Biblio

An important source of cyber-attacks is malware, which proliferates in different forms such as botnets. The botnet malware typically looks for vulnerable devices across the Internet, rather than targeting specific individuals, companies or industries. It attempts to infect as many connected devices as possible, using their resources for automated tasks that may cause significant economic and social harm while being hidden to the user and device. Thus, it becomes very difficult to detect such activity. A considerable amount of research has been conducted to detect and prevent botnet infestation. In this paper, we attempt to create a foundation for an anomaly-based intrusion detection system using a statistical learning method to improve network security and reduce human involvement in botnet detection. We focus on identifying the best features to detect botnet activity within network traffic using a lightweight logistic regression model. The network traffic is processed by Bro, a popular network monitoring framework which provides aggregate statistics about the packets exchanged between a source and destination over a certain time interval. These statistics serve as features to a logistic regression model responsible for classifying malicious and benign traffic. Our model is easy to implement and simple to interpret. We characterized and modeled 8 different botnet families separately and as a mixed dataset. Finally, we measured the performance of our model on multiple parameters using F1 score, accuracy and Area Under Curve (AUC).

The IRC botnet is the earliest and most significant botnet group that has a significant impact. Its characteristic is to control multiple zombies hosts through the IRC protocol and constructing command control channels. Relevant research analyzes the large amount of network traffic generated by command interaction between the botnet client and the C&C server. Packet capture traffic monitoring on the network is currently a more effective detection method, but this information does not reflect the essential characteristics of the IRC botnet. The increase in the amount of erroneous judgments has often occurred. To identify whether the botnet control server is a homogenous botnet, dynamic network communication characteristic curves are extracted. For unequal time series, dynamic time warping distance clustering is used to identify the homologous botnets by category, and in order to improve detection. Speed, experiments will use SAX to reduce the dimension of the extracted curve, reducing the time cost without reducing the accuracy.

Internet of Things is attracting a lot of interest in the modern world and has become a part of daily life leading to a large scale of distribution of Low power and Lossy Networks (LLN). For such networks constrained by low power and storage, IETF has proposed RPL an open standard routing protocol. However RPL protocol is exposed to a number of attacks which may degrade the performance and resources of the network leading to incorrect output. In this paper, to address Wormhole and Grayhole attack we propose a light weight Trust based mechanism. The proposed method uses direct trust which is computed based on node properties and Indirect Trust which is based on opinion of the neighboring nodes. The proposed method is energy friendly and does not impose excessive overhead on network traffic.

Data privacy and security is a leading concern for providers and customers of cloud computing, where Virtual Machines (VMs) can co-reside within the same underlying physical machine. Side channel attacks within multi-tenant virtualized cloud environments are an established problem, where attackers are able to monitor and exfiltrate data from co-resident VMs. Virtualization services have attempted to mitigate such attacks by preventing VM-to-VM interference on shared hardware by providing logical resource isolation between co-located VMs via an internal virtual network. However, such approaches are also insecure, with attackers capable of performing network channel attacks which bypass mitigation strategies using vectors such as ARP Spoofing, TCP/IP steganography, and DNS poisoning. In this paper we identify a new vulnerability within the internal cloud virtual network, showing that through a combination of TAP impersonation and mirroring, a malicious VM can successfully redirect and monitor network traffic of VMs co-located within the same physical machine. We demonstrate the feasibility of this attack in a prominent cloud platform - OpenStack - under various security requirements and system conditions, and propose countermeasures for mitigation.

Traffic classification, i.e. associating network traffic to the application that generated it, is an important tool for several tasks, spanning on different fields (security, management, traffic engineering, R&D). This process is challenged by applications that preserve Internet users' privacy by encrypting the communication content, and even more by anonymity tools, additionally hiding the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide (repeatable) classification results with the aim of investigating to what degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of the other considered anonymity tools, using machine learning approaches based on the sole statistical features. To this end, four classifiers are trained and tested on the dataset: (i) Naïve Bayes, (ii) Bayesian Network, (iii) C4.5, and (iv) Random Forest. Results show that the three considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.99%), telling even the specific application generating the traffic (with an accuracy of 98.00%).

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.

Live migration is the process used in virtualization environment of datacenters in order to take the benefit of zero downtime during system maintenance. But during migrating live virtual machines along with system files and storage data, network traffic gets increases across network bandwidth and delays in migration time. There is need to reduce the migration time in order to maintain the system performance by analyzing and optimizing the storage overheads which mainly creates due to unnecessary duplicated data transferred during live migration. So there is need of such storage device which will keep the duplicated data residing in both the source as well as target physical host i.e. NAS. The proposed hash map based algorithm maps all I/O operations in order to track the duplicated data by assigning hash value to both NAS and RAM data. Only the unique data then will be sent data to the target host without affecting service level agreement (SLA), without affecting VM migration time, application downtime, SLA violations, VM pre-migration and downtime post migration overheads during pre and post migration of virtual machines.

Detection and prevention of data breaches in corporate networks is one of the most important security problems of today's world. The techniques and applications proposed for solution are not successful when attackers attempt to steal data using steganography. Steganography is the art of storing data in a file called cover, such as picture, sound and video. The concealed data cannot be directly recognized in the cover. Steganalysis is the process of revealing the presence of embedded messages in these files. There are many statistical and signature based steganalysis algorithms. In this work, the detection of steganographic images with steganalysis techniques is reviewed and a system has been developed which automatically detects steganographic images in network traffic by using open source tools.

In this paper, we address the problem of peer grouping employees in an organization for identifying security risks. Our motivation for studying peer grouping is its importance for a clear understanding of user and entity behavior analytics (UEBA) that is the primary tool for identifying insider threat through detecting anomalies in network traffic. We show that using Louvain method of community detection it is possible to automate peer group creation with feature-based weight assignments. Depending on the number of employees and their features we show that it is also possible to give each group a meaningful description. We present three new algorithms: one that allows an addition of new employees to already generated peer groups, another that allows for incorporating user feedback, and lastly one that provides the user with recommended nodes to be reassigned. We use Niara's data to validate our claims. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.

Conventional intrusion detection systems for smart grid communications rely heavily on static based attack detection techniques. In essence, signatures created from historical data are compared to incoming network traffic to identify abnormalities. In the case of attacks where no historical data exists, static based approaches become ineffective thus relinquishing system resilience and stability. Moving target defense (MTD) has shown to be effective in discouraging attackers by introducing system entropy to increase exploit costs. Increase in exploit cost leads to a decrease in profitability for an attacker. In this paper, a Moving Target Defense Intrusion Detection System (MTDIDS) is proposed for smart grid IPv6 based advanced metering infrastructure. The advantage of MTDIDS is the ability to detect anomalies across moving targets by means of planar keys thereupon increasing detection rate. Evaluation of MTDIDS was carried out in a smart grid advanced metering infrastructure simulated in MATLAB.

Bitcoin, one major virtual currency, attracts users' attention by its novel mode in recent years. With blockchain as its basic technique, Bitcoin possesses strong security features which anonymizes user's identity to protect their private information. However, some criminals utilize Bitcoin to do several illegal activities bringing in great security threat to the society. Therefore, it is necessary to get knowledge of the current trend of Bitcoin and make effort to de-anonymize. In this paper, we put forward and realize a system to analyze Bitcoin from two aspects: blockchain data and network traffic data. We resolve the blockchain data to analyze Bitcoin from the point of Bitcoin address while simulate Bitcoin P2P protocol to evaluate Bitcoin from the point of IP address. At last, with our system, we finish analyzing its current trends and tracing its transactions by putting some statistics on Bitcoin transactions and addresses, tracing the transaction flow and de-anonymizing some Bitcoin addresses to IPs.

Cloud computing is a solution to reduce the cost of IT by providing elastic access to shared resources. It also provides solutions for on-demand computing power and storage for devices at the edge networks with limited resources. However, increasing the number of connected devices caused by IoT architecture leads to higher network traffic and delay for cloud computing. The centralised architecture of cloud computing also makes the edge networks more susceptible to challenges in the core network. Fog computing is a solution to decrease the network traffic, delay, and increase network resilience. In this paper, we study how fog computing may improve network resilience. We also conduct a simulation to study the effect of fog computing on network traffic and delay. We conclude that using fog computing prepares the network for better response time in case of interactive requests and makes the edge networks more resilient to challenges in the core network.

The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.

Denial of service (DOS) attacks are a serious threat to network security. These attacks are often sourced from virtual machines in the cloud, rather than from the attacker's own machine, to achieve anonymity and higher network bandwidth. Past research focused on analyzing traffic on the destination (victim's) side with predefined thresholds. These approaches have significant disadvantages. They are only passive defenses after the attack, they cannot use the outbound statistical features of attacks, and it is hard to trace back to the attacker with these approaches. In this paper, we propose a DOS attack detection system on the source side in the cloud, based on machine learning techniques. This system leverages statistical information from both the cloud server's hypervisor and the virtual machines, to prevent network packages from being sent out to the outside network. We evaluate nine machine learning algorithms and carefully compare their performance. Our experimental results show that more than 99.7% of four kinds of DOS attacks are successfully detected. Our approach does not degrade performance and can be easily extended to broader DOS attacks.

This paper focuses on one type of Covert Storage Channel (CSC) that uses the 6-bit TCP flag header in TCP/IP network packets to transmit secret messages between accomplices. We use relative entropy to characterize the irregularity of network flows in comparison to normal traffic. A normal profile is created by the frequency distribution of TCP flags in regular traffic packets. In detection, the TCP flag frequency distribution of network traffic is computed for each unique IP pair. In order to evaluate the accuracy and efficiency of the proposed method, this study uses real regular traffic data sets as well as CSC messages using coding schemes under assumptions of both clear text, composed by a list of keywords common in Unix systems, and encrypted text. Moreover, smart accomplices may use only those TCP flags that are ever appearing in normal traffic. Then, in detection, the relative entropy can reveal the dissimilarity of a different frequency distribution from this normal profile. We have also used different data processing methods in detection: one method summarizes all the packets for a pair of IP addresses into one flow and the other uses a sliding moving window over such a flow to generate multiple frames of packets. The experimentation results, displayed by Receiver Operating Characteristic (ROC) curves, have shown that the method is promising to differentiate normal and CSC traffic packet streams. Furthermore the delay of raising an alert is analyzed for CSC messages to show its efficiency.

Industrial Control System (ICS) consists of large number of electronic devices connected to field devices to execute the physical processes. Communication network of ICS supports wide range of packet based applications. A growing issue with network security and its impact on ICS have highlighted some fundamental risks to critical infrastructure. To address network security issues for ICS a clear understanding of security specific defensive countermeasures is required. Reconnaissance of ICS network by deep packet inspection (DPI) consists analysis of the contents of the captured packets in order to get accurate measures of process that uses specific countermeasure to create an aggregated posture. In this paper we focus on novel approach by presenting a technique with captured network traffic. This technique is capable to identify the protocols and extract different features for classification of traffic based on network protocol, header information and payload to understand the whole architecture of complex system. Here we have segregated possible types of attacks on ICS.

Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. These systems need to be evaluated against high quality datasets for correctly assessing their usefulness and comparing their performance. We present an Intrusion Detection Dataset Toolkit (ID2T) for the creation of labeled datasets containing user defined synthetic attacks. The architecture of the toolkit is provided for examination and the example of an injected attack, in real network traffic, is visualized and analyzed. We further discuss the ability of the toolkit of creating realistic synthetic attacks of high quality and low bias.

Sharing cyber security data across organizational boundaries brings both privacy risks in the exposure of personal information and data, and organizational risk in disclosing internal information. These risks occur as information leaks in network traffic or logs, and also in queries made across organizations. They are also complicated by the trade-offs in privacy preservation and utility present in anonymization to manage disclosure. In this paper, we define three principles that guide sharing security information across organizations: Least Disclosure, Qualitative Evaluation, and Forward Progress. We then discuss engineering approaches that apply these principles to a distributed security system. Application of these principles can reduce the risk of data exposure and help manage trust requirements for data sharing, helping to meet our goal of balancing privacy, organizational risk, and the ability to better respond to security with shared information.

Denial-of-Service (DoS) attacks pose a threat to any service provider on the internet. While traditional DoS flooding attacks require the attacker to control at least as much resources as the service provider in order to be effective, so-called low-rate DoS attacks can exploit weaknesses in careless design to effectively deny a service using minimal amounts of network traffic. This paper investigates one such weakness found within version 2.2 of the popular Apache HTTP Server software. The weakness concerns how the server handles the persistent connection feature in HTTP 1.1. An attack simulator exploiting this weakness has been developed and shown to be effective. The attack was then studied with spectral analysis for the purpose of examining how well the attack could be detected. Similar to other papers on spectral analysis of low-rate DoS attacks, the results show that disproportionate amounts of energy in the lower frequencies can be detected when the attack is present. However, by randomizing the attack pattern, an attacker can efficiently reduce this disproportion to a degree where it might be impossible to correctly identify an attack in a real world scenario.

The integration of social networking concepts into the Internet of things has led to the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability in information/service discovery. Within this scenario, we focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. In the subjective model each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the friends in common with the potential service providers. In the objective model, the information about each node is distributed and stored making use of a distributed hash table structure so that any node can make use of the same information. Simulations show how the proposed models can effectively isolate almost any malicious nodes in the network at the expenses of an increase in the network traffic for feedback exchange.

The dazzling emergence of cyber-threats exert today's cyberspace, which needs practical and efficient capabilities for malware traffic detection. In this paper, we propose an extension to an initial research effort, namely, towards fingerprinting malicious traffic by putting an emphasis on the attribution of maliciousness to malware families. The proposed technique in the previous work establishes a synergy between automatic dynamic analysis of malware and machine learning to fingerprint badness in network traffic. Machine learning algorithms are used with features that exploit only high-level properties of traffic packets (e.g. packet headers). Besides, the detection of malicious packets, we want to enhance fingerprinting capability with the identification of malware families responsible in the generation of malicious packets. The identification of the underlying malware family is derived from a sequence of application protocols, which is used as a signature to the family in question. Furthermore, our results show that our technique achieves promising malware family identification rate with low false positives.

The dazzling emergence of cyber-threats exert today's cyberspace, which needs practical and efficient capabilities for malware traffic detection. In this paper, we propose an extension to an initial research effort, namely, towards fingerprinting malicious traffic by putting an emphasis on the attribution of maliciousness to malware families. The proposed technique in the previous work establishes a synergy between automatic dynamic analysis of malware and machine learning to fingerprint badness in network traffic. Machine learning algorithms are used with features that exploit only high-level properties of traffic packets (e.g. packet headers). Besides, the detection of malicious packets, we want to enhance fingerprinting capability with the identification of malware families responsible in the generation of malicious packets. The identification of the underlying malware family is derived from a sequence of application protocols, which is used as a signature to the family in question. Furthermore, our results show that our technique achieves promising malware family identification rate with low false positives.

In the cyber crime huge log data, transactional data occurs which tends to plenty of data for storage and analyze them. It is difficult for forensic investigators to play plenty of time to find out clue and analyze those data. In network forensic analysis involves network traces and detection of attacks. The trace involves an Intrusion Detection System and firewall logs, logs generated by network services and applications, packet captures by sniffers. In network lots of data is generated in every event of action, so it is difficult for forensic investigators to find out clue and analyzing those data. In network forensics is deals with analysis, monitoring, capturing, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper focuses on data collection from the cyber system and web browser. The FTK 4.0 is discussing for memory forensic analysis and remote system forensic which is to be used as evidence for aiding investigation.
 

Rapid advances in wireless ad hoc networks lead to increase their applications in real life. Since wireless ad hoc networks have no centralized infrastructure and management, they are vulnerable to several security threats. Malicious packet dropping is a serious attack against these networks. In this attack, an adversary node tries to drop all or partial received packets instead of forwarding them to the next hop through the path. A dangerous type of this attack is called black hole. In this attack, after absorbing network traffic by the malicious node, it drops all received packets to form a denial of service (DOS) attack. In this paper, a dynamic trust model to defend network against this attack is proposed. In this approach, a node trusts all immediate neighbors initially. Getting feedback from neighbors' behaviors, a node updates the corresponding trust value. The simulation results by NS-2 show that the attack is detected successfully with low false positive probability.

Mobile ad hoc network (MANET) is a self-created and self organized network of wireless mobile nodes. Due to special characteristics of these networks, security issue is a difficult task to achieve. Hence, applying current intrusion detection techniques developed for fixed networks is not sufficient for MANETs. In this paper, we proposed an approach based on genetic algorithm (GA) and artificial immune system (AIS), called GAAIS, for dynamic intrusion detection in AODV-based MANETs. GAAIS is able to adapting itself to network topology changes using two updating methods: partial and total. Each normal feature vector extracted from network traffic is represented by a hypersphere with fix radius. A set of spherical detector is generated using NicheMGA algorithm for covering the nonself space. Spherical detectors are used for detecting anomaly in network traffic. The performance of GAAIS is evaluated for detecting several types of routing attacks simulated using the NS2 simulator, such as Flooding, Blackhole, Neighbor, Rushing, and Wormhole. Experimental results show that GAAIS is more efficient in comparison with similar approaches.