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This research paper presents MLDroid—a web-based framework—which helps to detect malware from Android devices. Due to increase in the popularity of Android devices, malware developers develop malware on daily basis to threaten the system integrity and user’s privacy. The proposed framework detects malware from Android apps by performing its dynamic analysis. To detect malware from real-world apps, we trained our proposed framework by selecting features which are gained by implementing feature selection approaches. Further, these selected features help to build a model by considering different machine learning algorithms. Experiment was performed on 5,00,000 plus Android apps. Empirical result reveals that model developed by considering all the four distinct machine learning algorithms parallelly (i.e., deep learning algorithm, farthest first clustering, Y-MLP and nonlinear ensemble decision tree forest approach) and rough set analysis as a feature subset selection algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.

With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the “silent battle” of cybernetics. To protect Android based devices from malware has become more complex and crucial for academicians and researchers. The main vulnerability lies in the underlying permission model of Android apps. Android apps demand permission or permission sets at the time of their installation. In this study, we consider permission and API calls as features that help in developing a model for malware detection. To select appropriate features or feature sets from thirty different categories of Android apps, we implemented ten distinct feature selection approaches. With the help of selected feature sets we developed distinct models by using five different unsupervised machine learning algorithms. We conduct an experiment on 5,00,000 distinct Android apps which belongs to thirty distinct categories. Empirical results reveals that the model build by considering rough set analysis as a feature selection approach, and farthest first as a machine learning algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.

Cloud computing is a computer science paradigm that has grown significantly in recent years. It provides on-demand access to a diverse set of software, infrastructure and platform services through the internet. However, due to their diversity and functional similarities, selecting trustworthy cloud services is a challenge. The absence of adequate trust evaluation methods for cloud services has hampered the widespread adoption of cloud computing. To address this issue and assist customers in selecting trustworthy cloud services, this paper presents a framework Selection of Trustworthy Cloud Services (SelTCS). SelTCS selects services by combining objective and subjective trust. A novel objective trust assessment approach has been presented that prioritizes quality-of-service attributes according to user preferences. Also, a novel subjective trust assessment approach is proposed which evaluates trust as a combination of reputation assessment based on aggregated user feedback that employs a modified hypertext induced topic search-based algorithm for identifying and removing malicious users, and direct trust based on users’ own experiences gained through direct interactions. Experiments using the Quality of Web Services (QWS) version 1.0 and Epinions datasets reveal that SelTCS greatly enhances the accuracy of trust evaluation and is more effective than existing approaches at detecting malicious user ratings.

This paper proposes a novel hybrid multi-objective optimization algorithm named HMOSHSSA by synthesizing the strengths of Multi-objective Spotted Hyena Optimizer (MOSHO) and Salp Swarm Algorithm (SSA). HMOSHSSA utilizes the exploration capability of MOSHO to explore the search space effectively and leader and follower selection mechanism of SSA to achieve global best solution with faster convergence. The proposed algorithm is evaluated on 24 benchmark test functions, and its performance is compared with seven well-known multi-objective optimization algorithms. The experimental results demonstrate that HMOSHSSA acquires very competitive results and outperforms other algorithms in terms of convergence speed, search-ability and accuracy. Additionally, HMOSHSSA is also applied on seven well-known engineering problems to further verify its efficacy. The results reveal the effectiveness of proposed algorithm toward solving real-life multi-objective optimization problems.

In the present computer era, the vulnerabilities inherent in the Internet architecture enable various kinds of attacks. Distributed Denial of Service (DDoS) is one of such prominent attack that is a lethal threat to Internet domain that harnesses its computing and communication resources. The increase in network traffic rates of legitimate traffic and its flow similarity with attack traffic has made the DDoS detection very difficult despite deployment of diversified defense solutions. The ISPs are bound to invest heavily to counter such problems which has a significant impact on company finances. To provide uninterrupted quality services to the end users, ISPs needs to deploy a distributed solution for timely detection and discrimination of attack and behaviorally similar flash events (FE) traffic. Such distributed defense systems can be deployed at source-end, intermediate network-end or at the victim-end location. Since the volume of traffic to be analyzed is very large, the detection accuracy and low computational complexity of the proposed defense solution is always a challenging problem. This paper proposes an ISP level distributed, collaborative and automated (D-CAD) defense system for detecting DDoS attacks and FEs, and has the capability to effectively distinguishing the two. Additionally, D-CAD defense system is also capable of categorizing FE traffic and has low computational complexity. The proposed system is validated in novel software defined networks (SDN) using Mininet emulator. The results show that D-CAD defense system outperformed its existing counterparts on various detection system evaluation metrics.

With all the brisk growth of web, distributed denial of service attacks are becoming the most serious issues in a data center scenarios where lot many servers are deployed. A Distributed Denial of Service attack gen-erates substantial packets by a large number of agents and can easily tire out the processing and communication resources of a victim within very less period of time. Defending DDoS problem involved several steps from detection, characterization and traceback in order todomitigation. The contribution of this research paper is a lot more. Firstly, flooding based DDoS problems is detected using obtained packets based entropy approach in a data center scenario. Secondly entropy based traceback method is applied to find the edge routers from where the whole attack traffic is entering into the ISP domain of the data center. Various simulation scenarios using NS2 are depicted in order to validate the proposed method using GT-ITM primarily based topology generators. Information theory based metrics like entropy; average entropy and differential entropy are used for this purpose.

Recent wireless networks observe number of deployments in various conditions where they come across different intensities of link disconnection. On the basis of extent of the operating circumstances these networks are termed as Intermittently Connected Networks (ICNs). The prevailing TCP/IP protocol cannot be operational in ICNs thus providing number of new stimulating problems that are appealing the focus of the researchers. The multi-copy routing schemes achieve higher delivery probability as compared to the single copy routing scheme. This improvement is achieved at the cost of higher resource utilization i.e. multi-copy routing protocols requires more buffer space, more bandwidth, incur more overheads and consume other vital network resources. Contribution of this work is the deletion of useless replicas of the messages which are already delivered to the intended destination. We evaluate our proposed method by simulation, on four major DTNs routing algorithms: Epidemic, Spray and Wait, ProPHET and MaxProp.

Mobile ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any stand-alone infrastructure or centralized administration. Mobile ad-hoc network have the attributes such as wireless connection, continuously changing topology, distributed operation and ease of deployment. In this paper we have compared the performance of two reactive MANET routing protocol AODV and DSR by using Group mobility model. Both share similar On-Demand behavior, but the protocol's internal mechanism leads to significant performance difference. We have analyzed the performance of protocols by varying network load, mobility and type of traffic (CBR and TCP). Group Mobility model has been generated by IMPORTANT (Impact of Mobility Patterns on Routing in Ad-hoc NeTwork) tool. A detailed simulation has been carried out in NS2. The metrics used for performance analysis are Packet Delivery Fraction, Average end-to-end Delay, Routing Overhead and Normalized Routing Load. It has been observed that AODV gives better performance in CBR traffic and real time delivery of packet. Where as DSR gives better results in TCP traffic and under restricted bandwidth condition.

This article aims to discuss the nodes in the Delay Tolerant Network (DTN) work on the foundation of cooperation in the network. When working in a cooperative manner, these nodes consume some network resources like bandwidth, buffer space, etc. Like any other networks, DTNs are also prone to the malicious nodes and different attacks. In this work, the researchers have proposed an attack model comprising of falsification of extended routing protocol metadata information combined with drop all attack. They have proposed the attack model definition and analyzed the performance of extended Epidemic routing protocol of DTN under this attack model. From the simulation results, they analyzed that the delivery probability of extended Epidemic routing protocols is greatly affected by the proposed attack model whereas the DTN routing protocols are proved to be robust against the individual attacks when implemented independently of each other.

Salmonella enterica subspecies enterica is traditionally subdivided into serovars by serological and nutritional characteristics. We used Multilocus Sequence Typing (MLST) to assign 4,257 isolates from 554 serovars to 1092 sequence types (STs). The majority of the isolates and many STs were grouped into 138 genetically closely related clusters called eBurstGroups (eBGs). Many eBGs correspond to a serovar, for example most Typhimurium are in eBG1 and most Enteritidis are in eBG4, but many eBGs contained more than one serovar. Furthermore, most serovars were polyphyletic and are distributed across multiple unrelated eBGs. Thus, serovar designations confounded genetically unrelated isolates and failed to recognize natural evolutionary groupings. An inability of serotyping to correctly group isolates was most apparent for Paratyphi B and its variant Java. Most Paratyphi B were included within a sub-cluster of STs belonging to eBG5, which also encompasses a separate sub-cluster of Java STs. However, diphasic Java variants were also found in two other eBGs and monophasic Java variants were in four other eBGs or STs, one of which is in subspecies salamae and a second of which includes isolates assigned to Enteritidis, Dublin and monophasic Paratyphi B. Similarly, Choleraesuis was found in eBG6 and is closely related to Paratyphi C, which is in eBG20. However, Choleraesuis var. Decatur consists of isolates from seven other, unrelated eBGs or STs. The serological assignment of these Decatur isolates to Choleraesuis likely reflects lateral gene transfer of flagellar genes between unrelated bacteria plus purifying selection. By confounding multiple evolutionary groups, serotyping can be misleading about the disease potential of S. enterica. Unlike serotyping, MLST recognizes evolutionary groupings and we recommend that Salmonella classification by serotyping should be replaced by MLST or its equivalents.