Neural Network and data from Microsoft Windows Prefetch files. We demonstrate the model's improvement on the state-of-the-art using a large dataset of malware families and four major anti-virus vendor naming schemes. The model is effective in classifying malware samples that belong to common and rare malware families and can incrementally accommodate the introduction of new malware samples and families.

Jain, D., Khemani, S., Prasad, G..  2018.  Identification of Distributed Malware. 2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS). :242-246.

Smartphones have evolved over the years from simple devices to communicate with each other to fully functional portable computers although with comparatively less computational power but inholding multiple applications within. With the smartphone revolution, the value of personal data has increased. As technological complexities increase, so do the vulnerabilities in the system. Smartphones are the latest target for attacks. Android being an open source platform and also the most widely used smartphone OS draws the attention of many malware writers to exploit the vulnerabilities of it. Attackers try to take advantage of these vulnerabilities and fool the user and misuse their data. Malwares have come a long way from simple worms to sophisticated DDOS using Botnets, the latest trends in computer malware tend to go in the distributed direction, to evade the multiple anti-virus apps developed to counter generic viruses and Trojans. However, the recent trend in android system is to have a combination of applications which acts as malware. The applications are benign individually but when grouped, these may result into a malicious activity. This paper proposes a new category of distributed malware in android system, how it can be used to evade the current security, and how it can be detected with the help of graph matching algorithm.

Bak, D., Mazurek, P..  2018.  Air-Gap Data Transmission Using Screen Brightness Modulation. 2018 International Interdisciplinary PhD Workshop (IIPhDW). :147–150.

Air-gap data is important for the security of computer systems. The injection of the computer virus is limited but possible, however data communication channel is necessary for the transmission of stolen data. This paper considers BFSK digital modulation applied to brightness changes of screen for unidirectional transmission of valuable data. Experimental validation and limitations of the proposed technique are provided.

Tsuda, Y., Nakazato, J., Takagi, Y., Inoue, D., Nakao, K., Terada, K..  2018.  A Lightweight Host-Based Intrusion Detection Based on Process Generation Patterns. 2018 13th Asia Joint Conference on Information Security (AsiaJCIS). :102–108.
Advanced persistent threat (APT) has been considered globally as a serious social problem since the 2010s. Adversaries of this threat, at first, try to penetrate into targeting organizations by using a backdoor which is opened with drive-by-download attacks, malicious e-mail attachments, etc. After adversaries' intruding, they usually execute benign applications (e.g, OS built-in commands, management tools published by OS vendors, etc.) for investigating networks of targeting organizations. Therefore, if they penetrate into networks once, it is difficult to rapidly detect these malicious activities only by using anti-virus software or network-based intrusion systems. Meanwhile, enterprise networks are managed well in general. That means network administrators have a good grasp of installed applications and routinely used applications for employees' daily works. Thereby, in order to find anomaly behaviors on well-managed networks, it is effective to observe changes executing their applications. In this paper, we propose a lightweight host-based intrusion detection system by using process generation patterns. Our system periodically collects lists of active processes from each host, then the system constructs process trees from the lists. In addition, the system detects anomaly processes from the process trees considering parent-child relationships, execution sequences and lifetime of processes. Moreover, we evaluated the system in our organization. The system collected 2, 403, 230 process paths in total from 498 hosts for two months, then the system could extract 38 anomaly processes. Among them, one PowerShell process was also detected by using an anti-virus software running on our organization. Furthermore, our system could filter out the other 18 PowerShell processes, which were used for maintenance of our network.
Nallusamy, T., Ravi, R..  2017.  Node energy based virus propagation model for bluetooth. 2017 International Conference on Communication and Signal Processing (ICCSP). :1778–1780.

With the continuous development of mobile based Wireless technologies, Bluetooth plays a vital role in smart-phone Era. In such scenario, the security measures are needed to be enhanced for Bluetooth. We propose a Node Energy Based Virus Propagation Model (NBV) for Bluetooth. The algorithm works with key features of node capacity and node energy in Bluetooth network. This proposed NBV model works along with E-mail worm Propagation model. Finally, this work simulates and compares the virus propagation with respect to Node Energy and network traffic.

Lee, Y., Choi, S. S., Choi, J., Song, J..  2017.  A Lightweight Malware Classification Method Based on Detection Results of Anti-Virus Software. 2017 12th Asia Joint Conference on Information Security (AsiaJCIS). :5–9.

With the development of cyber threats on the Internet, the number of malware, especially unknown malware, is also dramatically increasing. Since all of malware cannot be analyzed by analysts, it is very important to find out new malware that should be analyzed by them. In order to cope with this issue, the existing approaches focused on malware classification using static or dynamic analysis results of malware. However, the static and the dynamic analyses themselves are also too costly and not easy to build the isolated, secure and Internet-like analysis environments such as sandbox. In this paper, we propose a lightweight malware classification method based on detection results of anti-virus software. Since the proposed method can reduce the volume of malware that should be analyzed by analysts, it can be used as a preprocess for in-depth analysis of malware. The experimental showed that the proposed method succeeded in classification of 1,000 malware samples into 187 unique groups. This means that 81% of the original malware samples do not need to analyze by analysts.

Chakraborty, S., Stokes, J. W., Xiao, L., Zhou, D., Marinescu, M., Thomas, A..  2017.  Hierarchical learning for automated malware classification. MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM). :23–28.

Despite widespread use of commercial anti-virus products, the number of malicious files detected on home and corporate computers continues to increase at a significant rate. Recently, anti-virus companies have started investing in machine learning solutions to augment signatures manually designed by analysts. A malicious file's determination is often represented as a hierarchical structure consisting of a type (e.g. Worm, Backdoor), a platform (e.g. Win32, Win64), a family (e.g. Rbot, Rugrat) and a family variant (e.g. A, B). While there has been substantial research in automated malware classification, the aforementioned hierarchical structure, which can provide additional information to the classification models, has been ignored. In this paper, we propose the novel idea and study the performance of employing hierarchical learning algorithms for automated classification of malicious files. To the best of our knowledge, this is the first research effort which incorporates the hierarchical structure of the malware label in its automated classification and in the security domain, in general. It is important to note that our method does not require any additional effort by analysts because they typically assign these hierarchical labels today. Our empirical results on a real world, industrial-scale malware dataset of 3.6 million files demonstrate that incorporation of the label hierarchy achieves a significant reduction of 33.1% in the binary error rate as compared to a non-hierarchical classifier which is traditionally used in such problems.

Hassen, M., Carvalho, M. M., Chan, P. K..  2017.  Malware classification using static analysis based features. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). :1–7.

Anti-virus vendors receive hundreds of thousands of malware to be analysed each day. Some are new malware while others are variations or evolutions of existing malware. Because analyzing each malware sample by hand is impossible, automated techniques to analyse and categorize incoming samples are needed. In this work, we explore various machine learning features extracted from malware samples through static analysis for classification of malware binaries into already known malware families. We present a new feature based on control statement shingling that has a comparable accuracy to ordinary opcode n-gram based features while requiring smaller dimensions. This, in turn, results in a shorter training time.

Jiao, L., Yin, H., Guo, D., Lyu, Y..  2017.  Heterogeneous Malware Spread Process in Star Network. 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW). :265–269.

The heterogeneous SIS model for virus spread in any finite size graph characterizes the influence of factors of SIS model and could be analyzed by the extended N-Intertwined model introduced in [1]. We specifically focus on the heterogeneous virus spread in the star network in this paper. The epidemic threshold and the average meta-stable state fraction of infected nodes are derived for virus spread in the star network. Our results illustrate the effect of the factors of SIS model on the steady state infection.

Kim, H., Yoo, D., Kang, J. S., Yeom, Y..  2017.  Dynamic Ransomware Protection Using Deterministic Random Bit Generator. 2017 IEEE Conference on Application, Information and Network Security (AINS). :64–68.

Ransomware has become a very significant cyber threat. The basic idea of ransomware was presented in the form of a cryptovirus in 1995. However, it was considered as merely a conceptual topic since then for over a decade. In 2017, ransomware has become a reality, with several famous cases of ransomware having compromised important computer systems worldwide. For example, the damage caused by CryptoLocker and WannaCry is huge, as well as global. They encrypt victims' files and require user's payment to decrypt them. Because they utilize public key cryptography, the key for recovery cannot be found in the footprint of the ransomware on the victim's system. Therefore, once infected, the system cannot be recovered without paying for restoration. Various methods to deal this threat have been developed by antivirus researchers and experts in network security. However, it is believed that cryptographic defense is infeasible because recovering a victim's files is computationally as difficult as breaking a public key cryptosystem. Quite recently, various approaches to protect the crypto-API of an OS from malicious codes have been proposed. Most ransomware generate encryption keys using the random number generation service provided by the victim's OS. Thus, if a user can control all random numbers generated by the system, then he/she can recover the random numbers used by the ransomware for the encryption key. In this paper, we propose a dynamic ransomware protection method that replaces the random number generator of the OS with a user-defined generator. As the proposed method causes the virus program to generate keys based on the output from the user-defined generator, it is possible to recover an infected file system by reproducing the keys the attacker used to perform the encryption.

Muthumanickam, K., Ilavarasan, E..  2017.  Optimizing Detection of Malware Attacks through Graph-Based Approach. 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC). :87–91.

Today the technology advancement in communication technology permits a malware author to introduce code obfuscation technique, for example, Application Programming Interface (API) hook, to make detecting the footprints of their code more difficult. A signature-based model such as Antivirus software is not effective against such attacks. In this paper, an API graph-based model is proposed with the objective of detecting hook attacks during malicious code execution. The proposed model incorporates techniques such as graph-generation, graph partition and graph comparison to distinguish a legitimate system call from malicious system call. The simulation results confirm that the proposed model outperforms than existing approaches.

Gonzalez, D., Hayajneh, T..  2017.  Detection and Prevention of Crypto-Ransomware. 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). :472–478.

Crypto-ransomware is a challenging threat that ciphers a user's files while hiding the decryption key until a ransom is paid by the victim. This type of malware is a lucrative business for cybercriminals, generating millions of dollars annually. The spread of ransomware is increasing as traditional detection-based protection, such as antivirus and anti-malware, has proven ineffective at preventing attacks. Additionally, this form of malware is incorporating advanced encryption algorithms and expanding the number of file types it targets. Cybercriminals have found a lucrative market and no one is safe from being the next victim. Encrypting ransomware targets business small and large as well as the regular home user. This paper discusses ransomware methods of infection, technology behind it and what can be done to help prevent becoming the next victim. The paper investigates the most common types of crypto-ransomware, various payload methods of infection, typical behavior of crypto ransomware, its tactics, how an attack is ordinarily carried out, what files are most commonly targeted on a victim's computer, and recommendations for prevention and safeguards are listed as well.

Bhattacharya, S., Kumar, C. R. S..  2017.  Ransomware: The CryptoVirus Subverting Cloud Security. 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). :1–6.

Cloud computing presents unlimited prospects for Information Technology (IT) industry and business enterprises alike. Rapid advancement brings a dark underbelly of new vulnerabilities and challenges unfolding with alarming regularity. Although cloud technology provides a ubiquitous environment facilitating business enterprises to conduct business across disparate locations, security effectiveness of this platform interspersed with threats which can bring everything that subscribes to the cloud, to a halt raises questions. However advantages of cloud platforms far outweighs drawbacks and study of new challenges helps overcome drawbacks of this technology. One such emerging security threat is of ransomware attack on the cloud which threatens to hold systems and data on cloud network to ransom with widespread damaging implications. This provides huge scope for IT security specialists to sharpen their skillset to overcome this new challenge. This paper covers the broad cloud architecture, current inherent cloud threat mechanisms, ransomware vulnerabilities posed and suggested methods to mitigate it.

Li-xiong, Z., Xiao-lin, X., Jia, L., Lu, Z., Xuan-chen, P., Zhi-yuan, M., Li-hong, Z..  2015.  Malicious URL prediction based on community detection. 2015 International Conference on Cyber Security of Smart Cities, Industrial Control System and Communications (SSIC). :1–7.

Traditional Anti-virus technology is primarily based on static analysis and dynamic monitoring. However, both technologies are heavily depended on application files, which increase the risk of being attacked, wasting of time and network bandwidth. In this study, we propose a new graph-based method, through which we can preliminary detect malicious URL without application file. First, the relationship between URLs can be found through the relationship between people and URLs. Then the association rules can be mined with confidence of each frequent URLs. Secondly, the networks of URLs was built through the association rules. When the networks of URLs were finished, we clustered the date with modularity to detect communities and every community represents different types of URLs. We suppose that a URL has association with one community, then the URL is malicious probably. In our experiments, we successfully captured 82 % of malicious samples, getting a higher capture than using traditional methods.

Trajanovski, S., Kuipers, F. A., Hayel, Y., Altman, E., Mieghem, P. Van.  2015.  Designing virus-resistant networks: A game-formation approach. 2015 54th IEEE Conference on Decision and Control (CDC). :294–299.

Forming, in a decentralized fashion, an optimal network topology while balancing multiple, possibly conflicting objectives like cost, high performance, security and resiliency to viruses is a challenging endeavor. In this paper, we take a game-formation approach to network design where each player, for instance an autonomous system in the Internet, aims to collectively minimize the cost of installing links, of protecting against viruses, and of assuring connectivity. In the game, minimizing virus risk as well as connectivity costs results in sparse graphs. We show that the Nash Equilibria are trees that, according to the Price of Anarchy (PoA), are close to the global optimum, while the worst-case Nash Equilibrium and the global optimum may significantly differ for small infection rate and link installation cost. Moreover, the types of trees, in both the Nash Equilibria and the optimal solution, depend on the virus infection rate, which provides new insights into how viruses spread: for high infection rate τ, the path graph is the worst- and the star graph is the best-case Nash Equilibrium. However, for small and intermediate values of τ, trees different from the path and star graphs may be optimal.

Yajin Zhou, Xuxian Jiang.  2012.  Dissecting Android Malware: Characterization and Evolution. Security and Privacy (SP), 2012 IEEE Symposium on. :95-109.

The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.