Biblio

The most common defenses against malware threats involves the use of signatures derived from instances of known malware. However, the constant evolution of the malware threat landscape necessitates defense against unknown malware, making a signature catalog of known threats insufficient to prevent zero-day vulnerabilities from being exploited. Recent research has applied machine learning approaches to identify malware through artifacts of malicious activity as observed through dynamic behavioral analysis. We have seen that these approaches mimic common malware defenses by simply offering a method of detecting known malware. We contribute a new method of identifying software as malicious or benign through analysis of the frequency of Windows API system function calls. We show that this is a powerful technique for malware detection because it generates learning models which understand the difference between malicious and benign software, rather than producing a malware signature classifier. We contribute a method of systematically comparing machine learning models against different datasets to determine their efficacy in accurately distinguishing the difference between malicious and benign software.

Hardware security is a critical concern in design and fabrication of integrated circuits (ICs). Contemporary hardware threats comprise tens of advance invasive and non-invasive attacks for compromising security of modern ICs. Numerous attack-specific countermeasures against the individual threats have been proposed, trading power, area, speed, and design complexity of a system for security. These typical overheads combined with strict performance requirements in advanced technology nodes and high complexity of modern ICs often make the codesign of multiple countermeasures impractical. In this paper, on-chip distribution networks are exploited for detecting those hardware security threats that require non-invasive, yet physical interaction with an operating device-under-attack (e.g., measuring equipment for collecting sensitive information in side-channel attacks). With the proposed approach, the effect of the malicious physical interference with the device-under-attack is captured in the form of on-chip voltage variations and utilized for detecting malicious activity in the compromised device. A machine learning (ML) security IC is trained to predict system security based on sensed variations of signals within on-chip distribution networks. The trained ML ICs are distributed on-chip, yielding a robust and high-confidence security network on-chip. To halt an active attack, a variety of desired counteractions can be executed in a cost-effective manner upon the attack detection. The applicability and effectiveness of these security networks is demonstrated in this paper with respect to power, timing, and electromagnetic analysis attacks.

Deep Packet Inspection (DPI) is instrumental in investigating the presence of malicious activity in network traffic and most existing DPI tools work on unencrypted payloads. As the internet is moving towards fully encrypted data-transfer, there is a critical requirement for privacy-aware techniques to efficiently decrypt network payloads. Until recently, passive proxying using certain aspects of TLS 1.2 were used to perform decryption and further DPI analysis. With the introduction of TLS 1.3 standard that only supports protocols with Perfect Forward Secrecy (PFS), many such techniques will become ineffective. Several security solutions will be forced to adopt active proxying that will become a big-data problem considering the velocity and veracity of network traffic involved. We have developed an ABAC (Attribute Based Access Control) framework that efficiently supports existing DPI tools while respecting user's privacy requirements and organizational policies. It gives the user the ability to accept or decline access decision based on his privileges. Our solution evaluates various observed and derived attributes of network connections against user access privileges using policies described with semantic technologies. In this paper, we describe our framework and demonstrate the efficacy of our technique with the help of use-case scenarios to identify network connections that are candidates for Deep Packet Inspection. Since our technique makes selective identification of connections based on policies, both processing and memory load at the gateway will be reduced significantly.

A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements.

Modern Energy Management Systems (EMS) are becoming increasingly complex in order to address the urgent issue of global energy consumption. These systems retrieve vital information from various Internet-connected resources in a smart grid to function effectively. However, relying on such resources results in them being susceptible to cyber attacks. Malicious actors can exploit the interconnections between the resources to perform nefarious tasks such as modifying critical firmware, sending bogus sensor data, or stealing sensitive information. To address this issue, we propose a novel framework that integrates PowerWatch, a solution that detects compromised devices in the smart grid with Cyber-secure Power Router (CSPR), a smart energy management system. The goal is to ascertain whether or not such a device has operated maliciously. To achieve this, PowerWatch utilizes a machine learning model that analyzes information from system and library call lists extracted from CSPR in order to detect malicious activity in the EMS. To test the efficacy of our framework, a number of unique attack scenarios were performed on a realistic testbed that comprises functional versions of CSPR and PowerWatch to monitor the electrical environment for suspicious activity. Our performance evaluation investigates the effectiveness of this first-of-its-kind merger and provides insight into the feasibility of developing future cybersecure EMS. The results of our experimental procedures yielded 100% accuracy for each of the attack scenarios. Finally, our implementation demonstrates that the integration of PowerWatch and CSPR is effective and yields minimal overhead to the EMS.

In order to examine malicious activity that occurs in a network or a system, intrusion detection system is used. Intrusion Detection is software or a device that scans a system or a network for a distrustful activity. Due to the growing connectivity between computers, intrusion detection becomes vital to perform network security. Various machine learning techniques and statistical methodologies have been used to build different types of Intrusion Detection Systems to protect the networks. Performance of an Intrusion Detection is mainly depends on accuracy. Accuracy for Intrusion detection must be enhanced to reduce false alarms and to increase the detection rate. In order to improve the performance, different techniques have been used in recent works. Analyzing huge network traffic data is the main work of intrusion detection system. A well-organized classification methodology is required to overcome this issue. This issue is taken in proposed approach. Machine learning techniques like Support Vector Machine (SVM) and Naïve Bayes are applied. These techniques are well-known to solve the classification problems. For evaluation of intrusion detection system, NSL- KDD knowledge discovery Dataset is taken. The outcomes show that SVM works better than Naïve Bayes. To perform comparative analysis, effective classification methods like Support Vector Machine and Naive Bayes are taken, their accuracy and misclassification rate get calculated.

In sensor-enabled Internet of Things (IoT), nodes are deployed in an open and remote environment, therefore, are vulnerable to a variety of attacks. Recently, trust-based schemes have played a pivotal role in addressing nodes' misbehavior attacks in IoT. However, the existing trust-based schemes apply network wide dissemination of the control packets that consume excessive energy in the quest of trust evaluation, which ultimately weakens the network lifetime. In this context, this paper presents an energy efficient trust evaluation (EETE) scheme that makes use of hierarchical trust evaluation model to alleviate the malicious effects of illegitimate sensor nodes and restricts network wide dissemination of trust requests to reduce the energy consumption in clustered-sensor enabled IoT. The proposed EETE scheme incorporates three dilemma game models to reduce additional needless transmissions while balancing the trust throughout the network. Specially: 1) a cluster formation game that promotes the nodes to be cluster head (CH) or cluster member to avoid the extraneous cluster; 2) an optimal cluster formation dilemma game to affirm the minimum number of trust recommendations for maintaining the balance of the trust in a cluster; and 3) an activity-based trust dilemma game to compute the Nash equilibrium that represents the best strategy for a CH to launch its anomaly detection technique which helps in mitigation of malicious activity. Simulation results show that the proposed EETE scheme outperforms the current trust evaluation schemes in terms of detection rate, energy efficiency and trust evaluation time for clustered-sensor enabled IoT.

Cyber defense can no longer be limited to intrusion detection methods. These systems require malicious activity to enter an internal network before an attack can be detected. Having advanced, predictive knowledge of future attacks allow a potential victim to heighten security and possibly prevent any malicious traffic from breaching the network. This paper investigates the use of Auto-Regressive Integrated Moving Average (ARIMA) models and Bayesian Networks (BN) to predict future cyber attack occurrences and intensities against two target entities. In addition to incident count forecasting, categorical and binary occurrence metrics are proposed to better represent volume forecasts to a victim. Different measurement periods are used in time series construction to better model the temporal patterns unique to each attack type and target configuration, seeing over 86% improvement over baseline forecasts. Using ground truth aggregated over different measurement periods as signals, a BN is trained and tested for each attack type and the obtained results provided further evidence to support the findings from ARIMA. This work highlights the complexity of cyber attack occurrences; each subset has unique characteristics and is influenced by a number of potential external factors.

The serializability of transactions is the most important property that ensure correct processing to transactions. In case of concurrent access to the same data by several transactions, or in case of dependency relationships between running sub transactions. But some transactions has been marked as malicious and they compromise the serialization of running system. For that purpose, we propose an intrusion tolerant scheme to ensure the continuity of the running transactions. A transaction dependency graph is also used by the CDC to make decisions concerning the set of data and transactions that are threatened by a malicious activity. We will give explanations about how to use the proposed scheme to illustrate its behavior and efficiency against a compromised transaction-based in a cloud of databases environment. Several issues should be considered when dealing with the processing of a set of interleaved transactions in a transaction based environment. In most cases, these issues are due to the concurrent access to the same data by several transactions or the dependency relationship between running transactions. The serializability may be affected if a transaction that belongs to the processing node is compromised.

Most network traffic analysis applications are designed to discover malicious activity by only relying on high-level flow-based message properties. However, to detect security breaches that are specifically designed to target one network (e.g., Advanced Persistent Threats), deep packet inspection and anomaly detection are indispensible. In this paper, we focus on how we can support experts in discovering whether anomalies at message level imply a security risk at network level. In SNAPS (Semantic Network traffic Analysis through Projection and Selection), we provide a bottom-up pixel-oriented approach for network traffic analysis where the expert starts with low-level anomalies and iteratively gains insight in higher level events through the creation of multiple selections of interest in parallel. The tight integration between visualization and machine learning enables the expert to iteratively refine anomaly scores, making the approach suitable for both post-traffic analysis and online monitoring tasks. To illustrate the effectiveness of this approach, we present example explorations on two real-world data sets for the detection and understanding of potential Advanced Persistent Threats in progress.

Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.

Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.